#2 Building a Single Source of Truth - Holy Grail or Overrated?

#2 Building a Single Source of Truth - Holy Grail or Overrated?

Released Thursday, 24th June 2021
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#2 Building a Single Source of Truth - Holy Grail or Overrated?

#2 Building a Single Source of Truth - Holy Grail or Overrated?

#2 Building a Single Source of Truth - Holy Grail or Overrated?

#2 Building a Single Source of Truth - Holy Grail or Overrated?

Thursday, 24th June 2021
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ABOUT THIS EPISODE
Many companies who seek to become more data-driven have it on their target list: Building a "Single Source of Truth". That's a term coined in the world of business intelligence, which has the idea to bring all data analysis and insights together in one place, or one system.

It's the exact opposite of the situation lots of companies still phase: having data silos all over the place, that limit their ability to connect and integrate data for holistic decisions.

Is such a Single Source of Truth the Holy Grail for business analytics or data democratization? Or is it just overrated? Maybe we should ask, is it even realistic to achieve - and how?

I will talk with Nate Spohn, VP EMEA at Fivetran - a solution that puts all your data integration efforts on autopilot. Fivetran is a YCombinator alumnus, backed by Andreessen Horowitz and other leading investors, and valued >$1.2B. Fivetran helps companies to build modern data stacks and solutions, so I'm sure Nate can share valuable insights from their client base.

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From The Podcast

DISRUPT YOUR DATA | The Data Leadership Podcast for business leadersWHY THIS PODCAST?“Stop talking about it, start having an impact with data”, that’s the mantra of this podcast. While many companies have invested in data, analytics & AI over the last years, most of them yet only see a limited business impact. The reasons are numerous, ranging from a missing overall data strategy to technical difficulties or challenges in establishing a data-driven company culture. Too many silos, too much data, too little insights. Hiring 10 data scientists, or solving everything with artificial intelligence, also doesn't do the trick.It’s time to disrupt your data & analytics programs to create true commercial outcomes!ABOUT THE PODCASTIn this podcast, we'll interview outstanding guests who share valuable learnings and their secret sauce to foster digital business success with data. They won't necessarily be data experts, but have proven a commercial impact with data in one way or the other - so you can do that, too.Further, we’ll talk about learnings from many years of data consulting projects, for business leaders in companies such as Zalando, Deutsche Bank, Douglas, Lufthansa, Google, Samsung, ERGO Insurance Group, and more.This podcast is targeted at business leaders, who want to grow faster and create a real impact with Data, Analytics & AI. Whether you’re an executive, leading a department, or a functional team - benefit from best practices from over-performing companies and avoid stepping into classic pitfalls.We know, business leaders have busy schedules, but we promise hearing the podcast is a wise and helpful decision when it comes to data best practices - many of which you can implement in your company as well.ABOUT THE HOSTChris Lubasch is a trusted data advisor, digital consultant, and entrepreneur, with 14 years of experience in digital business models and growth. He was the International Director of BI at Groupon, a digital marketing freelancer, and started multiple companies as an entrepreneur.Chris co-founded LEROI Consulting, a leading data & analytics consulting-agency in Germany, with 35 consultants and solution specialists, working for industry leaders such as the clients listed above and 250 more. LEROI got acquired by Dept Agency in 2019, where Chris is now a Partner and chief data evangelist. In the agency portfolio, his mission is to deeply integrate data with creativity, technology, and digital marketing for digital success.CONNECT WITH CHRISLinkedIn: https://www.linkedin.com/in/lubaschPersonal website: https://www.chrislubasch.com

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