blog: Where’s the beef?

Richard Sharpe Analytics & Big Data

 

With all of the attention being given to analytics and big data, the question that I often hear is:

“How are companies really solving specific business problems using analytics and big data?  Talk to me about the real and sustainable value that companies are finding by investing in analytics and big data capabilities.”

To coin a phrase made popular years ago by a fast food chain Where’s the beef?”

 

In earlier blog postings, I explained how analytics, coupled with accurate and specific data, can be used to facilitate cross functional decision making that is based on facts, not allocations, estimates or opinions.  Moving beyond “siloed’ data and decision making processes can create a real advantage in driving increased financial performance.  This advantage becomes a competitive differentiation that is based on factual insights across the entire enterprise versus departmental decisions that are based on isolated operating metrics.

Navigating in today’s complex business environments requires the ability to make smarter, pinpointed decisions.  The myriad of systems with segregated data often make it difficult to have the right information that is needed.  Better decisions are driven by having complete visibility across the enterprise.  With that visibility, very specific strategies by region, individual customers, product categories, SKU’s or channels can be tested, deployed and monitored.  This avoids the trap of trying to force a ‘one size fits all’ policy decision based on incomplete data both before and after the decision.

So assume for a moment that your company has defined very specific profit improvement goals that are not being met.  You have a cross functional team assembled.  You have all of the data you need, not just from one system but from EVERY system.  The data is accurate and complete.  There is no organizational anxiety about its source or validity.  With the proper analytics and data, you would want to simply and quickly answer the following questions:

  • What are the specific and detailed facts that offer insights regarding this current lack of performance?  (Descriptive Analytics)
  • Considering the end to end operation, what caused this performance to happen, i.e. the root cause that drove this performance? (Diagnostic Analytics)
  • What would be the financial impact if we make these very specific cross functional changes? (Predictive Analytics)
  • What other options do we have that the team has not considered or that are too complicated to completely define currently?  (Prescriptive Analytics)

It might be utopia for your company, but not for companies that have successfully developed Integrated Business Planning (IBP) solutions through the proper alignment of analytical and data capabilities.  Having this organizational capability provides immediate and sustainable value.  Value that drives competitive advantage by showing “Here’s the beef”!

I 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.