data discovery

On December 7, 1941, Japan attacked Pearl Harbor, drawing America into World War II. Admiral Husband Kimmel, a decorated veteran and commander of the US naval fleet in Hawaii, was completely caught off guard. But it didn’t have to be a surprise. How did the US forces at Pearl Harbor miss the critical warning signs? Mismanagement of important intelligence data played a significant role.

Companies run on data. It’s the backbone that allows them to understand their customers, make informed decisions, and see the big picture. But what happens when you don’t know what data you have, where it is, or how to access it?

Steve Twomey, author of Countdown to Pearl Harbor: The Twelve Days to the Attack, explains why the US forces at Pearl Harbor were unprepared for the attack, and Scott Bridgen, former Global GTM & Strategy Lead-GRC & Integrated Risk at OneTrust, explains why

Welcome to a new show about trust. It takes hard work to build and a moment to break. And if that happens, you might never get it back.

We’ve got five surprising stories that will help you understand why trust matters, from a tragic steamboat fire that claimed more than 1,000 lives to the space shuttle Challenger disaster, and a medical research project that harmed instead of healed.   

These stories will help you understand your blind spots, from privacy and consent, to data discovery, to workplace culture – and show you how to create an organization built around trust.

Join Open||Source||Data executive producer Audra Montenegro as she and Sam discuss his learnings and takeaways from this season and what the future of open source data looks like.

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“There's such an open conversation about, ‘Yeah, open source,’ we usually think about open source software. How can we cross apply more of what we think about in software in general into data, and then what is it that's totally new about this domain? So, the answers cluster into three groups. It's either about the source of the data itself is open, meaning this is government data or data that's been made public and it's openly accessible. Or it could be that open source data is how the data is actually produced. Is it using open source tooling? Is it on an open source architecture? And finally, how do you trust that open source data? If it's just a whole bunch of data but it hasn't been labeled, if it hasn't been managed and produced, turned into a product. How do you understand its heritage? How do you understand the lineage of the data so that you can produce trustworthy models and trustworthy results based on it? So it's a big open field, but those are the general responses that people have when we explore that topic.” – Sam Ramji

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Episode Timestamps:

(01:29): What open source data means to our guests

(02:57): Sam discusses the themes of season 3

(10:38): What Sam is looking forward to in the future of open source data

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Links:

LinkedIn - Connect with Sam

LinkedIn - Connect with Audra

Twitter - Follow Sam

Twitter - Follow Audra

This bonus episode features conversations from season 1 and 2 of the Open||Source||Data podcast. In this episode, you’ll hear from Zhamak Dehghani, Director of Emerging Technologies at ThoughtWorks North America; David Thomas, Principal at Deloitte; and Shirshanka Das, Founder of LinkedIn DataHub and Acryl Data.

Sam sat down with each guest to discuss data meshes, fabrics, and discovery. 

You can listen to the full episodes from Zhamak Dehghani, David Thomas, and Shirshanka Das by clicking the links below.

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Episode Timestamps:

(00:36): Zhamak Dehghani

(01:41): David Thomas

(02:43): Shirshanka Das

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Links:

Listen to Zhamak’s episode

Listen to David’s episode

Listen to Shirshanka’s episode

This episode, in honor of draft season, we’re discussing the NFL’s newest tactics to quantify and predict players’ success, and diving into Spotify’s case for data discovery. Leaving behind the problems of “not enough data,” Will and Triveni ask new questions: when we have so much data, where do we start, how do we organize it, and how can we use it?

Catch up on what we’re reading: 

How We Improved Data Discovery for Data Scientists at Spotify - https://labs.spotify.com/2020/02/27/how-we-improved-data-discovery-for-data-scientists-at-spotify/

The NFL’s Quest to Quantify Quarterback Evaluation - https://www.theringer.com/2020/4/17/21224389/nfl-draft-quantifying-quarterback-evaluation

Solution to Riddle #1: 

Question: Write an equation to make two 2s equal the value of 5. You can only use the number 2 twice. 

Answer: Square root of 0.2 to the power of minus 2.

In this episode of The Audio Project with Deena Tearney, we talk about elevating data and making data discovery THE process.  Listen in as Deena Tearney and Nick Redding PhD chat on how he created an impactful framework for making discoveries to improve wellness in Hawaii.

Deena White Tearney is the Founder and CEO of Pacific Point, a Salesforce Consulting firm. Her passion is mentoring youth to pursue careers in tech. Connect with Deena on Instagram or Linkedin and find out more about Pacific Point .