August 18, 2021 - In this episode, Robin Hunt of ThinkData Solutions, Inc. and Oz du Soleil of Excel on Fire discuss why it's crucial to have conversations about what your data shows and what you know about your business. They explore various scenarios with data that, if taken at face value, may be misleading, and why an analyst needs to be "relentless" with data.

What is Oz’s scenario that involves data being skewed by a bikini? Or when to listen to the data given and when to look deeper into why the data looks the way it does. (1:00)
Robin explains what data can and cannot do. (2:35)
Robin explains why it is important to know your mission with data. (3:45)
Oz explores how using analytics alone without an idea of where you want to be may take you on a path you don’t want to be on. (4:25)
Robin discusses how to decide on data that needs to be explored more for insights. (5:48)
Oz talks about finding outliers that are not captured. (6:25)
Robin explores how often data that is meant to inform a decision is used to actually dictate a decision. Oz gives examples of his experiences with such a scenario. (6:50)
Oz gives an example of how clickbait can backfire when looking at analytics. (9:00)
Robin and Oz talk about how they approach scenarios when what you would think you need to do with data seems to be taking you off-path. (13:00)
Robin and Oz give advice to new analysts on dealing with data that seem to be opposite of what decision-makers are expecting. (15:48)
Have conversations about goals. (17:00)
Robin and Oz discuss what they mean by being a relentless analyst. (18:29)
Oz talks about the importance of curiosity and tenaciousness and gives scenarios. (18:45)
Robin gives her insights into what makes a relentless analyst and gives examples. (19:10)
Robin and Oz compare how their roles can be different with their clients. (20:00)
Oz and Robin give approaches to explaining to their clients why they need to dig deeper to get the solutions their clients want. Robin shares a scenario. (22:00)
Oz talks about what it can be like to be the sole “data” guy that sees how all the data is connected and people are looking to you to find their solutions. (28:20)

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