ISG Software Research Analyst Perspectives

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In May 2010 SAP announced an <a href="http://www.sap.com/press.epx?pressid=13202">agreement to acquire Sybase</a>, primarily for its mobility technology and had already been advancing its efforts with <a href="//davidmenninger.ventanaresearch.com/2011/03/02/sap-business-intelligence-4-0/">SAP HANA and BI</a>. In July 2010 EMC agreed to <a href="http://www.emc.com/about/news/press/2010/20100706-01.htm">acquire data warehouse appliance vendor Greenplum</a>. In <a href="//marksmith.ventanaresearch.com/2010/11/28/ibm-makes-major-buy-in-analytics-market-with-netezza/">September 2010 IBM countered by acquiring Netezza</a>, a competitor of Greenplum. In February 2011 HP announced <a href="//marksmith.ventanaresearch.com/2010/12/06/hp-gives-up-on-business-intelligence-and-analytics-markets/">after giving up on its original focus...</a> </div> <a href="https://blog.isg-research.net/2011/03/05/aster-data-is-now-part-of-the-new-teradata"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing</a>, <a href="https://blog.isg-research.net/topic/microsoft">Microsoft</a>, <a href="https://blog.isg-research.net/topic/rdbms">RDBMS</a>, <a href="https://blog.isg-research.net/topic/sas">SAS</a>, <a href="https://blog.isg-research.net/topic/teradata">Teradata</a>, <a href="https://blog.isg-research.net/topic/it-performance">IT Performance</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/hp">HP</a>, <a href="https://blog.isg-research.net/topic/ibm">IBM</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a>, <a href="https://blog.isg-research.net/topic/oracle">Oracle</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/2011/03/04/secrets-revealed-in-columnar-database-technology">Secrets Revealed in Columnar Database Technology</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 03 March 2011 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg" width="200" !important: srcset="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=100&name=shutterstock_314838419-1.jpg 100w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg 200w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=300&name=shutterstock_314838419-1.jpg 300w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=400&name=shutterstock_314838419-1.jpg 400w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=500&name=shutterstock_314838419-1.jpg 500w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=600&name=shutterstock_314838419-1.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> This is the second in a series of posts on the architectures of analytic databases. The first post addressed <a href="//davidmenninger.ventanaresearch.com/2011/01/19/secrets-revealed-in-massively-parallel-processing-and-database-technology/">massively parallel processing (MPP) and database technology</a>. In this post, we’ll look at columnar database technology. Given the <a href="http://www.hp.com/hpinfo/newsroom/press/2011/110214xb.html">recent announcement</a> of HP’s plan to acquire Vertica, a columnar database vendor, there is likely to be even more interest in columnar database technology, how it operates and what benefits it offers. </div> <a href="https://blog.isg-research.net/2011/03/04/secrets-revealed-in-columnar-database-technology"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing</a>, <a href="https://blog.isg-research.net/topic/rdbms">RDBMS</a>, <a href="https://blog.isg-research.net/topic/it-performance">IT Performance</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/2011/03/03/living-in-the-era-of-hadoop-and-large-scale-data">Living in the Era of Hadoop and Large-Scale Data</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 02 March 2011 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg" width="200" !important: srcset="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=100&name=shutterstock_314838419-1.jpg 100w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg 200w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=300&name=shutterstock_314838419-1.jpg 300w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=400&name=shutterstock_314838419-1.jpg 400w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=500&name=shutterstock_314838419-1.jpg 500w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=600&name=shutterstock_314838419-1.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> It’s clear that now we are living in the era of big data. The stores of data on which modern businesses rely are already vast and increasing at an unprecedented pace. Organizations are capturing data at deeper levels of detail and keeping more history than they ever have before. Managing all of the data is thus emerging as one of the key challenges of the new decade. </div> <a href="https://blog.isg-research.net/2011/03/03/living-in-the-era-of-hadoop-and-large-scale-data"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing</a>, <a href="https://blog.isg-research.net/topic/rdbms">RDBMS</a>, <a href="https://blog.isg-research.net/topic/it-performance">IT Performance</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a>, <a href="https://blog.isg-research.net/topic/stratahadoop">Strata+Hadoop</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/2011/02/14/kognitio-pablo-brings-mdx-to-data-and-analytics">Kognitio Pablo Brings MDX to Data and Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 14 February 2011 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg" width="200" !important: srcset="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=100&name=shutterstock_314838419-1.jpg 100w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg 200w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=300&name=shutterstock_314838419-1.jpg 300w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=400&name=shutterstock_314838419-1.jpg 400w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=500&name=shutterstock_314838419-1.jpg 500w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=600&name=shutterstock_314838419-1.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Kognitio <a href="http://www.kognitio.com/next-big-thing-in-data-analytics-no-more-physical-cubes" target="_blank">announced</a> the addition of MultiDimensional eXpressions (MDX) capabilities for its WX2 product line. John Thompson, CEO of U.S. operations, and Sean Jackson, VP of marketing, shared some of the details with me recently. I find the marriage of MDX and large-scale data both technically challenging and potentially valuable to the market. </div> <a href="https://blog.isg-research.net/2011/02/14/kognitio-pablo-brings-mdx-to-data-and-analytics"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing</a>, <a href="https://blog.isg-research.net/topic/mdx">MDX</a>, <a href="https://blog.isg-research.net/topic/rdbms">RDBMS</a>, <a href="https://blog.isg-research.net/topic/it-performance">IT Performance</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a>, <a href="https://blog.isg-research.net/topic/kognitio">Kognitio</a>, <a href="https://blog.isg-research.net/topic/mpp">MPP</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/2011/01/19/secrets-revealed-in-massively-parallel-processing-and-database-technology">Secrets Revealed in Massively Parallel Processing and Database Technology</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 19 January 2011 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg" width="200" !important: srcset="//blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=100&name=shutterstock_314838419-1.jpg 100w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=200&name=shutterstock_314838419-1.jpg 200w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=300&name=shutterstock_314838419-1.jpg 300w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=400&name=shutterstock_314838419-1.jpg 400w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=500&name=shutterstock_314838419-1.jpg 500w, //blog.isg-research.net/hs-fs/hubfs/Ventana_Research_Sept_2016/images/shutterstock_314838419-1.jpg?width=600&name=shutterstock_314838419-1.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> This is the first in a series of posts on the architectures of analytic databases. This is relational database technology that has been “supercharged” in some way to handle large amounts of data such as typical data warehouse workloads. The series will examine massively parallel processing (MPP), columnar databases, appliances and in-database analytics. Our purpose is to help those evaluating analytic database technologies understand some of the alternative approaches so they can differentiate... </div> <a href="https://blog.isg-research.net/2011/01/19/secrets-revealed-in-massively-parallel-processing-and-database-technology"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing</a>, <a href="https://blog.isg-research.net/topic/rdbms">RDBMS</a>, <a href="https://blog.isg-research.net/topic/it-performance">IT Performance</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/data-management">Data Management</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); }); // Active tag $('.bttn').click(function(){ $('.bttn').removeClass('active'); $(this).addClass('active'); }) </script> </div> <div class="blog-numeric-pagination span12 ">   </div> </div> <div class="blog-sidebar">   <style> .blog-sidebar h3 { font-size: 18px; margin-bottom: 30px; position: relative; color: var(--light_blue); padding-top:40px; } .btn-secondary, .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } </style> <a href="https://cta-redirect.hubspot.com/cta/redirect/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be"><img class="hs-cta-img" id="hs-cta-img-960cd0b7-7494-40d3-ac1c-5825d5a075be" style="border-width:0px;" src="https://no-cache.hubspot.com/cta/default/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be.png" alt="JOIN OUR COMMUNITY"></a><script charset="utf-8" src="https://js.hscta.net/cta/current.js"></script><script type="text/javascript"> hbspt.cta.load(2035844, '960cd0b7-7494-40d3-ac1c-5825d5a075be', {"useNewLoader":"true","region":"na1"}); </script> <h3>Our Analyst Perspective Policy</h3> <ul> <li style="display: none; list-style: none;"> Ventana Research’s Analyst Perspectives are fact-based analysis and guidance on business, <g class="gr_ gr_12 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="12" data-gr-id="12">industry</g> and technology vendor trends. 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If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@ventanaresearch.com" style="color: #284a7b;">ChiefResearchOfficer@ventanaresearch.com</a> </li> </ul> <a class="btn-secondary" href="https://www.ventanaresearch.com/analyst-perspective-policy">View Policy</a>  <div id="hs_cos_wrapper_module_168621267066296" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> </style> <h3 style="color: #255a89;">Our Analysts</h3>  <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=300&name=Orr.Jeff.Circle.png 300w" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Jeff Orr</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=300&name=Smith.Mark%20Pic.Circle.png 300w"> <h5 class="analyst_name">Mark Smith</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=300&name=Hurrell.Stephen%20Pic.Circle.png 300w"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> </div>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=100&name=ISG_Bug_Reverse_Transparent.png" alt="ISG Software Research" width="100" data-constrained="true" caption="false" 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href="https://blog.isg-research.net/topic/cio">CIO (112)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cio-callenges">cio callenges (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cirro">Cirro (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cisco">Cisco (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ciso">CISO (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clarabridge">Clarabridge (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clarity-systems">Clarity Systems (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/clickfox">Clickfox (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/close">close (43)</a> </li> <li style="display:none;"> <a 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href="https://blog.isg-research.net/topic/customer-journey-maps">Customer Journey Maps (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-life-cycle">customer life cycle (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance (109)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-relationship-management">Customer Relationship Management (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-service">Customer Service (289)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/customer-service-and-support">customer service and support (20)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cxm">CXM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cybersecurity">Cybersecurity (5)</a> </li> <li 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Discovery (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-fabric">data fabric (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-governance">Data Governance (155)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-integration">Data Integration (82)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-integrity">data integrity (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-intelligence">Data Intelligence (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-lake">Data Lake (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-lakes">data lakes (43)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-management">Data Management (88)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-mart">data mart (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-mining">Data Mining (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-observability">Data Observability (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-operations">data operations (101)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-ops">Data Ops (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-orchestration">Data Orchestration (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-pipelines">Data Pipelines (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-platforms">data platforms (45)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-preparation">Data 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href="https://blog.isg-research.net/topic/data-visualization">Data Visualization (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-viz">data viz (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/data-warehousing">Data Warehousing (19)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database">Database (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database-as-a-service">Database as a Service (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/database-com">database.com (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/datameer">Datameer (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dataops">dataops (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dataprep">DataPrep (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/datawatch">Datawatch (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dayforce">dayforce (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ddi">DDI (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/debt">debt (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision">decision (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-hub">Decision Hub (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-intelligence">Decision Intelligence (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decision-management">Decision Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/decusoft">Decusoft (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/deep-learning">Deep Learning (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dell">Dell (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dell-boomi">Dell Boomi (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-chain">Demand Chain (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-generation">Demand Generation (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-management">demand management (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/demand-planning">Demand Planning (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/design">design (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/designed-data">designed data 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href="https://blog.isg-research.net/topic/digital-marketing">Digital Marketing (28)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-operations">Digital Operations (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-publishing">Digital Publishing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-security">Digital Security (23)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology (292)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-transformation">Digital transformation (20)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/direct-data-mapping">Direct Data Mapping (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/disaster-recovery">Disaster Recovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery">Discovery (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery-analytics">discovery analytics (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/discovery-tools">discovery tools (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/distribution">distribution (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dns">DNS (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/document-management">Document Management (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dodd-frank">Dodd-Frank (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/domestic-payroll">Domestic Payroll (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/domo">Domo 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href="https://blog.isg-research.net/topic/dynamics-nav-dynamics-sl">Dynamics NAV Dynamics SL (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/e-discovery">e-discovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/echopass">Echopass (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eclipse">Eclipse (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/edi">EDI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ediscovery">eDiscovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/editorial">Editorial (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/effectiveness">effectiveness (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eg-solutions">eg Solutions (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/elasticube">elasticube (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eloqua">Eloqua (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/embedded-analytics">embedded analytics (95)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/emc">EMC (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-engagement">Employee Engagement (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-experience">employee experience (47)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-onboarding">Employee Onboarding (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-productivity">Employee Productivity (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/empower">Empower (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/end-to-end">end-to-end (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/endeca">endeca (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enghouse-interactive">Enghouse interactive (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enkata">Enkata (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-applications">Enterprise Applications (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-data-strategy">Enterprise Data Strategy (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-messaging-internet-of-things-iot-middle">Enterprise messaging, Internet of Things, IoT, mid (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-profitability-management">enterprise profitability management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-resource-planning">Enterprise Resource Planning (72)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-risk-management">enterprise risk management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-software">Enterprise Software (27)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-spreadsheet">enterprise spreadsheet (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-tax">Enterprise Tax (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterworks">Enterworks (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environment-management">Environment Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environmental">Environmental (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/envision">Envision (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epicor">Epicor (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epiphany">Epiphany (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equifax">Equifax (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equity-research">equity research (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erm">ERM (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp">ERP (83)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp-and-continuous-accounting">ERP and Continuous Accounting (150)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/error">error (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esg">ESG (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esri">ESRI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/essbase">Essbase (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ethority">eThority (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-data">Event Data (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-discovery">Event discovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream">Event Stream (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream-processing">Event Stream Processing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-streams">Event Streams (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exacttarget">ExactTarget (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exadata">exadata (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exalytics">Exalytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excel">Excel (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excentive">Excentive (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/executive">executive (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense">expense (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense-management">expense management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/experience-management">Experience Management (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expert-systems">Expert Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploration">Exploration (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploratory-analytics">exploratory analytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/explorium">Explorium (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-close">extended close (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-reality">extended reality (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/external-data-platform">External Data Platform (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exterro">Exterro (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/f-type">F-Type (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fasb">FASB (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fast-close">Fast close (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/feedback">Feedback (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fei">FEI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ferf-ceo">FERF CEO (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/field-service">Field Service (29)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance">finance (53)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-analytics">Finance Analytics (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-cloud">finance cloud (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-financial-applications-financial-close">Finance Financial Applications Financial Close (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-performance-management">Finance Performance Management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-transformation">finance transformation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial">financial (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-analytics">financial analytics (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-applications">Financial Applications (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-close">Financial Close (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-governance">Financial Governance (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-management">Financial Management (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-model">financial model (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance">Financial Performance (561)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-2">financial performance (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-management">Financial Performance Management (237)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-management-fpm">Financial Performance Management (FPM) (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-planning">financial planning (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-regulation">financial regulation (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-reporting">financial reporting (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-risk-management">financial risk management (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-services">Financial Services (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-software">financial software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-standards">financial standards (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-statement">financial statement (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-systems">financial systems (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce">FinancialForce (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce-com">FinancialForce.com (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financials">financials (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/five9">Five9 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/flow">flow (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fms">FMS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/for-example-genesys">for example Genesys (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecast">Forecast (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecasting">forecasting (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpa">FP&A (22)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpm">FPM (83)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fraud">Fraud (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fuel50">Fuel50 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fusion-applications">Fusion 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(1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gooddata">Gooddata (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/google">Google (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance">Governance (49)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk">Governance & Risk (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk-and-compliance">Governance Risk and Compliance (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC) (143)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/grc">GRC (55)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/grc-governance-risk-compliance-risk-management-wat">GRC, governance, risk, 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href="https://blog.isg-research.net/topic/iac">IaC (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/iasb">IASB (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm">IBM (73)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-business-analytics">IBM Business Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-db2">IBM DB2 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-predictive-customer-intelligence">IBM Predictive Customer Intelligence (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-spss">IBM SPSS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibm-watson">IBM Watson (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ibp">IBP (4)</a> </li> <li style="display:none;"> <a 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href="https://blog.isg-research.net/topic/influencers">Influencers (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infodiscovery">InfoDiscovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infomgt-location">InfoMgt; Location (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infopia">InfoPia (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infor">Infor (31)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infor-birst">Infor Birst (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica">Informatica (22)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica-cloud">Informatica Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/informatica-hparser">Informatica HParser (1)</a> </li> <li 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href="https://blog.isg-research.net/topic/information-builders-summit-2019">Information Builders Summit 2019 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-discovery">Information Discovery (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-life-cycle-management">Information Life Cycle Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-management">Information Management (381)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-management-im">Information Management (IM) (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-optimization">Information Optimization (52)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-platform">Information Platform (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/information-technology">Information Technology (47)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/infosphere">InfoSphere (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/initiatives-management">Initiatives Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/initiatives-planning">Initiatives Planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/inkling">inkling (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/innovation-awards">Innovation Awards (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/insights">Insights (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intacct">Intacct (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/integrated-business-planning">Integrated Business Planning (38)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-automation">Intelligent Automation (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-cx">Intelligent CX (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-marketing">intelligent marketing (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-memory">Intelligent Memory (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-sales">intelligent sales (22)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/intelligent-self-service">Intelligent Self-Service (22)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/interactions">Interactions (1)</a> </li> 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href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance (504)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/office-of-revenue">Office of Revenue (39)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/office-of-sales">Office of Sales (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/olap">OLAP (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/omnichannel">Omnichannel (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/omnichannel-commerce-digital-kdigital">Omnichannel, Commerce, Digital kDigital (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/on24">On24 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/onboarding">Onboarding (1)</a> </li> <li style="display:none;"> <a 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href="https://blog.isg-research.net/topic/openworld">OpenWorld (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/opera-solutions">Opera Solutions (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-data-platforms">operational data platforms (39)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-intelligence">Operational Intelligence (180)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-management">Operational Management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-performance">Operational Performance (696)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-performance-management-opm">Operational Performance Management (OPM) (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operational-planning">Operational Planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operations">Operations (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/operations-supply-chain">Operations & Supply Chain (90)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle">Oracle (78)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle-cloud">Oracle Cloud (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oracle-fusion-hcm">Oracle Fusion HCM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/orcaeyes">OrcaEyes (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/orchestration">Orchestration (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/order-management">Order Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/order-to-cash">Order-to-Cash (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/overall-operational-leadership">Overall Operational Leadership (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/oversight-systems">Oversight Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/overtone">Overtone (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paas">PaaS (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/panopticon">Panopticon (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paraccel">Paraccel (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pardot">Pardot (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/partner-experience">Partner Experience (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/partner-management">partner management (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/patersons">Patersons (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/paxata">Paxata (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pay-equity">Pay Equity (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pay-for-performance">Pay for Performance (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/payroll-management">Payroll Management (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/payroll-optimization">Payroll Optimization (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pdi">PDI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pentaho">Pentaho (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/people-analytics">People Analytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/people-cloud">People Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/peoplefluent">Peoplefluent (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/peoplesoft">PeopleSoft (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance">Performance (30)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance-analytics">Performance Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/performance-management">Performance Management (89)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/perillon">Perillon (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/personalization">Personalization (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pervasive">Pervasive (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pim">PIM (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pim-product-information-management-sales-marketing">PIM, Product Information Management, Sales, Market (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pipkins">Pipkins (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pitney-bowes">Pitney Bowes (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pivotal-hd">Pivotal HD (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pivotlink">PivotLink (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planmill">PlanMill (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planning">Planning (88)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/planview">Planview (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plateau">Plateau (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/platfora">Platfora (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/platform-as-a-service">Platform as a service (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/playbook">Playbook (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plex">Plex (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/plunk">Plunk (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/portfolio-management">Portfolio Management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/portrait-software">Portrait Software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/potus-department-of-labor-flsa-part-541-overtime-r">POTUS, Department of Labor, FLSA, Part 541, Overti (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/powercenter">PowerCenter (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive">Predictive (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-analytics">Predictive Analytics (203)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-hcm">Predictive HCM (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predictive-planning">Predictive Planning (49)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/predixion">Predixion (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/price-and-revenue-management">Price and Revenue Management (70)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/price-optimization">Price Optimization (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pricing-and-promotion-management">Pricing and Promotion Management (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pricing-management">pricing management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pro">PRO (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/process-management">process management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/process-mining">Process Mining (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procure-to-pay">Procure-to-Pay (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procurement">procurement (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-experience-management">product experience management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-information-management">Product Information Management (90)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services">Professional Services (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services-automation">Professional Services Automation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profit-velocity">Profit Velocity (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability">Profitability (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability-management">profitability management (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-management">Project Management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-planning">Project Planning (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-portfolio-management">Project Portfolio Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projecthelp">ProjectHelp (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projectops">ProjectOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projector">Projector (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/promptbook">Promptbook (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/property-technology">Property Technology (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros">PROS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros-pricing">PROS Pricing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/psa">PSA (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/publishing">Publishing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing">purchasing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing-sourcing-payments">Purchasing/Sourcing/Payments (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pv-accelerator">PV Accelerator (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pxm">PXM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qad">QAD (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik">Qlik (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qlik-sense">Qlik Qlik Sense (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qonnections">Qlik Qonnections (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-analytics-qlik-sense-qlik-business-intelligen">Qlik, Analytics, Qlik Sense, Qlik, Business intell (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qliktech">Qliktech (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlikview">QlikView (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qualtrics">Qualtrics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quantrix">Quantrix (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quinterian">Quinterian (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quotas">Quotas (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qvidian">Qvidian (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/r">R (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ransomware">Ransomware (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapide">Rapide (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapidminer">Rapidminer (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rdbms">RDBMS (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-estate">real estate (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-time">Real-time (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reconciliation">Reconciliation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recovery">Recovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recruiting">Recruiting (41)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recurring-revenue">Recurring Revenue (19)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/report">report (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting">Reporting (78)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting-xbrl-ixbrl-document-management">Reporting, XBRL, iXBRL, document management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/research">Research (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/resilience">Resilience (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/responsetek">ResponseTek (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/retail-analytics">Retail Analytics (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue">Revenue (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-and-lease-accounting">revenue and lease accounting (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-management">Revenue Management (33)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance">Revenue Performance (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance-management">Revenue Performance Management (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-recognition">revenue recognition (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-lease-and-tax-accounting">Revenue, Lease and Tax Accounting (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution">Revolution (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution-analtyics">Revolution Analtyics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rightnow">RightNow (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rim">RIM (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk">Risk (37)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-compliance-grc">Risk & Compliance (GRC) (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-analytics">Risk Analytics (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/riversand">Riversand (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roambi">Roambi (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-automation">robotic automation (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-finance">robotic finance (38)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-process-automation">Robotic Process Automation (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roi">ROI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rpa">RPA (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rypple">Rypple (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sop">S&OP (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saas">SaaS (27)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sab-miller">SAB Miller (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saba">Saba (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage">Sage (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage-software">Sage Software (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales">Sales (169)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-and-operations-planning">Sales and Operations Planning (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-cloud">Sales Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-coaching">Sales Coaching (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-commission">Sales Commission (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-compensation">Sales Compensation (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-effectiveness">Sales Effectiveness (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement">sales enablement (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement-and-execution">Sales Enablement and Execution (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-engagement">Sales Engagement (25)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-force-automation">Sales Force Automation (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-forecasting">Sales Forecasting (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-operations">Sales Operations (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance">Sales Performance (446)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management">Sales Performance Management (77)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management-spm">Sales Performance Management (SPM) (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning">Sales Planning (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning-and-analytics">Sales Planning and Analytics (18)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-tax">Sales Tax (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-marketing-sales-performance-management-marke">Sales, Marketing, Sales Performance Management, Ma (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-sfa-spm-sales-performance-management-sales-c">Sales, SFA, SPM, Sales Performance Management, Sal (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salescloud">SalesCloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salesforce-com">Salesforce.com (58)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap">SAP (76)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-epm">SAP EPM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-hana">SAP HANA (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-insider">SAP insider (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-lumira">SAP Lumira (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-netweaver">SAP NetWeaver (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-predictive-analytics">SAP predictive analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-teched">SAP TechEd (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sapphire">SAPPHIRE (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sarbanes-oxley">Sarbanes Oxley (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas">SAS (26)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas-institute">SAS institute (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sasb">SASB (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sase">SASE (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/savi">Savi (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm">SCM (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm-demand-planning">SCM Demand Planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scout-rfp">Scout RFP (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scribe-software">Scribe Software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/search">Search (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec">SEC (19)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec-software">SEC Software (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secfinops">SecFinOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secops">SecOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security">Security (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security-posture">Security Posture (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sedar">SEDAR (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/self-service">Self-service (41)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/semantic-model">semantic model (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-supply-chain">Service & Supply Chain (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-cloud">Service Cloud (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/servigistics">Servigistics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfa">SFA (24)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfdc">SFDC (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sharepoint">SharePoint (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/shoretel">ShoreTel (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/siemens-enterprise-communications">Siemens Enterprise Communications (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/signal-demand">Signal Demand (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/silverpop">SilverPop (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/simba">Simba (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-financial-close">Smart Financial Close (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-manufacturing">Smart Manufacturing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-phones">Smart Phones (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-watches">Smart Watches (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-analytics">Smarter Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-workforce">Smarter Workforce (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smb">SMB (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snaplogic">SnapLogic (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snowfly">Snowfly (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soa">SOA (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soc">SOC (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social">Social (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-business">Social Business (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-collaboration">Social Collaboration (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-crm">Social CRM (128)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-dynamx">Social Dynamx (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media">Social Media (365)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-analytics">Social Media Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-intelligence">Social Media Intelligence (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-recognition">Social Recognition (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/socialtext">SocialText (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sococare">SoCoCare (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/softscape">Softscape (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software">Software (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software-as-a-service">Software as a Service (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sourcing">sourcing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sovereign-cloud">Sovereign Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sox">SOX (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spago-solutions">Spago Solutions (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spatial-data-quality">Spatial Data Quality (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/speech-analytics">Speech Analytics (177)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/splunk">Splunk (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spm">SPM (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spotfire">Spotfire (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spreadsheets">Spreadsheets (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spss">SPSS (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sql-h">SQL-H (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sqlstream">SQLstream (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/star-analytics">Star Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/statistics">Statistics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/stibo-systems">Stibo Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/stratahadoop">Strata+Hadoop (56)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-analytics">Streaming Analytics (28)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-data">Streaming Data (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/streaming-data-events">Streaming Data & Events (32)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscriber-experience">Subscriber Experience (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-billing">Subscription Billing (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-management">Subscription Management (68)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/subscription-recurring-revenue-zuora">Subscription, Recurring Revenue, Zuora (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/successfactors">SuccessFactors (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumo-logic">Sumo Logic (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumtotal">SumTotal (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sumtotal-systems">SumTotal Systems (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sungard">Sungard (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sunsystems">Sunsystems (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supplier-relationship-management">supplier relationship management (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain">Supply Chain (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-management">supply chain management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-performance">Supply Chain Performance (253)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/supply-chain-planning">Supply Chain Planning (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/surface">Surface (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/surveys">Surveys (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability">Sustainability (38)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability-and-esg">Sustainability and ESG (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sustainability-management">Sustainability Management (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/swordciboodle">SwordCiboodle (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase">Sybase (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase-iq">Sybase IQ (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sybase-mobile-industry">Sybase. 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style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-planning">tax planning (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-software">Tax Software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-datawarehouse">Tax-Datawarehouse (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tax-tax-provision-transfer-pricing">tax; tax provision; transfer pricing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tcm">TCM (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology">Technology (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-innovation">Technology Innovation (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-innovation-award">Technology Innovation Award (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-spending">Technology Spending (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/technology-vendors">Technology Vendors (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/telephony">Telephony (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/teradata">Teradata (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/teradata-aster">Teradata Aster (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/text-analytics">Text Analytics (182)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tez">Tez (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/thomson-reuters-multinational">Thomson-Reuters multinational (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/threat-landscape">Threat Landscape (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/threat-management">Threat Management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tibco">Tibco (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tidemark">Tidemark (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/time-attendance">Time & Attendance (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tm">TM (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/tm1">TM1 (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/topic">Topic (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/total-compensation-management">Total Compensation Management (46)</a> </li> <li style="display:none;"> 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the New Teradata", "mainEntityOfPage": "https://blog.isg-research.net/2011/03/05/aster-data-is-now-part-of-the-new-teradata", "datePublished": "04/03/2011", "dateModified": "04/03/2011", "articleBody": "There has been a spate of acquisitions in the data warehousing and business analytics market in recent months. In May 2010 SAP announced an agreement to acquire Sybase, primarily for its mobility technology and had already been advancing its efforts with SAP HANA and BI. In July 2010 EMC agreed to acquire data warehouse appliance vendor Greenplum. In September 2010 IBM countered by acquiring Netezza, a competitor of Greenplum. In February 2011 HP announced after giving up on its original focus with HP Neoview and now has acquired analytics vendor Vertica that had been advancing its efforts efficiently. Even Microsoft shipped in 2010 its new release of SQL Server database and appliance efforts. Now, less than one month later, Teradata has announced its intent to acquire Aster Data for analytics and data management. Teradata bought an 11% stake in Aster Data in September, so its purchase of the rest of the company shouldn’t come as a complete surprise. My colleague had raised the question if Aster Data could be the new Teradata but now is part of them. All these plays have implications for how enterprises manage and use their fast-growing stores of data. We’re living in an era of large-scale data, as I wrote recently. Founded in 1979, Teradata has been a dominant player in this market for years. Teradata was a pioneer in massively parallel processing (MPP) for database systems that I recently described, a concept behind much of today’s analytic database market, including all the recently acquired vendors mentioned above. When I worked at Oracle in the late 1990s, Teradata was the chief competitor when pursuing 1 terabyte (TB) data warehouse opportunities. Yes, managing a single terabyte was considered a significant challenge then that few vendors were ready to take on. Although the data volumes have grown, little else has changed since those years with Oracle now competing against more providers despite its recent promotions of its second generation Oracle Exadata appliance and it Oracle 11g Release 2 database at the 2010 Oracle OpenWorld. Of course, that has all changed long since. Over the last few years Aster Data established itself as a player in the data warehousing market with an MPP relational database product. It also embraced the MapReduce parallel processing technology earlier than most other data warehouse vendors. MapReduce is a key concept of the increasingly significant Apache Hadoop project; it appears that Aster Data’s proprietary implementation of MapReduce was a significant factor in Teradata’s decision to acquire it. The tone of the joint Teradata/Aster Data briefing for analysts even suggests that Teradata is attempting an end-run around Hadoop. Aster Data has successfully developed a market niche of customers doing analysis of unstructured data and social networks. Both of these are activities one might use Hadoop to do. The company also had success in other segments, such as financial services, marketing services, retail and e-commerce. Aster Data customers should benefit from the increased resources Teradata can invest in developing the product, and the rich heritage of Teradata in this space should enhance the infrastructure supporting the Aster Data product line. For example, Teradata’s workload management tools are among the best in the industry. However, even if the association with Teradata brings some of these capabilities to the Aster Data platform, it’s likely that the cost advantages of Aster Data over Teradata will decline over time. Integration into the Teradata organization and technology stack could detour Aster Data from its previous path of innovation. So customers may see a longer time between releases and maybe a less ambitious product roadmap. Obviously Teradata has many more customers than Aster Data and is most concerned about them. They, too, might see some negative impact on development schedules, but on the plus side they instantly receive a new source of technology that could be beneficial to them. The question, as yet unanswered until a roadmap is published, will be how quickly Teradata customers can take advantage of the innovations Aster Data has brought to market. Teradata officials talked about Aster Data retaining some independence in operations, in the product line and perhaps in identity, but integration is the key to making the acquisition valuable to the Teradata customer base. One likely outcome favorable to current and future Teradata customers is more support for industry-standard server hardware, which was specifically mentioned as a benefit of the acquisition. Teradata customers may also benefit from the columnar database capabilities if those capabilities are ported to the main Teradata product line. There were a couple of notable omissions from the discussion as the acquisition was announced. Both Teradata and Aster Data had partnerships with SAS and Cloudera, the Hadoop vendor. The SAS relationship provided advanced analytics including statistical analyses and data mining embedded within the database. Teradata may be looking to shift to using Aster Data’s embedded analytic capabilities. With respect to Hadoop, although both companies had publicly announced partnerships with Cloudera, neither had offered any deep integration with Hadoop. I expect such an arm’s-length relationship to continue, but I suspect the new combination will put more weight behind the embedded SQL-MR capability that Aster Data developed. Teradata may be large enough to attempt such an independent strategy, but I think it would be a mistake to alienate the entire Hadoop community that I have been researching, which in my opinion is a market not served by Teradata today. Customers of other data warehousing companies should feel little immediate negative impact from this coupling. In fact, more competition over the analysis of unstructured data could spark those companies to enhance their product offerings. As I noted in beginning, the market for independent products has shrunk over the last six months, so the remaining vendors could see an increase in revenues as customers who were using Aster Data or others in preference to larger companies may start to consider other alternatives. However, at some point the musical chairs of the large-scale database acquisitions is going to stop, and when that does, if your vendor is still reliant on venture capital (VC) funding (i.e., does not yet have positive cash flow) you could have a problem. The VCs may decide to stop funding such a company, which could force them to scale back on their plans. Probably some other shoes will drop before the market shift is over. Dell had a partnership with Aster Data that is now called into question. Dell therefore might seek an alternative partner or even acquire one of the other vendors to get into the game. Potential candidates include many we have been assessing including: 1010Data, Calpont, Kognitio and ParAccel. I didn’t include Infobright in this list because it doesn’t have an MPP offering, which seems to be table stakes now. This latest acquisition could also lead to another round of acquisitions based on Hadoop or NoSQL technologies. Or perhaps the game may take a step in the direction of complex event processing or predictive analytics. We’ll have to wait and see although at this rate we may not have to wait long! Regards, Ventana Research", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Secrets Revealed in Columnar Database Technology", "mainEntityOfPage": "https://blog.isg-research.net/2011/03/04/secrets-revealed-in-columnar-database-technology", "datePublished": "03/03/2011", "dateModified": "03/03/2011", "articleBody": "This is the second in a series of posts on the architectures of analytic databases. The first post addressed massively parallel processing (MPP) and database technology. In this post, we’ll look at columnar database technology. Given the recent announcement of HP’s plan to acquire Vertica, a columnar database vendor, there is likely to be even more interest in columnar database technology, how it operates and what benefits it offers. Fundamentally, columnar database technology offers two primary benefits, increased speed and reduced storage requirements. We repeatedly emphasize the importance of speed to end users. Our benchmark research on business intelligence and performance management show that performance is a key consideration among those seeking improvement in this area. Besides speed and reduced storage other benefits may exist in particular implementations, but these are the two most significant – and most directly attributable to columnar technology. Columnar technology was made popular by Sybase, now part of SAP, with its IQ product. Today, many other vendors have brought columnar database products to market, among them 1010data, Calpont, Infobright, ParAccel, Sand Technology, SenSage and Vertica who was just acquired by HP. Recently, traditional row-oriented database vendors – among them Aster Data now being acquired by Teradata, EMC Greenplum and Oracle – have added some columnar capabilities to their products including. Additionally, in-memory database technologies frequently utilize column-based architectures. Columnar database technology typically includes columnar storage and/or columnar database execution. We’ll talk about both and why each is important. Columnar storage turns the traditional relational (row-oriented) database on its side. Instead of storing data in rows, which is the norm for databases such as IBM DB2, Microsoft SQLServer, MySQL and Oracle, data is stored by column. Whereas a row would consist of customer name, order date, amount of the order, order number, shipment date, method of shipment, and so on, a column of data would consist of all the customer names. A separate column would contain all the order dates. Another would contain all the order amounts, and so on. Row-oriented storage is more efficient (that is, faster) when recording or retrieving the data necessary to process a transaction. The disk only needs to go to one location for either operation. Column-oriented storage, on the other hand, is more efficient (that is, faster) when querying the same item across many rows of data, which is common in most business intelligence (BI) queries. Since all the similar items are grouped together on disk the database can scan them more rapidly than if it had to retrieve all the fields in a record just to get at one or two of them. The difference is magnified when querying hundreds of millions or billions of rows. Columnar organization yields several storage-related benefits as well. First, columnar databases make little or no use of user-defined indexes, so they require no additional storage other than that for the data. Second, once the data is sorted and stored by column, the potential for compression increases dramatically. One simple example I like to use is to imagine storing billions of stock quotes, instances of web activity, network activity or any other type of data that includes a date field. Even if you kept five years of history, there are only 1826 or 1827 (depending on leap years) unique date values in that time period. With a row-oriented storage approach, you would need to store all of those billions of values on disk. Using a columnar storage technique you could potentially store each date only once and record the number of occurrences for that date. This technique, referred to as run-length encoding, can lead to dramatic compression ratios, as suggested by the example. Other techniques are available too but I won’t go into those at this point. What is important to note is that the benefits of compression are significant enough that row-oriented vendors have figured out how to engineer some these same techniques into their storage algorithms. Let’s move on to the second major aspect of columnar databases, columnar execution. Columnar databases not only can reduce the amount of storage required, but also in many cases can reduce the amount of memory required to process the data. If the compact representation of the data can be retained as queries are processed, it follows that less memory is required to manipulate the data. Without going into all the details, some columnar database engines can select subsets of data and perform joins on the compact representation, resulting in even more efficiency and performance gains. Columnar databases do have their downsides. They are typically less efficient when it is necessary to update or delete data, for several reasons. First and foremost, updating or deleting a single row of data requires finding several locations on disk where the individual columns are stored. Even single row retrievals can be slower, resulting in a noticeable performance difference. I know of one financial service firm that has spreadsheets with thousands of individual data references in separate cells of the spreadsheets they use. Even though each individual lookup only takes a small fraction of a second longer, the overall performance is much slower because the difference is magnified many times over. The second reason updates or deletes can be slower is the organization of the data. If a single value in the middle of a long list of values is deleted or updated, some portion of the page needs to be reorganized. Depending on the vendor’s approach this reorganization issue can become significant over time as more and more updates or deletes are processed. Because of these issues with columnar databases, at least two hybrid implementations have emerged. One form of hybrid is a row-oriented database with some columnar capabilities added, such as those noted above. The other form is a columnar database with some row-oriented capabilities added on. Although a hybrid architecture minimizes the downsides of each approach, I suggest that you think about it this way: While a hybrid minimizes the downsides it does not eliminate them. The bottom line is that your database engine will be primarily column- or row-oriented, and you should consider that in the selection and evaluation process. Over time I suspect the hybrid techniques may approach each other in terms of performance and capabilities, but for the time being I think you will still see some differences depending on the specific use case and workload you are trying to manage. Columnar databases can also be implemented as MPP (massively parallel processing) systems, as hardware appliances or as in-memory systems. Calpont, Paraccel and Vertica are examples of MPP columnar databases. Kickfire, acquired by Teradata, is probably the best example of an appliance-based columnar system, while the SAP High-Performance Analytic Appliance (HANA) is probably the most ambitious in-memory columnar system. I hope you can use this information as you investigate database vendors and evaluate their product offerings. Consider whether you need columnar storage alone or if you need columnar execution as well. Consider whether the cost of learning and managing a separate system is worth the benefits. Consider your current and future use cases and how they match a row-versus-column orientation. Regardless of what you think you understand about the different approaches make sure to conduct a proof of concept with your data and your workload. As you might imagine from this article, the differences between these approaches can be subtle and a proof of concept is likely to be the only way to evaluate which approach works best for you. Regards, Ventana Research", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Living in the Era of Hadoop and Large-Scale Data", "mainEntityOfPage": "https://blog.isg-research.net/2011/03/03/living-in-the-era-of-hadoop-and-large-scale-data", "datePublished": "02/03/2011", "dateModified": "02/03/2011", "articleBody": "It’s clear that now we are living in the era of big data. The stores of data on which modern businesses rely are already vast and increasing at an unprecedented pace. Organizations are capturing data at deeper levels of detail and keeping more history than they ever have before. Managing all of the data is thus emerging as one of the key challenges of the new decade. The solutions to this challenge vary, but interest in them seems to be universal. The largest database vendors and others that wish to compete with them are developing or acquiring various technologies, among them database appliances, massively parallel databases, and columnar databases. I have written recently about another contender in this arena: an open source, parallel processing technique named Apache Hadoop, which has gained popularity as a way to deal with very large sets of data. The rise of Hadoop has been dramatic. It has been successfully deployed at some of the largest Internet-based organizations in the world, including eBay, Facebook, Google and Yahoo. Seeing this, other organizations whose business depends on managing large amounts of data have begun to explore Hadoop as well. Last October 12 in New York City, Hadoop World attracted approximately 900 attendees. Local Hadoop user groups are popping up around the world and attracting hundreds of users . The Hadoop User Group on LinkedIn has more than 4,000 members. More and more software vendors area adding support for Hadoop. Daniel Abadi, who has done extensive work on a project called HadoopDB , recently received funding for a big-data start-up company. However, because the level of interest in Hadoop has grown so rapidly, there is little research on the business applications that use Hadoop and the IT requirements necessary to support a successful Hadoop deployment. Many audiences could benefit from independent research, information and analysis in this area. CIOs considering Hadoop need more information about the business case and the characteristics of successful deployments. Organizations developing Hadoop and related software products need to understand better the characteristics of the developing markets they seek to serve. And software companies looking to incorporate Hadoop need guidance on how best to adapt their own product lines. Another consequence of the dramatic rise in popularity of Hadoop is much confusion among vendors as well as buyers. The rapidly changing market situation makes it difficult for vendors to determine how to allocate investments in Hadoop in ways that will maximize revenues. Without reliable information, vendors are left to guess at what potential customers need, and so they risk creating a flawed strategy based on inaccurate assumptions about the market. To help address these issues, Ventana Research is conducting benchmark research that will gather data on the enterprise use of Hadoop, its key components and related technologies; we expect it to provide previously unavailable insights. The research will establish how Hadoop is being used and for what business purposes. It also will explore whether Hadoop is replacing existing technologies and, if so, which ones. In addition, this program will identify benefits that organizations are realizing from the use of Hadoop as well as issues they have encountered. Background about this benchmark research can be found here as will the results once they are available and if you are looking to participate. Whether you are working with Hadoop or using some other technology to tackle your big-data challenges, please participate in this research to help advance the state of the industry with respect to managing and analyzing large-scale data. Regards, Ventana Research", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Kognitio Pablo Brings MDX to Data and Analytics", "mainEntityOfPage": "https://blog.isg-research.net/2011/02/14/kognitio-pablo-brings-mdx-to-data-and-analytics", "datePublished": "14/02/2011", "dateModified": "14/02/2011", "articleBody": "Kognitio announced the addition of MultiDimensional eXpressions (MDX) capabilities for its WX2 product line. John Thompson, CEO of U.S. operations, and Sean Jackson, VP of marketing, shared some of the details with me recently. I find the marriage of MDX and large-scale data both technically challenging and potentially valuable to the market. Kognitio develops and markets a massively parallel processing (MPP) database system targeted at the business intelligence and data warehousing market for use as an analytical database. WX2 is available as on-premises software, an appliance or software as a service (SaaS). The product has a relatively long history, having been developed in the early 1990s as part of Whitecross Systems, which merged with Kognitio in 2005. Headquartered in the U.K. the company is making a push for a bigger presence in the U.S. using funds raised for the purpose in December. MDX provides more powerful ways than SQL to express relationships between data elements that are organized into cubes for online analytical processing (OLAP) analysis. Although a number of vendors have implemented OLAP capabilities on top of relational databases (referred to as ROLAP), it’s difficult to create meaningful sets of derived data using this approach. MDX makes it easier to express formulaic relationships between different data elements, thus enabling organizations to create relatively sophisticated historical or prospective measures to assess or project the performance of their business. To illustrate the difference, with SQL you could project sales as a percentage of last year’s sales, but with MDX you could project sales based on last year’s sales along with this year’s advertising programs and hiring plans and include a projected ramp-up period for each sales person. Using MDX it also is much easier to create what-if and planning types of analyses. Our benchmark research shows that only 22% of organizations currently can conduct what-if analysis for planning and forecasting, but 84% said it is important or very important to add these capabilities for decision-making and performance management. Let me note that providing MDX or other what-if analyses over large amounts of data presents some significant technical challenges; these are mostly based around performance issues, although the amount of memory required for the analyses can be another issue. Because MDX makes it easier to express complex formulas, any calculation could reference any other data point in the entire data set. The challenge lies in working through the calculation dependencies and getting the necessary data into memory quickly to perform the calculations and deliver the results. As I pointed out in the MPP blog post referenced above, it becomes even more challenging when the data can reside on another node in the MPP system. Kognitio will introduce one of the first products to combine MPP and MDX capabilities. SAP also provides MDX capabilities for its high-performance analytic appliance (SAP HANA), which my colleague commented on As more and more data gets stored and analyzed using MPP systems, the MDX capabilities can help organizations produce more accurate analyses of where their business is headed and thus make better business decisions. The alternative today is to operate on subsets of data, which can potentially reduce the accuracy of the results or can lead to more complicated systems that attempt to combine results from multiple separate analyses. The MDX capabilities of Kognitio’s WX2, referred to as Pablo, are not a separate product but are built into it. The company touts these features as helping with the process of producing and managing analytic cubes. While these capabilities will help with that process by maintaining the cubes directly within WX2, I see as much potential value in being able to do more meaningful analyses over larger amounts of data. These new MDX capabilities are scheduled for availability in June. Initially at least they come with some caveats. If you are evaluating Kognitio, be aware that the initial target client tool is Excel pivot tables. You’ll have to wait to use another BI front-end tool. I also advise you to explore what subset of MDX is supported to make sure the expressions that are critical to your business models can be included in your models. Also, Kognitio provides no inherent mechanism to process updates for purposes of what-if analysis, but the company claims you can update the underlying relational data using other mechanisms and the cubes will be recalculated automatically. Finally the biggest caveat will be performance. In addition to assessing the overall performance of the new system vs. existing OLAP systems, you will want to see how much memory is required for the cubes and what happens when the system exceeds the available amount of physical memory. The litmus test will be to define a cube that is larger than the memory on one of the nodes and see whether it continues to perform adequately. Regardless of whether Kognitio gets it right in the first release, it’s encouraging to see vendors advancing the types of analytics that can be performed on large data. I expect others to follow suit, and that will be good for business users who need to perform planning and what-if analysis on ever larger amounts of data. Let me know your thoughts or come and collaborate with me on Facebook, LinkedIn and Twitter . Regards, Ventana Research", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Secrets Revealed in Massively Parallel Processing and Database Technology", "mainEntityOfPage": "https://blog.isg-research.net/2011/01/19/secrets-revealed-in-massively-parallel-processing-and-database-technology", "datePublished": "19/01/2011", "dateModified": "19/01/2011", "articleBody": "This is the first in a series of posts on the architectures of analytic databases. This is relational database technology that has been “supercharged” in some way to handle large amounts of data such as typical data warehouse workloads. The series will examine massively parallel processing (MPP), columnar databases, appliances and in-database analytics. Our purpose is to help those evaluating analytic database technologies understand some of the alternative approaches so they can differentiate between different vendors’ offerings. There is no single best solution for all analytics for all types of applications; usually the decision involves a series of trade-offs. Understanding what you might be giving up or gaining, you may be able to make a better decision about which solution is best for your organization’s needs. Analytic databases exist because of the need for speed and scale. Our benchmark research on business intelligence and performance management show that performance and scalability are key considerations seeking improvement in this area. Some years ago operating on summary data via OLAP cubes could provide speed to end users, but that approach is no longer sufficient for many types of analyses. In this post we’ll look at MPP concepts including how these techniques speed up processing, what some of the challenges are and the different approaches that vendors use. Fundamentally, massively parallel processing is about splitting a database workload across multiple servers or nodes and executing a portion of the workload in parallel on each of the nodes. Once the parallel activities are completed, the results from each node are stitched together to deliver the end results. MPP systems eliminate the need to manually split workloads across multiple machines. The manual technique known as sharding became a common way to overcome limitations of single-server database systems such as MySQL. Many organizations found that a single instance of their database could not deliver the performance or scale they needed, so they created application level schemes to split the data manually across multiple machines. For instance, all data for customers whose last name began with A-M would be stored in one database instance on one machine and all data for N-Z on another. With this approach and a lot of effort (i.e., coding) you could get theoretically almost twice the performance and twice the scale out of your system. However, the amount of coding and maintenance in such a system can become significant. Instead many organizations have opted to purchase an MPP system with the built-in ability to split workloads. As you explore different MPP architectures you’ll need to understand how a vendor segments the data across the nodes in the system as well as how it segments the workload. Both are necessary to accomplish the maximum degree of parallelization in the system. If the data is not evenly distributed across the nodes, it can affect the speed of the queries. For example, suppose you have a cluster consisting of five nodes. If 80% of your data is on one node and 5% of the data is on each of the other nodes, it’s obvious that a query would have to wait for the node with 80% of the data to finish its work while the nodes with only 5% of the data probably sat idle. If the data were split evenly with 20% on each node, it’s likely that all five nodes would finish their work at approximately the same time and considerably faster than the unbalanced arrangement. When it comes to how the workload is segmented across the nodes, the data distribution dictates which nodes must be involved in queries, but vendors have adopted different styles of workload distribution. MPP systems can be designed with either homogeneous or heterogeneous workload distribution. In the case of homogeneous nodes, the workload – whether loading or querying data – is distributed to all nodes in the system. In the case of heterogeneous nodes, each node is designated for a specific type of workload. For instance, queries are directed to one set of nodes and load processes are directed to a different set of nodes. It is not obvious that one approach is better than the other. However, in the case of homogeneous nodes, the burden is on the software vendor to design a system that can ensure an optimized and balanced workload. In the case of heterogeneous nodes, the burden is shifted somewhat to the enterprise deploying the system. If the load processes are slow, you can alter the number of nodes dealing with the load processes, by adding more nodes to the system or changing the designation of some of the query nodes to data loading. I like to think of this trade-off as the difference between an automatic transmission and a standard transmission. Some people like the control offered by the standard transmission, while others don’t want to have to think about shifting. Another issue to understand is where queries are initiated within the system. The initiation process can become a bottleneck if all queries must funnel through one or a few nodes of the system. The reason is that the node initiating the query has extra responsibilities. Some amount of post-processing is necessary by the query initiator. For instance, if the user requests the top 10 products based on sales, each node is asked to produce its list of the top 10 within its segment of the data and ships the results to the initiator. Then the initiator compares each of the top 10 results sets to come up with the top 10 overall. Ideally the bulk of the work is done in parallel on each of the nodes but then a small amount of work is done on the aggregated results on the initiating node alone. Unfortunately it’s not always that simple, and the load on the initiator can become significant. Not all problems can be neatly divided and performed on a specific segment of the data and then combined at the end of the process for some finishing touches. Some calculations require data to move back and forth across multiple nodes. For example, supposed data is segmented by across nodes by customer ID and you want to calculate each customer’s revenue as a percentage of the best-selling product in each region, state or city – in other words, which customers are contributing the most to the products that are selling well in that area. The data for one customer may all be on the same node, but to calculate total product sales in each region city or state you have to look at sales from all the nodes since any customer could have made a purchase in any region. The simplest approach is to ship all the product sales data to the query initiator and have the initiator perform the rest of the processing. While this approach may be the best strategy, it probably is not. The query optimizer needs to evaluate a series of trade-offs to get the best response time for the query. Should it ship all the data to one node and do all the processing on that one node? While not highly parallel, that strategy might work if the total amount of data that needs to be combined is relatively small, for example, if the sales are restricted to just a few products and a few time periods. Alternatively, should the query optimizer ship some portion of data to each of the nodes to speed up processing? This scenario might work if there is a relatively small set of data that can be constructed (for example, product sales by region) that would be combined with large amounts of data on each node. These examples oversimplify the complexities that the optimizers must deal with. For examples of some of the considerations in MPP query plan optimization and the way some vendors have tackled these issues, see these posts by EMC Greenplum and Vertica. Queries can be much more complicated than the examples given above; data distribution schemes vary and the speed of moving data across nodes is affected by interconnect speeds, storage medium and volume of data to be moved. Query optimizers need to consider many points and must have algorithms that produce efficient plans. An IT organization considering an MPP database product needs to understand a few things in order to evaluate and maximize the performance of different database systems, starting with the data distribution scheme. Typically, the person designing the database implementation must provide some guidance or information to the MPP database product (such as which keys or columns to use in its distribution scheme) to help it distribute data in a way that produces a highly parallel and evenly distributed workload. I recommend that you test your typical query workload against the database to see what kind of results you get. If your workload never changes, this approach may be sufficient. However, if your workload has an ad-hoc component or if it changes over time, odds are you will encounter some sort of challenge in query tuning sooner or later. In that case, you need to understand the types of techniques the optimizer uses in its query plan design. Here are some of the issues to consider: • Where do queries get initiated? Are there enough nodes processing queries? • Similarly, are there enough nodes loading data? • How does data loading impact query processing? If the nodes are separate, this might be easier to understand and address a conflict between loading and query processing. • How much data and which data (if any) should be replicated across nodes? Keeping frequently accessed data on all nodes can minimize the movement of data across nodes and speed processing. • Can redundant copies of the data (perhaps used to achieve high availability) be used in the query plans to minimize data movement across nodes and facilitate more local joins? • What tools are available to you to understand the query execution plans? Can you use these tools to identify the bottlenecks? The bottom line is that you need to understand the “secrets” of the MPP database systems you are considering or using. One is the data distribution strategy and the extent to which you can or need to influence it. Another key is the workload distribution strategy and what causes it difficulties. Evaluate those difficulties and determine how they might affect your typical workloads. Finally, there are additional tricks or techniques that can be used to maximize parallelization and minimize data movement. Evaluate whether those additional techniques are well documented and easily applied when needed. Let me know your thoughts or come and collaborate with me on Facebook, LinkedIn and Twitter . Regards, Ventana Research", "image": { "@type": "ImageObject", "url": "", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "ISG Software Research", "url": "https://blog.isg-research.net/author/isg-software-research", "sameAs": "https://www.linkedin.com/company/ventana-research" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script>  <script data-hs-allowed="true" src="/_hcms/forms/v2.js"></script> <script data-hs-allowed="true"> var options = { portalId: '2035844', formId: 'fa276fc9-9a16-4673-a644-1fcbedcc3c24', formInstanceId: '556', pageId: '3884418273', region: 'na1', inlineMessage: "Thanks for subscribing!", rawInlineMessage: "Thanks for subscribing!", hsFormKey: "68ab003a58c27330fea8362cccce2c67", css: '', target: '#hs_form_target_module_1552944263900179', contentType: "listing-page", formsBaseUrl: '/_hcms/forms/', formData: { cssClass: 'hs-form stacked hs-custom-form' } }; 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