Predictive Analytics: Data Is Useless Until It’s Put into Action

Almost everything we do today leaves a digital trail. When we buy items online or interact with our friends on social media, we are generating data. Companies keep track of every communication with their clients, whether it’s their purchases, website activity or online interactions.

This collection of “big data” has some problems, including that it’s just too big (vast). Organizations believe that data is a valuable commodity, so they collect as much as they can. But that leads to data overload, not to mention the cost of storing all that data, the fact that it has a limited lifespan, and many organizations don’t know what to do with it.

The fact is, data is useless. On its own, it has no value.  In fact, the Scottish poet Andrew Lang once criticized politicians by saying “They use statistics as a drunken man uses lampposts—for support rather than illumination.” It’s an apt metaphor for how many organizations are using their data. They look to it to support decisions that they have already made. To use your data well, you must use it to drive and direct your actions in the future rather than to justify what you’re already doing.

Big data can be overwhelming. As I recently said in an interview with CIO Magazine on getting started in predictive analytics, data must be distilled into actionable strategies that can generate incremental revenue. Here are a few simple steps you can use to get started.

Step #1: Include More Viewpoints

None of the actions below will be helpful if you don’t have participation and buy-in from your entire organization. Avoid working in silos. From the very beginning, when you’re asking the specific questions that will move your company forward, make sure you are including all your stakeholders.

Step #2: Ask Specific Questions

There’s a lot of data out there, but you don’t need it all. Think small. Clearly define your business goals, then ask specific questions that will help you measure the things that will get you specific, actionable data.

Step #3: Be Open to Real Change

Will your company’s leadership team be willing to make changes to current products, processes, and workflows if the data indicates that change is necessary?  Will they be able to make the needed changes?  If the answer to either question is no, it probably isn’t worth asking the question (collecting the data).

Step #4: Embrace Creative Segmentation

Grouping/Segmenting your customers and/or data will allow you to take better advantage of your data. Do older customers behave differently from younger ones? Do a certain number of visits to your website trigger a purchase?  What factors drive greater customer loyalty? In Machine Learning, this step is what our team calls “domain-driven feature engineering.” If done well, this step helps you boost your analytics performance and create the differentiator or competitive advantage for your product.

Companies must realize that predictive analytics demands continuous reinvestment and periodic review and validation of applications, models, and business assumptions and realities.

It’s Worth It

Predictive analytics is a journey worth taking. Data can be valuable, but it only has value if you know how to make it work for you. If you know how to do this, it’s priceless.

Ye Zhang is Katabat’s Chief Technology Officer and co-founded the company in 2006. He is an avid technologist and has published multiple conference and journal articles in the fields of computer vision, pattern recognition, and artificial intelligence.  Want to talk about big data, artificial intelligence, or predictive models? Feel free to contact me at

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Ye cofounded Katabat in 2006 and continues to enjoy creating technology solutions to solve business problems. Ye’s deep experience in artificial intelligence, banking and internet technologies have and continue to shape Katabat’s product development and evolution. Prior to Katabat, Ye worked for Bridgeforce and Ensuredmail, Inc. Ye received a BS and Master’s degree in Electrical and Electronics Engineering from Sichuan University. He also has his Master’s and Ph.D. in Computer Science from the University of Delaware. Ye is an avid technologist and has published multiple conference and journal articles in the fields of computer vision, pattern recognition, and artificial intelligence.

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