Building A Shared Understanding Of Data Assets In A Business Through A Single Pane Of Glass With Workstream
Data Engineering Podcast
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The Workstream Product Is a Single Paint of Glass for Analytics Assets
Reallat is a place where teams can bring together disperat data and analetic assets into a single unified repository. One way to think about our product is a single paint of glass for your analytics assets. And so it serves as that indof centralized, single plane of glass ascess layer. Then it also then facilitates a collaboration and work flows, all directly in context with your data.
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Summary
There is a constant tension in business data between growing siloes, and breaking them down. Even when a tool is designed to integrate information as a guard against data isolation, it can easily become a silo of its own, where you have to make a point of using it to seek out information. In order to help distribute critical context about data assets and their status into the locations where work is being done Nicholas Freund co-founded Workstream. In this episode he discusses the challenge of maintaining shared visibility and understanding of data work across the various stakeholders and his efforts to make it a seamless experience.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Nicholas Freund about Workstream, a platform aimed at providing a single pane of glass for analytics in your organization
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Workstream is and the story behind it?
- What is the core problem that you are trying to solve at Workstream?
- How does that problem manifest for the different stakeholders in an organization?
- What are the contributing factors that lead to fragmentation of visibility for data workflows at different stages?
- What are the sources of information that you use to build a cohesive view of an organization’s data assets?
- What are the lifecycle stages of a data asset that are most often overlooked or un-maintained?
- What are the risks and challenges associated with retirement of a data asset?
- Can you describe how Workstream is implemented?
- How have the design and goals of the system changed since you first started it?
- What does the day-to-day interaction with workstream look like for different roles in a company?
- What are the long-range impacts on team behaviors/productivity/capacity that you hope to catalyze?
- What are the most interesting, innovative, or unexpected ways that you have seen Workstream used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Workstream?
- When is Workstream the wrong choice?
- What do you have planned for the future of Workstream?
Contact Info
- @nickfreund on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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