Tori Oblad | Getting Executives to Support Data Science
22 July 2021, 05 pm

Tori Oblad | Getting Executives to Support Data Science

Enterprise Data Officier at WaFD

Episode notes

We were recently joined by Tori Oblad, Enterprise Data & Analytics Officer at WaFd Bank.

Here are a few snippets from our conversation:

1:14 - Start of session
3:00 - How to build an internal data science community
11:40 - Showing the art of the possible
14:00 - How do you get others to lead topics and foster engagement?
26:17 - Writing starter scripts for new users
35:55 - When to use R or Python versus BI
36:38 - Building toy models in Excel to explain it to people / to build relationships with business
38:33 - Avoiding vendor lock-in, being technology agnostic
43:35 - How to build confidence with IT and compliance
49:15 - Working with business users and creating business value
53:21 - Getting business and executive support
1:22:30 - What data scientists should focus on when communicating with stakeholders: value

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Featured in this episode
Rachael Dempsey
Rachael DempseyHost
Community Manager at RStudio
I love connecting people across the data science community to share what they're accomplishing with data and help others do the same through community discussions, industry meetups, and more.
Tori Oblad
Tori ObladGuest
Enterprise Data & Analytics Officer at WaFd Bank

Frequently asked questions

An audience-led conversation held on Zoom focused on data science leadership that is open to all every Thursday at 12 ET. You can check out the recordings on this site as well! Learn more on the About DSH page here:

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That's up to you. The host, Rachael usually opens up the discussion by asking the featured leader what they are most excited about in data science currently, and then we turn the questions over to the audience. Here are a few examples of questions that have come up before:

What is the divide between a business analyst and a data scientist?

What is the most unique problem you've had to solve in your industry?

My leaders refer to our BI tools when asked about data science, do you consider BI data science? Why/why not?

Did you always have managing skills/people skills, or did you have to learn them?

Could you define levels of data science maturity for companies?

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