Once Again, Data Integration Proves Critical to Data Analytics

When it comes to cloud-based data analytics, a recent study by Ventana Research (as found in Loraine Lawson’s recent blog post) provides a few interesting data points.  The study reveals that 40 percent of respondents cited lowered costs as a top benefit, improved efficiency was a close second at 39 percent, and better communication and knowledge sharing also ranked highly at 34 percent.

Ventana Research also found that organizations cite a unique and more complex reason to avoid cloud analytics and BI.  Legacy integration work can be a major hindrance, particularly when BI tools are already integrated with other applications.  In other words, it’s the same old story:

You can’t make sense of data that you can’t see.

Data Integration Proves Critical to Data Analytics
Data Integration is Critical to Data Analytics
The ability to deal with existing legacy systems when moving to concepts such as big data or cloud-based analytics is critical to the success of any enterprise data analytics strategy.  However, most enterprises don’t focus on data integration as much as they should, and hope that they can solve the problems using ad-hoc approaches.

These approaches rarely work as well a they should, if at all.  Thus, any investment made in data analytics technology is often diminished because the BI tools or applications that leverage analytics can’t see all of the relevant data.  As a result, only part of the story is told by the available data, and those who leverage data analytics don’t rely on the information, and that means failure.

What’s frustrating to me about this issue is that the problem is easily solved.  Those in the enterprise charged with standing up data analytics should put a plan in place to integrate new and legacy systems.  As part of that plan, there should be a common understanding around business concepts/entities of a customer, sale, inventory, etc., and all of the data related to these concepts/entities must be visible to the data analytics engines and tools.  This requires a data integration strategy, and technology.

As enterprises embark on a new day of more advanced and valuable data analytics technology, largely built upon the cloud and big data, the data integration strategy should be systemic.  This means mapping a path for the data from the source legacy systems, to the views that the data analytics systems should include.  What’s more, this data should be in real operational time because data analytics loses value as the data becomes older and out-of-date.  We operate a in a real-time world now.

So, the work ahead requires planning to occur at both the conceptual and physical levels to define how data analytics will work for your enterprise.  This includes what you need to see, when you need to see it, and then mapping a path for the data back to the business-critical and, typically, legacy systems.  Data integration should be first and foremost when planning the strategy, technology, and deployments.