ISG Software Research Analyst Perspectives

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We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution. <a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a> <a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a> <a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a> <a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a> <a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a> </div> <ul></ul> <div class="bottom_rich_text"> </div> </div> </div> </div> </div> </div> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div id="hs_cos_wrapper_Structured_data" 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"></div> <div class="body-container-wrapper"> <div class="blog-header background-image post_page_2" data-bg-color="secondary" style="background-size:cover;background-position:50%;background-repeat:no-repeat;background-image:url(https://blog.isg-research.net/hubfs/_images/_backgrounds/314838419_1200px.jpg)">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section">  <h1 class="analyst-header-title">Analyst Perspectives</h1>  </div> </div> </div> </div> <div class="content-wrapper-listing blog-single"> <div class="blog-content-wrapper"> <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/2015/08/20/pitney-bowes-doubles-down-on-customer-engagement">Pitney Bowes Doubles Down on Customer Engagement</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 20 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=200&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=100&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=200&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=300&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=400&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=500&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png?width=600&name=vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> From its history of managing postal mail, Pitney Bowes has expanded into products for data management, analytics and location intelligence, as <a href="//marksmith.ventanaresearch.com/2014/05/19/pitney-bowes-brings-new-software-for-better-business-insights/" target="_blank">my colleague Mark Smith noted</a>. Continuing this expansion through internal development and acquisitions of vendors such as Portrait Software and RTC, it has added to its portfolio products that include <a href="http://www.pitneybowes.com/us/customer-information-management.html" target="_blank">customer information management</a> and <a href="http://www.pitneybowes.com/us/customer-engagement-marketing.html" target="_blank">customer engagement</a>. </div> <a href="https://blog.isg-research.net/2015/08/20/pitney-bowes-doubles-down-on-customer-engagement"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/big-data">Big Data</a>, <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance</a>, <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://blog.isg-research.net/topic/business-collaboration">Business Collaboration</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/call-center">Call Center</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/2015/08/20/data-and-analytics-in-the-cloud-is-a-reality-today">Data and Analytics in the Cloud is a Reality Today</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 20 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=200&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=100&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=200&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=300&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=400&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=500&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png?width=600&name=vr_dac_04_widespread_use_of_cloud_based_analytics-3.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Our recently completed <a href="//www.ventanaresearch.com/DataAndAnalyticsintheCloud/" target="_blank">benchmark research on data and analytics in the cloud</a> shows that analytics deployed in cloud-based systems is gaining widespread adoption. Almost half (48%) of participating organizations are using cloud-based analytics, another 19 percent said they plan to begin using it within 12 months, and 31 percent said they will begin to use cloud-based analytics but do not know when. Participants in various areas of the organization said they use cloud-based analytics, but... </div> <a href="https://blog.isg-research.net/2015/08/20/data-and-analytics-in-the-cloud-is-a-reality-today"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/big-data">Big Data</a>, <a href="https://blog.isg-research.net/topic/software-as-a-service">Software as a Service</a>, <a href="https://blog.isg-research.net/topic/operational-performance-management-opm">Operational Performance Management (OPM)</a>, <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://blog.isg-research.net/topic/business-collaboration">Business Collaboration</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/customer-contact-center">Customer & Contact Center</a>, <a href="https://blog.isg-research.net/topic/operational-intelligence">Operational Intelligence</a>, <a href="https://blog.isg-research.net/topic/business-performance-management-bpm">Business Performance Management (BPM)</a>, <a href="https://blog.isg-research.net/topic/data">Data</a>, <a href="https://blog.isg-research.net/topic/information-optimization">Information Optimization</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/2015/08/20/tagetiks-solid-financial-performance-management-suite">Tagetik’s Solid Financial Performance Management Suite</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 20 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=200&name=vr_fcc_shortening_financial_close_updated-1.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=100&name=vr_fcc_shortening_financial_close_updated-1.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=200&name=vr_fcc_shortening_financial_close_updated-1.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=300&name=vr_fcc_shortening_financial_close_updated-1.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=400&name=vr_fcc_shortening_financial_close_updated-1.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=500&name=vr_fcc_shortening_financial_close_updated-1.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png?width=600&name=vr_fcc_shortening_financial_close_updated-1.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <a href="http://www.tagetik.com/" target="_blank">Tagetik</a> is a long-established vendor of financial performance management (FPM) software. Its full-featured suite includes planning, budgeting, consolidation, close management, disclosure management, analysis, dashboards and reporting. The software can be deployed on premises or in the cloud as multitenant software as a service or in a private cloud. Tagetik also offers pre-built integration with <a href="http://www.tagetik.com/software/technology/sap-integration" target="_blank">SAP and SAP HANA</a>, <a href="http://www.tagetik.com/software/technology/tagetik-enabled-by-microsoft-sharepoint" target="_blank">Microsoft SharePoint</a> and <a href="http://www.tagetik.com/software/bi/qlik" target="_blank">Qlik</a> to best support a range of financial management... </div> <a href="https://blog.isg-research.net/2015/08/20/tagetiks-solid-financial-performance-management-suite"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/big-data">Big Data</a>, <a href="https://blog.isg-research.net/topic/governance">Governance</a>, <a href="https://blog.isg-research.net/topic/human-capital">Human Capital</a>, <a href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance</a>, <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://blog.isg-research.net/topic/business-collaboration">Business Collaboration</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://blog.isg-research.net/topic/dashboards">Dashboards</a>, <a href="https://blog.isg-research.net/topic/financial-performance">Financial Performance</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/2015/08/14/interactive-intelligence-and-omnichannel-customer-engagement">Interactive Intelligence and Omnichannel Customer Engagement</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 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=200&name=vr_ngce_research_06_changes_to_improve_engagement-20.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=100&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=200&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=300&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=400&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=500&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png?width=600&name=vr_ngce_research_06_changes_to_improve_engagement-20.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> At the end of last year, <a href="http://richardsnow.ventanaresearch.com/2014/12/26/interactive-intelligence-innovates-in-the-cloud-for-contact-centers/" target="_blank">I wrote</a> about Interactive Intelligence’s release of a new service, PureCloud. It was the company’s first step into the multitenant cloud computing market, using Amazon Web Services and aimed at small-to-midsize contact centers. To help companies understand the different cloud-computing models, I provided answers to <a href="http://www.inin.com/resources/Documents/WP-Best-Cloud-Architecture-for-Your-Contact-Center.pdf" target="_blank">Interactive Intelligence questions on the advancements of these approaches for business</a>. </div> <a href="https://blog.isg-research.net/2015/08/14/interactive-intelligence-and-omnichannel-customer-engagement"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/customer-experience">Customer Experience</a>, <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance</a>, <a href="https://blog.isg-research.net/topic/cloud-computing">Cloud Computing</a>, <a href="https://blog.isg-research.net/topic/call-center">Call Center</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/2015/08/13/optimization-analytics-comes-to-the-mass-market">Optimization Analytics Comes to the Mass Market</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/isg-software-research"> ISG Software Research </a> on 13 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=200&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=100&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=200&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=300&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=400&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=500&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png?width=600&name=vr_big_data_analytics_01_use_of_big_data_analytics-2.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Optimization is the application of algorithms to sets of data to guide executives and managers in making the best decisions. It’s a trending topic because using optimization technologies and techniques to better manage a variety of day-to-day business issues is becoming easier. I expect optimization, once the preserve of data scientists and operations research specialists will become mainstream in general purpose business analytics over the next five years. </div> <a href="https://blog.isg-research.net/2015/08/13/optimization-analytics-comes-to-the-mass-market"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/big-data">Big Data</a>, <a href="https://blog.isg-research.net/topic/performance-management">Performance Management</a>, <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance</a>, <a href="https://blog.isg-research.net/topic/operational-performance">Operational Performance</a>, <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://blog.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://blog.isg-research.net/topic/financial-performance">Financial Performance</a>, <a href="https://blog.isg-research.net/topic/information-management">Information Management</a>, <a href="https://blog.isg-research.net/topic/price-optimization">Price Optimization</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/2015/08/06/data-preparation-is-essential-for-predictive-analytics">Data Preparation is Essential for Predictive 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 06 August 2015 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=200&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=100&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=200&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=300&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=400&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=500&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png?width=600&name=vr_NG_Predictive_Analytics_08_time_spent_in_predictive_analytic_process-1.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <a href="//www.ventanaresearch.com/NextGenerationPredictiveAnalytics/" target="_blank">Our research into next-generation predictive analytics</a> shows that along with not having enough skilled resources, which I discussed in <a href="http://tonycosentino.ventanaresearch.com/2015/07/23/skills-gap-challenges-potential-of-predictive-analytics/" target="_blank">my previous analysis</a>, the inability to readily access and integrate data is a primary reason for dissatisfaction with predictive analytics (in 62% of participating organizations). Furthermore, this area consumes the most time in the predictive analytics process: The research finds that preparing data for analysis (40%) and accessing data (22%) are the parts of... </div> <a href="https://blog.isg-research.net/2015/08/06/data-preparation-is-essential-for-predictive-analytics"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/big-data">Big Data</a>, <a href="https://blog.isg-research.net/topic/microsoft">Microsoft</a>, <a href="https://blog.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://blog.isg-research.net/topic/alteryx">alteryx</a>, <a href="https://blog.isg-research.net/topic/customer-performance">Customer Performance</a>, <a href="https://blog.isg-research.net/topic/operational-performance">Operational Performance</a>, <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://blog.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://blog.isg-research.net/topic/business-performance">Business Performance</a>, <a href="https://blog.isg-research.net/topic/operational-intelligence">Operational Intelligence</a>, <a href="https://blog.isg-research.net/topic/oracle">Oracle</a>, <a href="https://blog.isg-research.net/topic/information-optimization">Information Optimization</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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<a href="https://blog.isg-research.net/topic/chargebacks">chargebacks (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chart-of-accounts">chart of accounts (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chatbots">Chatbots (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chatter">Chatter (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chief-risk-officer">Chief Risk Officer (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/chro">CHRO (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ci-cd">CI/CD (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cicero">Cicero (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cincom">Cincom (2)</a> </li> <li style="display:none;"> <a 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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 (151)</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 Applications (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gaap">GAAP (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gamifiacation">Gamifiacation (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gartner">Gartner (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/genai">GenAI (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI (38)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/genesys">Genesys (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/geographic-information-systems">Geographic Information Systems (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gis">GIS (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/global-payroll">Global Payroll (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/globoforce">Globoforce (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/golden-records">Golden Records. (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, compliance, risk management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/green">Green (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/greenplum">Greenplum (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gui">GUI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hadoop">Hadoop (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hana">HANA (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/harvard-business-review">Harvard Business Review (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hawq">HAWQ (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm">HCM (57)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-analytics">HCM Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-systems">HCM Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-hr-hrms-workforce-management-learning-manageme">HCM, HR, HRMS, Workforce Management, Learning Mana (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdfs">HDFS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdinsights">HDinsights (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdp">HDP (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/headcount-planning">headcount planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-compliance">Healthcare Compliance (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-reform">Healthcare Reform (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/heiler">Heiler (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hewlett-packard">Hewlett Packard (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hirevue">HireVue (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hiring">Hiring (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hive">Hive (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hive9-marketing-performance-management">Hive9 Marketing Performance Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hortonworks">Hortonworks (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hospitality">Hospitality (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/host-analytics">Host Analytics (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp">HP (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp-neoview">HP Neoview (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hp-touchpad">HP Touchpad (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr">HR (56)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-analytics">HR Analytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-management">HR Management (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-managementhrms">HR Management(HRMS) (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-research">HR research (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-tech">HR Tech (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-technology">HR technology (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hr-technology-conference">HR Technology Conference (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hrpayroll">HR/Payroll (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hris">HRIS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hrms">HRMS (47)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/html5">HTML5 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hubspot">HubSpot (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-capital">Human Capital (45)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/human-capital-analytics">Human Capital Analytics 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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/kapplets">Kapplets (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/karmasphere">Karmasphere (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kenandy">Kenandy (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kenexa">Kenexa (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/key-performance-indicators">Key Performance Indicators (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/key-performance-indictors">Key Performance Indictors (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/kinaxis">Kinaxis (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knoahsoft">KnoahSoft (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/knopahsoft">KnopahSoft (1)</a> </li> <li 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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 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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 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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 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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 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href="https://www.youtube.com/c/Ventanaresearch"></a></li> <li><a class="no-decoration" target="_blank" href="https://www.instagram.com/ventana_research/"></a></li> </ul> </div> </div> </div> </div> </div> </div> <a href="javascript:" id="return-to-top"> </a> </footer> </div>  <script defer src="/hs/hsstatic/content-cwv-embed/static-1.1293/embed.js"></script> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/94278782902/1725882474552/Ventana_Research_2023/Ventana_Research_2023_child.min.js"></script> <script> var hsVars = hsVars || {}; hsVars['language'] = 'en-us'; </script> <script src="/hs/hsstatic/cos-i18n/static-1.53/bundles/project.js"></script> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/module_assets/90531062927/1702386872146/module_90531062927_Header-Global.min.js"></script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Pitney Bowes Doubles Down on Customer Engagement", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/20/pitney-bowes-doubles-down-on-customer-engagement", "datePublished": "20/08/2015", "dateModified": "20/08/2015", "articleBody": "From its history of managing postal mail, Pitney Bowes has expanded into products for data management, analytics and location intelligence, as my colleague Mark Smith noted. Continuing this expansion through internal development and acquisitions of vendors such as Portrait Software and RTC, it has added to its portfolio products that include customer information management and customer engagement. I have long maintained that companies can’t really improve customer engagement unless they know their customers. To get to know them they need systems that can extract from all their data stores and analyze all forms of customer data. In this effort, our benchmark research into next-generation customer analytics shows, the most common impediment (for 63% of organizations) to applying such tools is that the data is not readily available. Pitney Bowes addresses this and related issues through Customer Information Management, a suite of products that provide data management and integration, data quality and customer analytics and enable companies to do customer analysis based on a range of data sources. As yet it doesn’t work with many unstructured sources of customer data such as call recordings and text-based records, which are needed to complete the full view of the customer to support information-driven decisions and actions. For me the most interesting features are found in its suite of Customer Engagement products, which although having document management at their core are being developed to include an array of channels of engagement and handling of interactions across the enterprise. Pitney Bowes says that it has a five-year plan to create a single platform that will support omnichannel customer engagement across assisted and self-service channels throughout the customer life cycle and across the enterprise; the primary goal is to help users increase customer lifetime value. Recently the company announced the latest release of a key component, EngageOne Server 4.0, which will be the foundation for the complete platform. EngageOne Server enables companies to manage all aspects of producing electronic documents and delivering them to customers through a variety of channels. It includes capabilities to create and manage document templates, author and approve text-based content, produce required content as a batch process, on demand or interactively, and deliver the content in print, fax, email or SMS forms or on the company’s website. All of this content can be stored securely for later retrieval and use. Release 4.0 adds several new features to enhance functionality and make it easier to use, more scalable, accessible and secure, and cloud-ready. The first non-text-based channel it supports is personalized interactive video (PIV). This comes by way of the acquisition of RTC and to my knowledge makes Pitney Bowes the first vendor to support this as a self-service channel. The concept is simple: Instead of making a phone call customers connect to a voice-activated video that enables them to have an interactive dialogue in much the same way as talking to an agent. The dialogue can include a real person or an animated character asking questions or presenting answers, video clips, links to other shared content such as a website and presentation of content such as a document. It can change dynamically depending on information provided or the customer’s responses to questions, so that with the right programming the interaction can be personalized for the caller and the issue. The interaction thus can take many forms – a Q&A session, a request for information, a complaint about a product or a query about a bill. Here is a relatively simple example: The caller provides his or her name, asks a question about the latest bill, is presented with an image of the bill and then engages in a Q&A session to resolve the issue using the bill to focus the dialogue. In trying the system, I found it easier to use than touch-tone or visual IVR, more engaging than a video call and more satisfying than speech-activated software-based agents. However, I caution companies choosing to work with this channel to create such videos with the customer in mind, ensuring that it supports what customers want to do and how they want to do it, rather than as a means of trying to reduce the cost of handling interactions. Our benchmark research into next-generation customer engagement shows that companies have three main challenges in providing customers with the omnichannel experiences they expect: It is difficult to integrate systems (for 49%), channels are managed as silos (47%), and responses to customers across channels are inconsistent (39%). Addressing all these challenges is not easy, as I explained in a recent perspective about the technologies required to deliver EPIC customer experiences. The Pitney Bowes products have capabilities that can help address these issues, including tools that integrate with third-party products and other tools for collaboration that enable users to share information and join in the resolution of issues, which has the potential to improve consistency in interaction handling. EngageOne Server has more work to be a complete omnichannel product, but Pitney Bowes says it is committed to developing it into a comprehensive customer engagement platform that supports consistent handling of interactions across all channels and throughout the customer life cycle. My research shows that video calling is a channel likely to take off as consumers use video calls to engage with each other. PIV innovatively builds on video calling and has the potential to accelerate adoption of interactive self-service. As I recently wrote I advocate customer lifetime value as a key customer experience metric, but it is not easy to calculate. Pitney Bowes has a development program in place to create products that should help companies meet these objective so I will keenly watch how it develops both its customer information and customer engagement products and recommend that companies seeking to improve these aspects do likewise. Regards, Richard J. Snow VP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_customer_analytics_05_dissatisfaction_with_customer_analytics-4.png", "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": "Data and Analytics in the Cloud is a Reality Today", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/20/data-and-analytics-in-the-cloud-is-a-reality-today", "datePublished": "20/08/2015", "dateModified": "20/08/2015", "articleBody": "Our recently completed benchmark research on data and analytics in the cloud shows that analytics deployed in cloud-based systems is gaining widespread adoption. Almost half (48%) of participating organizations are using cloud-based analytics, another 19 percent said they plan to begin using it within 12 months, and 31 percent said they will begin to use cloud-based analytics but do not know when. Participants in various areas of the organization said they use cloud-based analytics, but front-office functions such as marketing and sales rated it important more often than did finance, accounting and human resources. This front-office focus is underscored by the finding that the categories of information for which cloud-based analytics is most often deemed important are forecasting (mentioned by 51%) and customer-related (47%) and sales-related (33%) information. The research also shows that while adoption is high, organizations face challenges as they seek to realize full value from their cloud-based data and analytics initiatives. Our Performance Index analysis reveals that only one in seven organizations reach the highest Innovative level of the four levels of performance in their use of cloud-based analytics. Of the four dimensions we use to further analyze performance, organizations do better in Technology and Process than in Information and People. That is, the tools and analytic processes used for data and analytics in the cloud have advanced more rapidly than users’ abilities to work with their information. The weaker performance in People and Information is reflected in findings on the most common barriers to deployment of cloud-based analytics: lack of confidence about the security of data and analytics, mentioned by 56 percent of organizations, and not enough skills to use cloud-based analytics (42%). Given the top barrier of perceived data security issues, it is not surprising the research finds that the largest percentage of organizations (66%) use a private cloud, which by its nature ostensibly is more secure, to deploy analytics; fewer use a public cloud (38%) or a hybrid cloud (30%), although many use more than one type today. We know from tracking analytics and business intelligence software providers that operate in the public cloud that this is changing quite rapidly. Comparing deployment by industry sector, the research analysis shows that private and hybrid clouds are more prevalent in the regulated areas of finance, insurance and real estate and government than in services and manufacturing. The research suggests that private and hybrid cloud deployments are used more often for analytics where data privacy is a concern. Furthermore, organizations said that access to data for analytics is easier with private and hybrid clouds (29% for public cloud vs. 58% for private cloud and 67% for hybrid cloud). In addition, organizations using private and hybrid cloud more often said they have improved communication and information sharing (56% public vs. 72% private and 70% hybrid). Thus, the research data makes clear that organizations feel more comfortable implementing analytics in a private or hybrid cloud in many areas. Private and hybrid cloud implementations of data and analytics often coincide with large data integration efforts, which are necessary at some point to benefit from such deployments. Those who said that integration is very important also said more often than those giving it less importance that cloud-based analytics helps their customers, partners and employees in an array of ways, including improved presentation of data and analytics (62% vs. 43% of those who said integration is important or somewhat important), gaining access to many different data sources (57% vs. 49%) and improved data quality and data management (59% vs. 53%). We note that the focus on data integration efforts correlates more with private and hybrid cloud approaches than with public cloud approaches, thus the benefits cannot be directly assigned to the various cloud approaches nor the integration efforts. Another key insight from the research is that data and analytics often are considered in conjunction with mobile and collaboration initiatives which have different priorities for business than IT or in consumer markets. Nine out of 10 organizations said they use or intend to use collaboration technology to support their cloud-based data and analytics, and 83 percent said they need to support data access and analytics on mobile devices. Two-thirds said they support both tablets and smartphones and multiple mobile operating systems, the most important of which are Apple iOS (ranked first by 60%), Google Android (ranked first by 26%) and Microsoft Windows Mobile (ranked first by 13%). We note that Microsoft has a higher percentage of importance here than its reported market share (approximately 2.5%) would suggest. Similarly, Google Android has greater penetration than Apple in the consumer market (51% vs. 41%). We expect that the influence of mobile operating systems related to data and analytics in the cloud will continue to evolve and be impacted by upcoming corporate technology refreshment cycles, the consolidation of PCs and mobile devices, and the “bring your own device” (BYOD) trend. The research finds that usability (63%) and reliability (57%) are the top technology buying criteria, which is consistent with our business technology innovation research conducted last year. What has changed is that manageability is cited as very important as often as functionality, by approximately half of respondents, a stronger showing than in our previous research. We think it likely that manageability is gaining prominence as cloud providers and organizations sort out issues in who manages deployments along with usage and licensing, along with who actually owns your data in the cloud which my colleague Robert Kugel has discussed. As the research shows, the importance of cloud data and analytics is continuing to grow. The importance of this topic makes me eager to discuss further the attitudes, requirements and future plans of organizations that use data and analytics in the cloud and to identify the best practices of those that are most proficient in it. For more information on this topic, and learn more on best practices for data and analytics in the cloud, and download the executive summary of the report to improve your readiness. Regards, Tony Cosentino Vice President and Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_dac_04_widespread_use_of_cloud_based_analytics-3.png", "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": "Tagetik’s Solid Financial Performance Management Suite", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/20/tagetiks-solid-financial-performance-management-suite", "datePublished": "20/08/2015", "dateModified": "20/08/2015", "articleBody": "Tagetik is a long-established vendor of financial performance management (FPM) software. Its full-featured suite includes planning, budgeting, consolidation, close management, disclosure management, analysis, dashboards and reporting. The software can be deployed on premises or in the cloud as multitenant software as a service or in a private cloud. Tagetik also offers pre-built integration with SAP and SAP HANA, Microsoft SharePoint and Qlik to best support a range of financial management needs. The current release, Tagetik 5, has a pleasing and productive consumer-style interface. Its design approach aims at enhancing the user experience and making it easier and less time-consuming to perform common tasks in finance and accounting departments. FPM is a mature software category that generally deals with the full cycle of finance department activities as well as the underlying information technology systems that support them. Thus there are limited differences between vendors’ suites in the required features and functions. We find that buyers often select products by how they execute particular tasks and especially how easy it is to perform them. In response FPM vendors have been putting greater emphasis in the design of their software to enhance the user experience, often through a consumer-style interface. Tagetik addresses the ease-of-use issue in its current release, Tagetik 5, by making it easier for business analysts to create and update basic dashboards without the need for IT department involvement or coding. This facilitates communication and performance monitoring. Users can work in Microsoft Excel, but behind this interface are all of the capabilities of a well-developed software application and database, which eliminate issues that occur when desktop spreadsheets are used in any repetitive collaborative enterprise process such as financial planning and closing. Unlike in desktop spreadsheets, rolling up and consolidating data submissions of any number of participants in a process is almost instantaneous in Tagetik 5. Moreover, unlike desktop spreadsheets, it stores the data with important attributes such as the time period, corporate structure (division or regions, for example), product (anywhere from families down to specific stock keeping units – SKUs – if desired) and currency. The software also offers comprehensive and easy-to-use administration capabilities, especially in creating and modifying business processes for the full range of financial performance management activities. This capability is more than just a convenience for a few people in the finance department. It can make it easier for whole companies to accelerate planning cycles and facilitates high-participation planning and budgeting processes. Modifying processes also enables companies to tightly manage their accounting close process. Our benchmark research on the fast, clean close shows that companies that successfully shorten their close most often (in 71%) attributed their success to being able to manage the process effectively and consistently. With a limited amount of training and no coding, finance department users of Tagetik can define and manage every step of the close process. It supports a continuous improvement approach to managing the close by making it straightforward for the department to modify these individual processes and subprocesses as they assess sources of delay and inefficiency in their close. Tagetik also supports more strategic finance processes. For example, it provides a platform that enables all parts of the business to plan in ways that conform to their needs and preferences and while still combining these plans into an integrated view as our next generation business planning research found makes planning processes to work better. For another, it facilitates the integration of long-term and strategic plans and company budgets. Our research in strategic and long-range planning finds that two-thirds of those that have fully or mostly integrated the two types can respond to changes immediately or soon enough, compared to just 22 percent of companies that have little or no integration. The suite’s Disclosure Management offering was designed to help corporations manage the creation, editing and publication of external disclosure documents such as those required by the U.S. Securities and Exchange Commission or other regulatory bodies such as bank regulators in European countries for their Common Reporting (COREP)requirements. However, this capability is also useful for automating a range of report creation functions, especially documents for internal or external use that combine text and data. For instance, users can create Microsoft PowerPoint and Word templates with text, numbers and graphics that can be automatically updated with the latest period’s data. Using this capability accelerates production of reports while cutting the staff time required to produce them. It’s easy to achieve bullet-proof accuracy since the numbers in the tables are assembled from a single authoritative source, and references in the text to a specific item in a table (an absolute amount or a percentage change, for example) are always in agreement, even when there are last-minute changes. Thus it is relatively easy to put together a periodic PowerPoint presentation for the senior leadership team or board of directors. It’s especially handy for creating monthly, quarterly and annual reports because once designed, the numbers can be quickly updated to the latest period. I recommend that companies looking for a financial performance management suite – especially those that are replacing a point solution (such as obsolete financial consolidation software) or those moving away from spreadsheets – add Tagetik to their list of vendors to consider. Regards, Robert Kugel – SVP Research", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_fcc_shortening_financial_close_updated-1.png", "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": "Interactive Intelligence and Omnichannel Customer Engagement", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/14/interactive-intelligence-and-omnichannel-customer-engagement", "datePublished": "14/08/2015", "dateModified": "14/08/2015", "articleBody": "At the end of last year, I wrote about Interactive Intelligence’s release of a new service, PureCloud. It was the company’s first step into the multitenant cloud computing market, using Amazon Web Services and aimed at small-to-midsize contact centers. To help companies understand the different cloud-computing models, I provided answers to Interactive Intelligence questions on the advancements of these approaches for business. Since then the new CMO, Jeff Platón, has led the company’s overall change to focus on customer engagement, in particular in omnichannel management. From a product perspective, its strategy continues to be built around supporting three products: CIC, its established on-premises product; CaaS, its proven single-tenant cloud product; and PureCloud. The PureCloud platform provides a distributed cloud architecture, a common user interface and integration capabilities to a variety of third-party products. It is also the foundation of three other products: Collaborate, Communicate and Engage. Our research into next-generation customer engagement finds that one of the biggest challenges companies face in trying to improve customer engagement is the number of employees involved handling customer interactions. It also shows that in an effort to overcome this challenge, companies are planning to deploy collaboration and mobile applications. PureCloud Collaborate includes both. It provides real-time chat, videoconferencing and desktop sharing. It enables users to find people, documents and chat histories, and groups to share information and work together, for example in editing documents. By supporting mobile devices it allows users to carry out these tasks away from their desks. The core of PureCloud Communicate is an advanced IP-based PBX that supports auto-attendant (automated transfer of calls to a selected extension), call recording, speech recognition and unified messaging. Used in combination, Collaborate and Communicate enable employees at multiple location, including mobile ones, to work together on customer interactions. Interactive Intelligence recently announced general availability of PureCloud Engage, the third product running on the platform. Engage supports a range of capabilities including multichannel interaction routing, speech-enabled IVR, outbound campaign management, workforce optimization, CRM integration, reporting and data analytics. Each of these is still in early stages of development, but the company says that it plans to develop them quickly to support omnichannel customer engagement. As I have written on several occasions, achieving such a goal is not easy for enterprise companies and vendors alike. For enterprises it is a necessary goal because done properly it can deliver the kind of engagement consumers have come to expect. and as it develops, PureCloud should enable companies to meet those objectives. To give its customers the broadest choice for deployment, especially those that are skeptical about cloud computing, Interactive Intelligence must continue to support three product lines. CIC and CaaS are mature products with extensive capabilities, but they will need further development to keep up with market expectations. I believe that cloud-based services such as PureCloud are the future because they address key company objectives: more manageable costs, less demand for in-house, highly skilled IT resources, faster deployment, quicker delivery of business benefits and the ability to innovate in customer engagement. Innovation is, I believe, the most important because as the Internet of Things digitally enables more kinds of devices to be connected, it is likely to disrupt what have been stable processes – paying for goods and services, using credit cards, travel, self-service and engaging with companies. This in turn will drive greater convergence between marketing, sales and service. I therefore recommend organizations planning for this future should assess how Interactive Intelligence PureCloud can help in those efforts. Regards, Richard J. Snow VP & Research Director", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_ngce_research_06_changes_to_improve_engagement-20.png", "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": "Optimization Analytics Comes to the Mass Market", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/13/optimization-analytics-comes-to-the-mass-market", "datePublished": "13/08/2015", "dateModified": "13/08/2015", "articleBody": "Optimization is the application of algorithms to sets of data to guide executives and managers in making the best decisions. It’s a trending topic because using optimization technologies and techniques to better manage a variety of day-to-day business issues is becoming easier. I expect optimization, once the preserve of data scientists and operations research specialists will become mainstream in general purpose business analytics over the next five years. Optimization was first adopted by businesses in the middle of the 20th century, aided by the introduction of digital computers. The first technique that gained broad for a few specific purposes was linear programming, one of the most basic optimization methods. Linear programming enables analysts to quickly determine how to achieve the best outcome (such as maximum unit volume or lowest cost) in a given situation. They do so using a mathematical model that captures the key variables that go into the decision and any constraints that may affect that decision. A food processor, for instance, may use three types of cooking oil to make a product. To maximize its profit, the company needs to determine the exact proportions of the three oils that result in the lowest production cost. However, it can’t just choose the cheapest of the three in every case because for flavor and shelf-life requirements there’s a limit to the maximum percentage of each oil that it can use. Linear programming using the simplex algorithm quickly solves the problem. As computing capabilities became increasingly affordable, companies could use more complex algorithms to handle ever more difficult optimization problems. For instance, the airline industry used it to determine how best to route aircraft between two cities and to staff flight crews. Not only can software find the best solution for scheduling these assets in advance, it also can rapidly re-optimize the solution when weather or mechanical issues force a change in how aircraft and crews are deployed. Airlines were also in the vanguard in the 1980s when they started using revenue management techniques. In this case, the optimization process was designed to enable established airlines to compete against low-cost startups. Revenue management enabled the large carriers to offer low fares to price-sensitive but flexible vacationers without sacrificing the higher fares that the less flexible businesspeople were willing to spend. The same approach was adopted by hotels in pricing their rooms. Starting in the 1990s markdown management software, which I have written about gained ground. It enables retailers to make more intelligent pricing decisions by monitoring the velocity of purchases of specific items and adjusting prices to maximize revenue. To be feasible, each of these optimization problems require large data sets and sufficient raw computing power. We’re now on the cusp of “democratizing” what I call optimization analytics. Big data technologies are making it feasible and affordable for even midsize companies to work with much larger data sets than they have been able to in the past. Our benchmark research on big data analytics finds that about half of participating companies already use analytics with big data. This is partly the result of more powerful and affordable data processing resources but also because companies have invested in systems to automate many functions. The rich data sets created by these business applications provide corporations with the raw material for analysis. This data has the potential to enable businesses to make more intelligent decisions. From a practical standpoint, though, the value of these large data sets can only be realized by moving optimization analytics out of the exclusive realm of data scientists and into the hands of business analysts. These analysts are the ones who have a sufficient understanding of the business and the subtleties of the data to find useful and repeatable optimization opportunities. Three-fourths of companies in our research said that they need these business skills (“domain expertise”) to use big data analytics successfully. Optimization analytics is a breakthrough technology with the potential to improve business performance and create a competitive advantage. You can’t do optimization in your head, and it’s not feasible in desktop spreadsheets for anything but the most basic use cases, such as linear programming optimizations on relatively small data sets. This is a good reason for almost any company to consider adopting optimization software. Another reason why companies will find it attractive to apply optimization analytics broadly is that the results of applying optimization routines may be superior to using common rules of thumb or relying on instinct and experience. One of the most important lessons for executives about optimization analytics is that optimal solutions are sometimes (but – crucially – not always) counterintuitive to established norms. For instance, in markdown management, retailers often have found that smaller, more frequent price reductions maximize profits and produce a considerable improvement in sales over the end-of-the-season price slashing that was once considered to be the best practice. In financial services, charging your best customers more for loans and other services turns out to be the optimal choice for the bottom line of financial institutions. Another important insight from our collective experience with optimization is that while the value of these analytics as realized in a single event or transaction may be small, it can have a measurable impact on profitability and competitiveness when applied broadly in a business. At this point optimization analytics is in a dual mode. On the one hand, there are proven examples of the narrow application of optimization such as those mentioned above. On the other, bringing optimization analytics to the masses is only beginning. Some vendors have made progress in simplifying their analytics, but mainstream products are only on the horizon. It’s also important to recognize that, as with past breakthroughs in information technology, there are bound to be more duds than success stories in initial attempts at using optimization analytics. Experience suggests that a small number of companies that have strong analytical skills and a rigorous approach to managing company data will prove to be the leaders in finding profitable opportunities for applying optimization technologies and techniques. Others will do well to find these examples and consider how to apply them to their own organizations. Regards, Robert Kugel – SVP Research", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/vr_big_data_analytics_01_use_of_big_data_analytics-2.png", "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": "Data Preparation is Essential for Predictive Analytics", "mainEntityOfPage": "https://blog.isg-research.net/2015/08/06/data-preparation-is-essential-for-predictive-analytics", "datePublished": "06/08/2015", "dateModified": "06/08/2015", "articleBody": "Our research into next-generation predictive analytics shows that along with not having enough skilled resources, which I discussed in my previous analysis, the inability to readily access and integrate data is a primary reason for dissatisfaction with predictive analytics (in 62% of participating organizations). Furthermore, this area consumes the most time in the predictive analytics process: The research finds that preparing data for analysis (40%) and accessing data (22%) are the parts of the predictive analysis process that create the most challenges for organizations. To allow more time for actual analysis, organizations must work to improve their data-related processes. Organizations apply predictive analytics to many categories of information. Our research shows that the most common categories are customer (used by 50%), marketing (44%), product (43%), financial (40%) and sales (38%). Such information often has to be combined from various systems and enriched with information from new sources. Before users can apply predictive analytics to these blended data sets, the information must be put into a common form and represented as a normalized analytic data set. Unlike in data warehouse systems, which provide a single data source with a common format, today data is often located in a variety of systems that have different formats and data models. Much of the current challenge in accessing and integrating data comes from the need to include not only a variety of relational data sources but also less structured forms of data. Data that varies in both structures and sizes is commonly called big data. To deal with the challenge of storing and computing big data, organizations planning to use predictive analytics increasingly turn to big data technology. While flat files and relational databases on standard hardware, each cited by almost two-thirds (63%) of participants, are still the most commonly used tools for predictive analytics, more than half (52%) of organizations now use data warehouse appliances for predictive analytics, and 31 percent use in-memory databases, which the second-highest percentage (24%) plan to adopt in the next 12 to 24 months. Hadoop and NoSQL technologies lag in adoption, currently used by one in four organizations, but in the next 12 to 24 months an additional 29 percent intend to use Hadoop and 20 percent more will use other NoSQL approaches. Furthermore, more than one-quarter (26%) of organizations are evaluating Hadoop for use in predictive analytics, which is the most of any technology. Some organizations are considering moving from on-premises to cloud-based storage of data for predictive analytics; the most common reasons for doing so are to improve accessing data (for 49%) and preparing data for analysis (43%). This trend speaks to the increasing importance of cloud-based data sources as well as cloud-based tools that provide access to many information sources and provide predictive analytics. As organizations accumulate more data and need to apply predictive analytics in a scalable manner, we expect the need to access and use big data and cloud-based systems to increase. While big data systems can help handle the size and variety of data, they do not of themselves solve the challenges of data access and normalization. This is especially true for organizations that need to blend new data that resides in isolated systems. How to do this is critical for organizations to consider, especially in light of the people using predictive analytic system and their skills. There are three key considerations here. One is the user interface, the most common of which are spreadsheets (used by 48%), graphical workflow modeling tools (44%), integrated development environments (37%) and menu-driven modeling tools (35%). Second is the number of data sources to deal with and which are supported by the system; our research shows that four out of five of organizations need to access and integrate five or more data sources. The third consideration is which analytic languages and libraries to use and which are supported by the system; the research finds that Microsoft Excel, SQL, R, Java and Python are the most widely used for predictive analytics. Considering these three priorities both in terms of the resident skills, processes, current technology, and information sources that need to be accessed are crucial for delivering value to the organization with predictive analytics. While there has been an exponential increase in data available to use in predictive analytics as well as advances in integration technology, our research shows that data access and preparation are still the most challenging and time-consuming tasks in the predictive analytics process. Although technology for these tasks has improved, complexity of the data has increased through the emergence of different data types, large-scale data and cloud-based data sources. Organizations must pay special attention to how they choose predictive analytics tools that can give easy access to multiple diverse data sources including big data stores and provide capabilities for data blending and provisioning of analytic data sets. Without these capabilities, predictive analytics tools will fall short of expectations. 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ISG Software Research Analyst Perspectives