Big data is maturing and developing more value to large companies. So what’s the downside? The issue is that with this grown-up resource comes grown-up responsibilities. The phrase “governance for big data” is coming up more and more in large organizations. Others include “stewardship” and “control.”
As we enter 2014, we are in the middle of a fundamental transformation in the way businesses view analytics. Analytics are now seen as core to a business. Analytics matters. We are just starting to see analytics used as the basis for new products and revenue streams. The breadth of decisions analytics support is increasing every day. The next few years are going to provide a lot to blog about and I am looking forward to it.
A huge global audience of analytics professionals joined us live for the unveiling of the 2014 IIA Analytics Predictions, to hear our faculty talk about what is in store for the new year. I personally found our conversations about predictions relating to automating and operationalizing analytics to be fascinating. We not only expect to see more use of these techniques, but we also expect to see organizations starting to bump up against the balance point of where we end up “over automating” our business processes.
Organizations succeed with analytics only when good data and insightful models are put to regular and productive use by business people in their decisions and their work. We don’t declare victory when a great model or application is developed – only when it’s being used to improve business performance and create new value.
Just like our children or grandchildren, anxiously awaiting that big day when they get to open holiday presents, we here at IIA are in the final countdown to the release of our 2014 Analytics Predictions. It is the time when our faculty gather round the virtual crystal ball and share their insights into where the world of analytics is headed in the next year.
A short while ago I wrote a post suggesting that human analysts would not be disappearing anytime soon. As important as hiring good analytical people, however, is taking advantage of all of the current possibilities for improving their productivity in analytical work. Machine learning tools and platforms are the most promising approach to creating analytical models at the pace required by big data.
We recently had a senior data analytics professional from a large financial services organization speak in my MBA Supply Chain relationships class. The scope of the datasets his team was working on was limited to supply management, but the sheer volume of data was staggering in its complexity and fragmentation.