Competition in the Era of Big Data Analytics

Much has been written on the explosion of data.  IBM has frequently mentioned that, “90% of the data in the world today has been created in the last two years alone” and that “80% of organizational data is unstructured.”

This surge in data generation is due to many reasons. Sensors are becoming cheaper and easier to use and devices are becoming more
intelligent and connected.  Also, businesses are starting to think of the data generated from their business processes as a source of value. But perhaps the most compelling reason is that it’s just now possible to store and analyze more data less expensively. Bill Franks did a good job discussing how companies are struggling with the amount of data (and finding value) in his blog.

But I’d like to discuss a different aspect of this ‘arms race.’ More decision are taking place in real-time and enterprises that can make good decisions faster will out-compete others. The key is that both must be done together. A poor decision done quickly is no better than a great decision done too late.

Imagine two companies with approximately equally effective analytics efforts. How might one of them improve their analytics results to gain a competitive advantage? Here are a few ideas:

  • Apply analytics to more decisions. A hot area right now is the application of big data and analytics to HR processes.
  • Improve the accuracy of current analytic models.  As I mentioned in my last blog post, machine learning models need to be continuously updated. There are many ways to improve the accuracy of analytics models; more/better data, better machine learning algorithms, even better understanding of the problem domain.
  • Speed up the application of analytics. This is an area that I don’t think gets enough attention. If the analytic model has proven accuracy and can be produced in a timely fashion, use it. I’ve heard of businesses spending resources developing a good model then continue to rely on HiPPO (Highest Paid Person’s Opinion) decision making.

I’d be interested in hearing suggestions about other ways to use analytics to gain a competitive advantage.

Michael Cavaretta is a Data Scientist and Manager at Ford Motor Company. He is a leader for the Predictive Analytics group in Research and Advanced Engineering.

Check out his previous posts and discussions.