Focus on the Business Question, not the Technology

This post is part of a series of interviews with experts in business intelligence, sales management, marketing, customer retention, management and strategic planning.  Everyday, the CAN team interacts clients, mentors, and friends who are leaders in their fields, and we started this series to share their expertise.
Corporate business intelligence has hit a roadblock, according to Cameron Ludwig, the Director of Analytics at BlueCross BlueShield of Nebraska. “As a discipline, we have been more enamored about what we can do, and not what we should do”.  Business intelligence of tomorrow needs to put less focus on technical capabilities, and instead, emphasize designing solutions that focus on answering essential business questions.  This need for a shift in focus is due to the exponential increases in data availability and the increasing reliance of executives on data in their decision making.  For example, in a recent study by McKinsey there is a projected 40% growth rate in the amount of new data generated per year, with many companies having hundreds of terabytes of data (link).  As a discipline, business intelligence  has matured to the point where we need to move beyond collecting and displaying of data.  It is time to shift to the next level.
“Now the knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information.”- Peter Drucker, 2005
In order for BI to make the transition from what is technically possible, what we can do, toward what is valued by business, what we should do, requires a shift in focus for the emerging field of data science.  Although I am hesitant to say that data scientists should study business at the exclusion of technology, this shift requires that data scientists become students of business as opposed to technology.  That is, their greatest value comes from studying technology to the point of knowing what is possible and how to apply technology to meet the needs of their end users.  For example when a contractor builds a house, he doesn’t study the hammer, he studies architectural plans and creates a finished product from raw materials.  The same goes for data scientists; they should focus on understanding the problems that need to be solved, then spend time studying how to use raw materials (data) to create a valuable finished product.
Keeping with the need for a shift in the field of business intelligence from technology to application, the valuable finished product is not a dashboard displaying metrics, but rather actionable intelligence focused on answering the business questions of the end user.  This renewed focus of business intelligence requires that BI only provides decision makers with what is essential to answer their questions.  All the slick user-interfaces, gauges and dials of flashy dashboards will never provide as much value as the algorithm behind an executive report that integrates ten different historical and environmental variables to advise which projects to bid on, including anticipated profit margins.
Tremendous value exists in the proper application of data science, but the maximum value comes from a deep understanding of the needs and objectives of the end user.  Ensuring that the end product fits the end user requires the right feedback, and at least as much criticism as creation.  When self- and peer-reviewing their work, Cameron recommends that data scientists should be required to justify the existence of each sentence, idea, graph, and model.  This requires each BI report to be designed with simplicity in mind, but also maximizes value to the end user and builds trust in BI by focusing only on that which contributes to solving the problem at hand.  If an artifact, tool or feature cannot be defended, it is most likely of little value and should be eliminated.  In order for business intelligence to contribute maximum value to the organization, every element of business intelligence must justify its existence.
“Capital importance of criticism in the work of creation itself.  Probably, indeed, the larger part of the labour of an author (programmer) in composing his work is critical labour; the labour of sifting, combining, constructing, expunging, correcting, testing: this frightful toil is as much critical as creative”- T.S. Eliot


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