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1:00 What is Oz’s bikini model data scenario? Or when to listen to the data given and when to look deeper into why the data looks the way it does.
2:35 Robin explains what data can and cannot do.
3:45 Robin explains why it is important to know your mission with data.
4:25 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.
5:48 Robin discusses how to decide on data that needs to be explored more for insights.
6:25 Oz talks about finding outliers that are not captured.
6:50 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.
9:00 Oz gives an example of how clickbait can backfire when looking at analytics.
13:00 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.
15:48 Robin and Oz give advice to new analysts on dealing with data that seem to be opposite of what decision-makers are expecting.
17:00 Have conversations about goals.
18:29 Robin and Oz discuss what they mean by being a relentless analyst.
18:45 Oz talks about the importance of curiosity and tenaciousness and gives scenarios.
19:10 Robin gives her insights into what makes a relentless analyst and gives examples.
20:00 Robin and Oz compare how their roles can be different with their clients.
22:00 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.
28:20 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.
29:09 Oz’s final thoughts and advice.
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Listen if you want the answers to these questions:
3:00 What data tools does Robin love?
4:24 What can you call a database?
6:47 How does Robin see a database?
8:22 How did Robin discover her love of databases?
11:00 How did Oz get into Excel?
14:15 Can you leverage database technologies to do your job?
18:00 When does Robin think you go from a spreadsheet to a database?
21:30 What does Robin want you to know about how to use databases?
27:30 When does Oz think you go from a spreadsheet to a database? 29:45 What is a data warehouse?
30:38 When should you re-evaluate your data governance stack?
32:22 What happens when your people don’t have data skills?
34:30 Is Power Query a database technology?
35:30 What should you know about joins?
38:58 Who do Robin’s students think teaches joins better, Robin or Oz?
40:00 Why does Robin think all data workers should have at least a basic knowledge of Access?
45:10 What are the pros and cons of databases?
46:35 What are the pros and cons of spreadsheets?
49:22 When is a good time to investigate database technology?
52:40 What should databases do?
57:00 What does Robin want all data workers to develop? 1:00:00 What are Robin’s takeaways?
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Robin Hunt of ThinkData Solutions and Oz du Soleil, Microsoft Excel MVP, both live and breathe data! Listen in their conversation if you ever want to find out their takes on the following questions:
What makes a good data analyst? (2:06)
Why have Robin and Oz started this series (3:35)
What is data validation for an analyst? (5:00)
What are some failures in how data can be taught? (6:13)
At what point should a good data analyst start asking questions about the data? (6:45)
How can a good leader identify a good data worker? (9:23)
What does the future data worker look like? (10:40)
Why is data validation important? (11:05)
Why is data quality important? (11:54)
Why is it important to develop processes in data? (15:00)
Why is data cleaning so important? (23:04)
What are some good tips to spot data that needs cleaning? (24:00)
Why is Robin such a record count fanatic? (26:00)
Why should data validation techniques be used at the point of data entry? (29:00)
What does Oz want you to take away from this episode? (33:24)
Both Robin and Oz are LinkedIn Learning Instructors whose courses have been studied by hundreds of thousands of people who are interested in data.
