blog: How Can Big Data Drive Better Decisions?

Richard Sharpe Analytics & Big Data

 

I just recently returned from the CSCMP Global Conference. While at the event, I had a number of Big Data conversations were people were asking questions like;

“Is it possible today to use Big Data to support meaningful business decisions?”

I walked away from the conference thinking that my next blog posting needed to further address this question.

You probably have heard of Big Data being used with the four following terms; Descriptive, Prescriptive, Predictive and Cognitive Analytics. The terms can be a little confusing. Each of these forms of analytics have been around for a long time but with the incorporation of Big Data, they each offer tremendous new opportunities to drive significant value to making better informed business decisions. Let’s explore each in the context of Big Data and improved decision support.

Descriptive Analytics – as the name implies, the focus is on describing historical performance. Nothing new about this you might say. But with Big Data, the level of performance information can be very specific by product, customer, channel, supplier and other key operational areas of focus. What is an example of this? At the Gartner Supply Chain Conference in Phoenix this spring, it was offered that companies will need to evolve from using a “standard cost to serve model” to a “total cost to serve model”. To accomplish this, a company must take a far more comprehensive approach that includes all direct and indirect costs associated with fulfilling each customer order. This requires validated data to be organized and applied in a repeatable process in order for the organization to have confidence in the information and to make it actionable. There are many more examples of how Descriptive Analytics, using Big Data, has the opportunity to identify immediate revenue and cost opportunities that are not visible with aggregated or disconnected information.

Prescriptive Analytics – this type of analysis focuses on defining different ways that you may operate your supply chain given certain assumptions, business rules and anticipated customer demands. A very common form of Prescriptive Analytics is in the use of network or inventory optimization applications. However, as we all know, any form of analysis is only as good as the quality of the data that is being used. Big Data’s contribution to putting Prescriptive Analytics on steroids is that companies no longer have to settle on what data they can assemble to support the work. By mastering Big Data, organizations can now have complete and accurate data to be creative in intelligently organizing information to meet the exact needs of the analysis. This, in turn, provides a more robust and accurate prescriptive business solution.

Predictive Analytics – this one is easy. Everyone has some form of forecast for the future. For most companies, forecasted customer demands play a primary role in their S&OP meetings. So how does Big Data support Predictive capabilities? The answer can be found in three ways. First, as noted above, getting everyone on the same page with regard to exactly what did happen provides for a “fact” versus “opinion based conversation. This has an obvious and very positive impact on discussing the future. Second, Big Data can also provide insights regarding more accurate trending and patterns because it is more specific and accurate than higher level or “silos” of operational data. Third, utilizing this information in predictive analytical tools like simulation applications provides a more accurate way to analyze the impact of specific decisions on future operating performance.

Cognitive Analytics – this fourth area of decision support also offers amazing new opportunities to positively impact financial performance. Having one source of data that has been created from all relevant structured and unstructured data sources provides a detailed picture consisting of operational patterns , trends and informational facts that can be linked together to provide valuable insights. But with advances in Cognitive Analytic capabilities, machine driven learnings that get smarter over time, this form of analytics has the potential to further drive innovation and competitive advantage. Blending the intelligence of the people who know the operation with the power of increased machine driven operational insights will be a big part of how companies will take additional advantage of Big Data. Although early in its development, Cognitive Analytics will be quickly adopted by those companies who have mastered the use of Big Data in their Descriptive, Prescriptive and Predictive analysis activities.

So, in order to circle back to the original question posed at the beginning of this posting;

“Is it possible today to use Big Data to support meaningful business decisions?”

The answer is yes. Big Data’s contributions to informed business decisions will only exponentially increase as we learn how to harness the insights to be gained. The real question is “How can your company incrementally use Big Data in your decision support processes?” Let me offer the following quote:

“I never guess. It is a capital mistake to theorize before one has the data. Insensibly one begins to twist the facts to suit theories instead of theories to suit facts.”
Sir Arthur Conan Doyle, Author of Sherlock Holmes stories

We would appreciate hearing your thoughts and comments. All the best, Richard

profits and competitive advantage.  You could say; what’s new about a focus on profit, we do that every day?  Let’s explore linking Big Data and corporate profitability a little more.

In a recent on-line video, I was asked, “Why is Big Data such a big deal?”  I answered the question using the following scenario:

A consumer walks into a store to buy a coffee maker for his wife as an anniversary gift.  He scans the barcode for the model she wants and enters it into an app on his mobile device to see if he can buy it cheaper through another retailer.  The consumer sees that just a half mile away he can purchase the same item for 5 dollars less than the price he sees in this store.  Now, one of the retailers has set the price of the product based on a corporate margin calculation.  The second retailer knows the exact margin of that product for that store which takes into account all of that SKU’s transactional costs including the change in their fuel surcharges that were incurred two weeks ago.

Which retailer is going to win?

Companies are beginning to focus on harnessing Big Data but there is a lot of confusion regarding the “WHY”.  The attached article offers information from a Gartner survey on that very point: https://readwrite.com/2013/09/18/gartner-on-big-data-everyones-doing-it-no-one-knows-why#awesm=~ohUJBlny4JeJN4 

In my opinion, learning how to repurpose fragmented data (data that is captured within and outside of your supply chain operation) into actionable insights will be the key to innovation and future success.  Success measured by increasing revenue growth and operational efficiencies and therefore sustained increases in profit and market share.  This is further emphasized by the following quote:

“Innovation is the ability to convert ideas into invoices.” – L. Duncan

Companies that take Big Data seriously will maintain significant market advantage.  In fact, it has been suggested that the processing power of the cloud coupled with Big Data best practices will create “Disruptive” solutions to the status quo.  This is much bigger than just adding technical capabilities to process large amounts of data or to connect two different data sources together to conduct data mining.  Big Data has the power to transform the way companies will succeed in meeting shareholders’ expectations.

We would appreciate hearing your thoughts and comments.  All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.