Years ago, in the dark ages of IT, the gold standard of decision-making was to rely on hard lessons learned and good, old-fashioned gut instinct, honed by those lessons. However, over the past decade or so, big data has changed that standard. What’s more, big data is only getting bigger and more complex.

The result? An enormous imperative for companies—no matter their size or business—to become “data driven.” Data-driven companies invest in ever-more sophisticated technology to collect and analyze massive amounts of data, thinking if they have enough data, and develop cutting-edge predictive and prescriptive tools to slice and dice that data, they can solve even their most intractable problems. Yet, many still don’t have the answers they seek.

Has the data pendulum swung too far? Data is good, and big data is better. However, without the ability to interpret data in its proper context, that data is virtually meaningless—or at least its meaning remains locked somewhere in the terabytes.

Companies are organic entities made up of living beings with unique experiences and symbiotic relationships. Each company is different from its competitors on every level. Without the experiential context that surrounds and informs the numbers produced by analytics tools, many companies have trouble unlocking the meaning in their mountains of data to make fully intelligent decisions.

Making intelligent decisions requires more than data. It requires designing intelligent information systems that couple the knowledge diffused throughout the living, breathing organisms we call companies with the hard data from information systems. Further, it entails interpreting that data within its specific context by using the experience, emotions, and instincts of decision makers to produce a holistic picture of organizational performance and develop strategies to improve on that performance.

But how do we design analytics systems like that? How do we know what decision-makers need? True to Ockham’s Razor, the simplest answer is also the correct one. We ask them, and we listen to their answers.

We learn from successful B2C companies who just “get” customer service and apply those lessons in developing analytics applications for internal customers (decision-makers and knowledge workers). We sample the margins, i.e., we look for opportunities and answers that many companies may miss by simply looking at the mushy middle of processes and events. Finally, we unleash innovation throughout the organization by looking for people who are innovating at the departmental level—even if it doesn’t follow procedure—and piloting those innovations in other departments and business areas.

In short, we let people tell us what they need to improve their performance–and thus the performance of the business as a whole–and we give them the technology, along with the latitude to use their experience and instinct, to go out and make those intelligent decisions that spur innovation and grow the bottom line.

This process–used by leading companies like Apple, IBM, Fidelity, Intuit, and many others–is called design thinking. Design thinking matches peoples’ needs with solutions that solve the toughest problems and add business value. In my next post, I’ll delve more deeply into what design thinking is, and how it’s used to develop people-centered analytics applications.  Stay tuned.

 

General manager of Teradata Consulting and Go-To-Market Analytic Solutions. Thought leader in analytics, business intelligence, big data, and business transformation. My passion is helping my clients drive value through data and achieve a sustainable competitive advantage.

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