For Better and Worse, Big Data Grows Up

Tom Davenport, IIA Research Director

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.”

Analytics Matters!


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.

If You’re Not Working In Analytics Yet, You Will Be

IIA CEO Jack Phillips

The way companies do business is transforming. Our economy is already shifting. Soon, every department and nearly every role will incorporate analytics to keep up with the competition or get ahead. If you’re not working in analytics yet, you will be.

Handfield’s Supply Chain Analytics Predictions for 2014

IIA Faculty Member Rob Handfield

2013 has certainly been an interesting year, and as it comes to a close, here are some of my thoughts regarding the supply chain trends we are likely to see emerge in 2014. Not surprisingly, analytics is at the top of the list. Here is what I expect to see next year:

Perfect Information Doesn’t Equal Perfect Predictions


Many organizations attempt to achieve “data nirvana” by having 100% complete information for any given business decision. In the customer analytics space, this is sometimes referred to as a “360 degree view of the customer.” However, we really never know everything about our customers.

Our Predictions in Analytics for 2014

Sarah Gates, IIA's VP of Research

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.

How to Get More Value Out of Your Data Analysts


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.

The Countdown to Predictions Has Begun

Sarah Gates, IIA's VP of Research

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.

Industrial-Strength Analytics With Machine Learning

Tom Davenport, IIA Research Director

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.

The Massive Implications of Supply Chain Analytics

IIA Faculty Member Rob Handfield

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.