Big Data & Analytics Heroes

James Taylor

CEO of Decision Management Solutions
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"Moving from insight to action is absolutely critical. This means integrating analytics into business processes."

This week’s IBM Big Data & Analytics hero, James Taylor, CEO of Decision Management Solutions, says that too many projects “start with the data [and] then hope that this will improve the business.”  Taylor advocates for businesses to “begin with the decision in mind.”

How are big data and analytics changing business strategy?

More and more organizations see becoming analytic (putting big data to work) as a critical element of their business strategy. They are aiming to make every decision analytically, looking for data that can help them make a better decision every time. Increasingly this is becoming pervasive—not just a strategy for the few, but an across-the-board operational requirement. This means that an organization’s analytic strategy must be merged with its operational strategy, and that its technology strategy is central to both.

What are the biggest challenges with getting started with big data and analytics?

The biggest challenge we see is correctly framing the problem so that big data and analytics can then be applied effectively. Too many projects start with the data, develop various analytics and then hope that this will improve the business. Instead, they need to frame a business problem (a business decision that needs to be improved) and then see how analytics might be used to improve that decision. Only then can they see what data will be required and put a big data analytics project into a business context. A “begin with the decision in mind” approach combined with models of decision-making frames the problem and ensures business value will result. It also allows non-analytic participants to play an active role in laying out the problem.

When it comes to big data and ­analytics really delivering on ROI, what is the market still missing?

A focus on improving decisions is necessary, not just on knowing things. Too many analytic projects declare themselves a success because they generated insight or told people something they did not know. But if the organization did not change its decision-making as a result, then it is not going to get any value from this insight. Moving from insight to action is absolutely critical. This means integrating analytics into business processes and systems by identifying the decisions that will be made differently because of the new analytic insight. Many analytic teams think they are done when they have the analytic insight developed instead of when the business is running better, and this has to change.

Should tomorrow's generation acquire analytic skills no matter the degree?

Everyone should acquire the skills they need to use analytics: an understanding of the difference between correlation and causation, an awareness of the importance of a control group and of experimentation and a feel for what is (and is not) statistically significant. I don’t think everyone needs “analytic skills,” however the level of automation and support for analytical decision-making is rising rapidly. Just as not everyone had to become a Java programmer or a COBOL programmer (or before that a telephone operator or typist), not everyone will have to become a data scientist. They will have to be able to take advantage of analytics though, and that means developing a feel for analytical decision-making.

Do you think big data and analytics will be able to handle the data growth over the next 10-15 years, or will we need another shift in technology?

I think they will handle the data volume OK. I am more concerned that they won’t handle the shift to truly pervasive analytics as well. Too much focus is given to how to help a knowledgeable decision-maker use analytics and not enough is given to embedding analytical decision-making into the systems we use every day. Greater data volumes will make it ever more important to embed decision-making in our IT infrastructure rather than simply pushing data and analytics to human users. This is an organizational and cultural challenge rather than a technical one.

Move from insight to action