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For predictive analytics to work, two different species must cooperate in harmony: the business leader and the quant. In order to function together, they each have to adapt. On the one hand, the quant needs to attain a business-oriented vantage. And on the other, the business leader must navigate a very alien world indeed. Deal and Pilcher’s new book, “Mining Your Own Business,” helps with that second bit.
When conversation in organizations turn to analytics, topics normally include the quality and accessibility of data, the infrastructure for storing and processing data, the necessary level of analytics sophistication, and the unique skillsets required to build a successful analytics program. Often overlooked in is the importance of having an enterprise level analytics governance strategy.
It will soon be rockfish season again, and even though most of us are better at talking about the fish we caught than we are at catching them, let’s take a moment to discuss a vital data science practice — avoiding reinforcement bias — that will help us improve our catch out on the water.
There is no doubt that a successful Data Scientist must be proficient in programming, modeling, and data munging (extracting, cleaning, and feature engineering data). However, there is another key skill that is often overlooked: the ability to communicate findings clearly and effectively. If you as a Data Scientist cannot motivate the business buy-in to effect change, your powerful model will collect dust on a shelf. Stakeholders will only trust your model if they understand the value it adds, what has been done to create it, and why it works. They should not be left to trust you and your “black box” blindly. The solution is data storytelling: using the power of narrative to communicate your findings in a way that resonates with your stakeholders. Doing this combines your data science expertise with intuitive visualizations and—most importantly—a story to connect the dots.
The key question that our clients ALWAYS ask is “Can we guarantee value from the analytics project?” They want to know whether the return on their investment will be positive. But they want this guarantee at the proposal stage, before we have even seen the data, the technical infrastructure, or other relevant details about the analytics environment. Though a prior guarantee is impossible, there are key factors that one can assess early to estimate project viability and value – to get at the expected cost and return of the proposed investment.
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