Taking Steps to Make Better Decisions with Big Data

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If only it were that easy. A recent article in Forbes presents “4 Steps to Turning Big Data into Business Impact.” Written in staccato fashion by Plyanka Jain, a consultant specializing in analytics, the piece falls into the general “How to” category characterized by articles such as “Five Steps to Flatter Abs” or that wikiHow classic, “How to Write a How to Article: 10 Steps.”

Jain is addressing executives who have been tasked with meeting ambitious growth targets mandated by their board of directors. Big Data holds the key to this growth. But, she asks the reader, despite an abundance of data in your organization, are you still at a loss as to how to use that data to understand the business drivers?

Enter Step 1 where she asks, “Introspection on your self and your leadership Team: Are you making evidence based decisions or are you gut-happy decision-maker?” (sic).

Your organization will not progress towards being data-driven, unless, you and your leadership team are asking the Three key questions of your data and your team,” Jain adds. They are: “(1) ‘How do we define our success?’ (2) ‘What drives our success?’ and (3) ‘Who are our customers, and how do we engage them?’ Whether you use zero-sum budgeting or other ways to hold your leadership team accountable to the decisions they make, there needs to be some accountability structure, because you can only manage what you measure. And as soon as you start looking back at decisions which were made, you start finding ways to optimize those decisions. And inarguably, there is no better way to optimize decisions, than basing it on data and facts.”

Good advice, but Step 1 also harbors the land mine that can destroy the whole four step process in an instant. As data scientist Thomas Thurston pointed out in a recent inside-BigData post, “… a lot of business decisions have to be made quickly. There isn’t time to build a predictive model or to even glance around for patterns…Relying on your wits is part of doing business. However if there are big problems that keep resurfacing, it’s a lot slower to go on guessing. If you don’t bring data science or some other form of rigor to the table, you may never get a grip on what the underlying problem is.”

So the underlying problem may really be the fact that as an executive you are more comfortable with a “gut-happy decision-maker” style of management, making snap judgements based on intuition and years of experience rather that a slow perusal of analytical data.

If you do happen to make it to Jain’s Step 2, you’ll find she recommends an investment in employees with well honed problem solving, analytical and managerial skills. Creating a robust data infrastructure is the call to action in Step 3; and Step 4 urges the reader to set up a transparent, formal decision making process.

If Jain’s how to article doesn’t solve your Big Data problems, she invites you to download a white paper, attend a half day data round table or, for a really immersive experience, attend her company’s Business and Testing workshop week in April.

Yes, the Forbes piece is blatantly a bit of marketing collateral for Aryng, Jain’s analytics training and consulting company. But if it helps move you from being a gut-happy decision maker to a manager who, without loosing the benefits of intuitive thinking, ¬ knows when to make decisions using all the tools that analytics and data science provide, it’s well worth the read.

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