SummaryIn this episode of the Data Engineering Podcast Lior Barak shares his insights on developing a three-year strategic vision for data management. He discusses the importance of having a strategic plan for data, highlighting the need for data teams to focus on impact rather than just enablement. He introduces the concept of a "data vision board" and explains how it can help organizations outline their strategic vision by considering three key forces: regulation, stakeholders, and organizational goals. Lior emphasizes the importance of balancing short-term pressures with long-term strategic goals, quantifying the cost of data issues to prioritize effectively, and maintaining the strategic vision as a living document through regular reviews. He encourages data teams to shift from being enablers to impact creators and provides practical advice on implementing a data vision board, setting clear KPIs, and embracing a product mindset to create tangible business impacts through strategic data management.Announcements<ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>It’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today to learn how Datafold can automate your migration and ensure source to target parity.&nbsp;</li><li>Your host is Tobias Macey and today I'm interviewing Lior Barak about how to develop your three year strategic vision for data</li></ul>Interview<ul><li>Introduction</li><li>How did you get involved in the area of data management?</li><li>Can you start by giving an outline of the types of problems that occur as a result of not developing a strategic plan for an organization's data systems?</li><li>What is the format that you recommend for capturing that strategic vision?<ul><li>What are the types of decisions and details that you believe should be included in a vision statement?</li></ul></li><li>Why is a 3 year horizon beneficial? What does that scale of time encourage/discourage in the debate and decision-making process?</li><li>Who are the personas that should be included in the process of developing this strategy document?</li><li>Can you walk us through the steps and processes involved in developing the data vision board for an organization?</li><li>What are the time-frames or milestones that should lead to revisiting and revising the strategic objectives?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen a data vision strategy used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on data strategy development?</li><li>When is a data vision board the wrong choice?</li><li>What are some additional resources or practices that you recommend teams invest in as a supplement to this strategic vision exercise?</li></ul>Contact Info<ul><li><a href="https://www.linkedin.com/in/liorbarak/" target="_blank">LinkedIn</a></li><li><a href="https://cookingdata.substack.com/" target="_blank">Substack</a></li></ul>Parting Question<ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Closing Announcements<ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email <a target="_blank">hosts@dataengineeringpodcast.com</a> with your story.</li></ul>Links<ul><li><a href="https://cookingdata.substack.com/p/wabi-sabi-your-data-crafting-an-imperfect" target="_blank">Vision Board Overview</a></li><li><a href="https://www.dataengineeringpodcast.com/data-product-strategy-episode-397" target="_blank">Episode 397: Defining A Strategy For Your Data Products</a></li><li><a href="https://www.mckinsey.com/alumni/news-and-events/global-news/alumni-news/barbara-minto-mece-i-invented-it-so-i-get-to-say-how-to-pronounce-it" target="_blank">Minto Pyramid Principle</a></li><li><a href="https://www.kpi.org/kpi-basics/" target="_blank">KPI == Key Performance Indicator</a></li><li><a href="https://en.wikipedia.org/wiki/Objectives_and_key_results" target="_blank">OKR == Objectives and Key Results</a></li><li><a href="https://amzn.to/3P93gYV" target="_blank">Phil Jackson: Eleven Rings</a> (affiliate link)</li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>

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