Improving procurement through data and analytics -- GCN

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analytics </h3> <ul id="ph_pcontent2_0_ByAuthor" class="byline"> <li class="author">By Jarrod McAdoo</li><li class="date">Oct 06, 2020</li> </ul> <p>For more than a decade now, public-sector procurement teams have been hard at work developing and deploying analytical strategies. Much like procurement teams in the private sector, their goal is to wrest meaningful insights from mountains of data, improve their organization’s decision-making and drive value. In many cases, as their experience in using analytics matures, their depth of understanding and number of actionable insights yielded by the data also improves. Those improvements lead to upgraded category plans, better pricing evaluations and improved consumption pattern analyses as well as better awareness of supplier compliance, distribution channels and contract terms. </p> <p>There is certainly no shortage of material for procurement teams to analyze. Terabytes of data are being collected and retained every day. It comes in different forms and from an assortment of internal and external sources -- constituents, employees, carriers, retailers, websites, sales staff, data brokers, suppliers and more. There are several important caveats to the using the data, however. One, <a href="https://web.archive.org/web/20201007053535/https://www.accenture.com/us-en/blogs/intelligent-operations-blog/ruehle-detwiler-procurement-data-insights">according to Accenture</a>, is that most potentially valuable data is either inaccessible or unused because procurement teams -- particularly those whose analytics capabilities are not yet mature -- still struggle with how to turn it into actionable insights. </p> <div class="ad"> <div id="div-gpt-ad-BOX_C1-0005"> <script type="text/javascript"> googletag.cmd.push(function() { googletag.display('div-gpt-ad-BOX_C1-0005'); }); </script> <noscript> <a href="https://web.archive.org/web/20201007053535/https://pubads.g.doubleclick.net/gampad/jump?iu=/5978/eof.gcn&t=item%253d2f5ebf7f_9cb5_49a7_97bb_278a8da3952a%26pos%253dBOX_C1%26Topic%253dData_and_Analytics&sz=640x481| 1x1| 300x250&tile=5&c=123456789" target="_blank"> <img src="https://web.archive.org/web/20201007053535im_/https://pubads.g.doubleclick.net/gampad/ad?iu=/5978/eof.gcn&t=item%253d2f5ebf7f_9cb5_49a7_97bb_278a8da3952a%26pos%253dBOX_C1%26Topic%253dData_and_Analytics&sz=640x481| 1x1| 300x250&tile=5&c=123456789" border="0" alt=""/> </a> </noscript> </div> </div> <br/> <p><strong>Garbage in…</strong></p> <p>However, another caveat is more vexing. It’s that a huge amount of the data which is readily available is simply no good. It’s what Deloitte calls “<a href="https://web.archive.org/web/20201007053535/https://www2.deloitte.com/us/en/insights/deloitte-review/issue-21/analytics-bad-data-quality.html">big bad data</a>.” In a survey of 107 professionals, the company asked respondents to anonymously review their own profiles as compiled by a leading data broker. More than two-thirds found that over half the information in their profiles, which included basic demographics as well as things like life insurance, vehicle ownership and number of children, was just plain wrong. </p> <p>But inaccuracy doesn’t just apply to consumer data. Poor data about suppliers is rampant. A <a href="https://web.archive.org/web/20201007053535/https://www.dcvelocity.com/articles/46095-survey-poor-supplier-data-is-hurting-procurement-and-sourcing-organizations">survey</a> by the big data firm Tealbook found that bad supplier data is causing a staggering number of issues for procurement organizations including project delays, unhappy clients and serious financial loss. According to <a href="https://web.archive.org/web/20201007053535/https://tealbook.com/severe-business-consequences-stem-from-poor-supplier-data/">The Data Warehousing Institute</a>, in the U.S. alone, bad, dirty, inaccurate or missing data is estimated to cost companies over $600 billion a year. Without a clear data management strategy or a deep appreciation for data governance, TDWI said, no company can avoid the repercussions of bad data. Unless that issue is addressed, its impact will only increase as bad data propagates across systems, data quality continues deteriorating and the cost of cleansing complex data spirals out of control.</p> <p>Various approaches have been devised to identify suspect information and cleanse data of its most egregious errors. However, having a procurement staff that’s vigilant to the possibilities of mistakes as well as to outright misrepresentation is key to applying them. Agencies should consciously work to create a culture that encourages individuals to -- instead of assuming that data handed to them is truthful -- look into that data critically, look for discrepancies, seek insights and ultimately use the right data to innovate and improve decision making. It’s a tall order, and it requires both money and effort to do it well.</p> <p>That’s because with data and analytics, the value received is a direct reflection of the investment put into transforming processes and acquiring new technology. Manual processes, legacy software, disconnected systems and other technology constraints limit real-time decision support and diminish the support required for a shift to modern procurement. However, there is a path forward that provides government buyers with more adaptable and flexible procurement solutions, even in a time of austerity.</p> <p><strong>Contemporary data-driven processes</strong></p> <p>Among the public-sector agencies that have made the commitment to modernizing their procurement processes, data and analytics remain a top priority. A modern procurement solution can satisfy not only the needs of public procurement professionals, it can also address the diverse needs of their suppliers and constituents. But what, exactly, is a modern, data-driven procurement process and what can it do for agencies? Some of its defining capabilities include:</p> <ul> <li>Enabling intelligent workflow, reducing errors and decreasing redundant work.</li> <li>Reducing the cost per transaction, shortening procurement cycle times and eliminating needless complexity in processes that impede performance and introduce risk through increased automation.</li> <li>Enhancing collaboration and increasing information sharing among departments to reduce costs and increase efficiencies.</li> <li>Increasing transparency by presenting data contextually to provide relevant insights and improve decision-making for all users.</li> </ul> <p>While collecting and analyzing data are critical to improving the outcomes of public procurement processes, agencies must first formulate a master data management strategy whose goal should be to help employees access and analyze the right data. It should involve data cleansing, integrating data sources, eliminating data silos, analyzing large datasets and sharing both data and insights with the supply chain network. 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