Unit 2: Asking Connecting Questions Faculty: Dr. Adam Mersereau and Dr. Brad Staats
The Successful Data Scientist: • Definition and infographic on page 7 • Five dimensions of the so-called data scientist include being a:
o (1) business or domain expert, (2) statistics expert, (3) programming expert, (4) database technology expert; and (5) visualization and communications expert. • The Data Science Team: No one person is an expert in all five dimensions. Put a team together who can be responsible for all of the five dimensions. (see data science teams article on page 8) Connecting questions equal “crunchy questions”* and should : • Be “sticky” & aligned with strategic goals. • Relate to key performance indicators. • Be designed to be actionable and informational. • Provide foresight rather than hindsight. –NICOLE LASKOWSKI (TechTarget) • Be capable of forming the connective tissue between tactical and senior-level objectives in an organization. – JOHN LUCKER (Deloitte) Using connecting questions is how an expert translates strategic goals into action. * “ Crunchy Questions” – from Deloitte Model Building:
• Asking connecting questions is really an exercise in model building. • By model, we mean a representation of how inputs are related to outputs. Models drive the conscious decisions that we make. • There can be an explicit model like Moneyball, based on written statistics. • Or there can be a mental model , a sense for what is good, but it’s not written down. • Exploring Blackjack: Movies like Bringing Down the House or 21 use the concept of data in order to win.
Connecting Questions Framework: “An approximate answer to the right question is worth a great deal more than a precise answer to the wrong question.” –JOHN TUKEY • Lots of moving parts - Inputs, outputs and relationships. • Challenges with data: Asking the wrong question, getting the wrong data, failing to understand the data, conducting a poor analysis of data, and making incorrect decisions. Key Takeaways: • Netflix is a great example of using data analytics to forecast what a user may enjoy. • Fictitious Casino Case Study: How can a casino can make more money? Giving comps, food and drinks to those who spend a lot of time at gambling. How much more gambling is generated by the comps, food and drinks? Assist to create best comp policy for the future. Are comps impacting daily behavior of guests? • Top-Down approach: Consider - what’s wrong? what are the symptoms? and how to solve it… • Bottom-Up approach: Look at data to understand questions we should ask.
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