Big Data & Analytics – A Transformational Journey

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Last month I had the good fortune to attend the Reverse Logistics Sustainability Council (RLSC) and Warranty Chain Management (WCM) conferences.   Big Data & Analytics was a topic that gained much prominence at both of these conferences.  Indeed, this is a subject that is gaining much attention in business and academic circles these days.  Interestingly, there is a general consensus among academics and industry thought leaders that Big Data Analytics is one of the most misunderstood and misused terms in the business world.  For some business professionals, the term analytics applies specifically to performance metrics, for others it has to do with unstructured data sets and data lakes, while still others think it relates to predicting the future.

Big Data refers to the volume, velocity, and variety of data that a company has at their disposal. Analytics applies to the discovery, interpretation, and communication of meaningful patterns in data.  The truth is that there are actually four (4) different types of Big Data Analytics that firms can rely on to make business decisions.

  • Descriptive Analytics: This type of Analytics answers the question “What is happening?”  In a field service organization (FSO) this may be as simple as KPIs like SLA compliance or First Time Fix rate.  The exact measurement tells an FSO how well it is doing when it comes to fixing problems right the first time and meeting customer obligations for response time.
  • Diagnostic Analytics: Understanding what is happening is important, but it is even more important to understand why something is happening.  This is how managers and executives can identify and resolve problems before they get out of hand. Diagnostic Analytics provides this level of insight, for example by pin-pointing why First Time Fix Rate is low.  Maybe it’s because the company is making poor decisions about which Field Engineers (FEs) are dispatched to the customers’ sites.  Or, perhaps selected Field Engineers do not have access to the right parts when they arrive on site and must return for a second visit.
  • Predictive Analytics: Ok, so now we know why something is happening. Wouldn’t it also be good to know what is likely to happen next?  Predictive Analytics provides this level of insight. In other words, it provides a forecast about what may happen if a company continues to experience a low first time fix rate.  For example, it could show the specific impact on customer satisfaction or the measurable effect on service costs and/or gross margins.   In this case, Predictive Analytics helps a company understand with a high level of statistical confidence how long it may continue to maintain the status quo before financial problems may arise.
  • Prescriptive Analytics: The final component of analytics is Prescriptive A This level of information helps a company understand at a granular level of certainty exactly what it should do to resolve a current situation and avoid future problems.  For example, Prescriptive Analytics may reveal that a company must ensure the field engineer has the right parts on hand prior to being dispatched to arriving at the customer site.  The Analytics can show which parts must be available and where they should be located.

In summary, Analytics takes the guesswork out of decision-making.  Instead of relying on intuition or prior experience, service executives can make sound business decisions based on objective analysis of data.  As a result, the probability of making the right decision increases.   Relying on Analytics to drive business decisions involves a transformational journey.  As innovative as it seems, a company cannot just start using Predictive or Prescriptive Analytics. It must first become proficient with Descriptive Analytics before it can leverage the power of more advanced analytic models.    This journey is not just about the data.  Many managers mistakenly believe that they must have enough of the right data to make Analytics work.  The truth is that we all have a wealth of data at our disposal.  Our challenge is finding the tools and technology to process the data, making Analytics a winning business proposition.  This begs the question: does your service organization have an optimal system in place to harness the power of Analytics?  If you are not certain, it may be time to conduct an audit and assessment of your infrastructure.  To learn more, schedule a free consultation today.

Please note: I reserve the right to delete comments that are offensive or off-topic.

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