I spend a lot of time helping organizations to “think like a data scientist.” My book “Big Data MBA: Driving Business Strategies with Data Science” has several chapters devoted to helping business leaders to embrace the power of data scientist thinking. My Big Data MBA class at the University of San Francisco School of Management focuses on teaching tomorrow’s business executives the power of analytics and data science to optimize key business processes, uncover new monetization opportunities and create a more compelling, engaging customer and channel engagement.
However in working with our data science teams, I have come to realize that we also need to address the other side of the data science equation; that we need to teach the data scientists in order for them to think like business executives. If the data science team cannot present the analytic results in a way that is relevant and meaningful to the business (so that it is clear what actions the business leaders need to take), then why bother.
In order to engagement more effectively with the business users, here are a couple of key points that the data science team needs to understand as they conduct their analytics:
The data science team needs to understand thoroughly the key decisions that the business users are trying to make. Then, the data science team can present where and how the analytic results can help the business users make better decisions.
As part of ensuring that the analytic results are relevant and meaningful to the business, it is also critical to tie the analytic results back to the organization’s key financial or business drivers. Figure 1 shows an example of linking the analytics to the organization’s key financial and business drivers around the following business decision:
Which customers should receive which promotional offers?
The Harvey Balls in Figure 1 show the relative impact that the promotional offer analytics would have on 6 key financial and business drivers in support of the customer targeting business decision.
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