Tuesday, January 3, 2012

The Data and Analytics Catch-22

It goes without saying that without data, analytics can't exist.  As such, most organizations invest in data collection systems (ERP, CRM, HRIS, etc), and then get into the business of analyzing the data collected.

Although the main role of these systems is to manage and automate operational business functions, the real value of the data collected is with the analytic insights that they generate.  The problem is that oftentimes analytic needs don't play a major role in these enterprise system implementations.  Compounding this issue is that there is a lag between the time when a system is implemented, and when data actually become available (collected) to analyze.  The result of all this is that analytics can become an afterthought or not practiced at all.

The alternative to this approach is to lead the implementation of a data collection system with analytics.  But how the heck are you supposed to analyze data without a data collection system to collect data?  This is the catch-22.

The reality is that prior to ERP, CRM, HRIS, etc implementations, most organizations are capturing related data in some way (be it spreadsheets, local databases, legacy systems or *gasp* paper records).  These "data systems" while perhaps not elegant, still contain the potential for analytic insights.  What's more is that if analytics, driven by business goals and processes, are practiced prior to enterprise system implementations, then they can sort out what data are really strategic or business critical in addition to operational or regulatory requirements.

The misperception is that big, robust data systems need to be in place before any analysis can happen.  This is just not true.

The message here is that a practice of analytics with available data, ahead of an enterprise system implementation, can lead to a much more informed and productive implementation (among other things).  The key word here is "practice".  Analytics should not be a one time activity or administered only towards a discrete goal (like an enterprise system implementation).  Instead it should be an ongoing effort, employed at all levels of an organization, and across business functions, with whatever data might be available (be they spreadsheets or ERP systems).

Affordable analytic technologies exist today that make it easy to pull data in from these rudimentary and disparate data sources, and blend them together to generate analytic insights that address specific business goals and needs.  The practice of analytics not only enables these insights, but also informs potential investment decisions related to enterprise data systems.

So before you think about that new ERP system, think again!  You might be better served first practicing analytics with data you have right now!