The real question is, what are we doing about it?

The buzzword may be getting tired… I now meet it with anticipation of yet another enterprise business analytics/data software provider about to “wow” me with their transformational definition of big data. This is naturally followed by a spiel about how their system can auto-magically improve the effectiveness of an organization’s marketing efforts, bringing them a vast amount of new analytics insight. In many cases these systems can do this, but it’s not “magic”, it’s much more complex than that and organizations need to be aware of the resources and cultural shift needed for the full potential of big data predictive modelling and analytics to be realized.

Here’s the thing that gets forgotten when these data systems are pitched… big data is still just data. Sure there’s more of it now than there ever has been in the past, which presents a big opportunity, and I think most of us get that. Regardless of how many zeros are in the number of terabytes, petabytes, [you name the measure]bytes, it’s still just data, the same stuff we’ve been working with for years. There may be some new stuff, but the principle is the same. Quality data is gold. It empowers us to make better business decisions and measure the effectiveness of what we’re executing.

The “Big Data” buzzword seems to bring about a sense of panic among marketers, IT, and the C-level alike. It’s seen as big and scary, and we’re all terrified that we’re going to get left behind or run out of data storage or worse. That panic may be what is driving some organizations to make significant investments in data intelligence software without first ensuring there is a plan and strategy in place. The problem with this knee jerk reaction is that the software systems are the tools to enable them, not the answer to their big data problems.

Data is the raw material in the software scenario

Let’s not forget about the things that we’ve known since before data was “big”. You can invest substantial amounts of money into the most sophisticated software systems, but the insight and value you get out of them will be limited to the value of the data that backs it and the skills and insights of your analysts. By no means am I advising against investing in these systems, but don’t think you are going to write a big cheque then sit back and watch the magic happen. Prepare by reviewing the data you’re capturing to find opportunities for improving the quality of the data and making sure you have the right people to use the software tools to the fullest potential.

Next week, in Part II of this post on Big Data, I will discuss how marketers can use the data they have to create truly insightful metrics. Stay Tuned!