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Leave IT Alone – The Vast Value of Self-Service

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As more and more business roles are expected to be data-driven, the demand for data is growing exponentially. The only way businesses can scale data-driven decision-making is with self-service. But ungoverned self-service access to data doesn't necessarily lead to better decisions. So the critical question for businesses is how to enable analysts and casual business users to self-serve data in a meaningful and trustworthy way. Check out this episode of Deep Dive to find out! Host Eric Kavanagh will share insights about best practices and great ideas in the field of self-service BI. He'll be joined by Kenny Cunanan of Looker, who will explain how practical guard rails can keep users on track, while enabling them to explore data in ways that spark ideas and lead to better decisions.

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Leave IT Alone – The Vast Value of Self-Service

  1. 1. DEEP DIVE Leave IT Alone: The Vast Value of Self-Service #DMRadio
  2. 2. Featured Speakers
  3. 3. The Long-Standing Data Warehousing Models
  4. 4. The Reliance on ETL Must Subside!
  5. 5. Trust is the Cornerstone of Data-Driven Business Data warehousing was designed with old constraints - Storage was expensive - Processors were slow - Pipes were thin - Consolidation into a warehouse was possible Today, we don’t need to strip out context! Federated queries can help restore trust in data! Without trust, data is basically useless
  6. 6. The Cycle Time for Disruption Is Collapsing The business world gets less forgiving by the day! Companies must be agile to survive. If organizations are not data-driven, they will not last! Consider these omens: Uber Airbnb LinkedIn
  7. 7. Leave IT Alone The Vast Value of Self Service DM Radio SPEAKER K en n y C u n a n a n Pr o d u c t M a r k et i n g & An a l y t i c s M a n a g er
  8. 8. Kenny Cunanan 2 Product Marketing Manager, Looker
  9. 9. Our Questions I. How did we get here? II. What has changed? III. What do we do? 3
  10. 10. How did we get here? 4
  11. 11. 5 By standing on the shoulders of giants. 1970 1980 1990 APRIL Relational Database Model Developed by Edgar Codd
  12. 12. 6 By standing on the shoulders of giants. 1970 1980 1990 APRIL Relational Database Model Developed by Edgar Codd SEQUEL (later SQL) developed at IBM
  13. 13. 7 By standing on the shoulders of giants. 1970 1980 1990 2000Relational Database Model Developed by Edgar Codd SEQUEL (later SQL) developed at IBM Monolithic BI tools like MicroStrategy and BusinessObjects gain traction
  14. 14. 8 By standing on the shoulders of giants. 1970 1980 1990 2000Relational Database Model Developed by Edgar Codd SEQUEL (later SQL) developed at IBM Monolithic BI tools like MicroStrategy and BusinessObjects gain traction Self-Service Tools like Tableau and Excel begin to gain prominence
  15. 15. What has changed? 9
  16. 16. 10 Databases NowDatabases Then We’re living in an age of modern databases
  17. 17. We’re also living in the Golden Age of Data 11 More data captured More data literacy
  18. 18. This means we can build on lessons from the past 12 Single Source of Truth Data Discovery Self-Service
  19. 19. 13 Technology alone is NOT going to solve everything.
  20. 20. 14 Think of accessing data as grocery lines
  21. 21. 15 Think of accessing data as grocery lines
  22. 22. 16 True self-service creates a relationship between IT and business users
  23. 23. So, what do we do? 17
  24. 24. 18 We focus on these two areas Build trust in the data Train Users Effectively
  25. 25. Build Trust in the Data 19 • Everyone accesses the same model • Analysts codify their knowledge • Logic lives in one place which means • Version Control • Auditable • Keeps everyone on the same page
  26. 26. Train Users Effectively 20 “If you have an open service culture, you want everyone to have skills to use business intelligence tools.” - Carl Anderson, Warby Parker
  27. 27. 21 Example of a Curriculum Data Sources What data sources are available to us? Data Storage Where does the data live in our organization? How does it get there? Data Access What are the different ways that we can access data? Why might we want to access data one way over the other? Data Analysis How do we ask proper questions of the data? What questions should I ask being in role X? Communication What’s the best way to visualize data? How do I present my findings so I don’t extrapolate or stretch an interpretation? How do I provide feedback?

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