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How To Structure Your Data Science Teams for Best Outcomes : Webinar

Ganes Kesari, Gramener's Head of Analytics, led the webinar, which covered the following topics:<br><br>-Why do data analytics and visualization projects necessitate siloed collaboration?<br>-What are the finest data science organizational structures?<br>-How should your organizational structure change as your data journey progresses?<br>-The most effective way for increasing data project team collaboration<br><br>Webinar link: https://info.gramener.com/data-science-teams-structure-for-best-outcomes<br><br>To Book A Free Demo visit: https://gramener.com/demorequest/

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How To Structure Your Data Science Teams for Best Outcomes : Webinar

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  1. How to Structure Your Data Science Teams for Best Outcomes Webinar, June 2020 Ganes Kesari

  2. Value from Data Science Promoting Collaboration Organizing Data Teams

  3. Introduction • Insights as Stories GanesKesari Co-founder & Head of Analytics • 100+ Clients /gkesari @kesaritweets Help start, apply and adopt Data Science “Simplify Data Science for all”

  4. Value from Data Science Organizing Data Teams Promoting collaboration

  5. “ Only 30 percent of Organizations align their Analytics Strategy with the Corporate Strategy. - McKinsey Reference: McKinsey report

  6. Here’s where you must Start to get Value from Data Here’s a quick recap from our last 2 webinars… Organizations improve in data maturity over phases For business value, they must start with users & pain areas.. ..and build a roadmap by prioritizing on 3 factors Every data science team must have 5 key roles.. ..which become important at different stage of maturity There are best practices to get the right talent onboard Recent Gramener Webinar recordings:Webinar 1: How to pick your data science projects? Webinar 2: How to build your data science teams?

  7. Here’s a short & simple poll to help you reflect. Poll #1 What are your top challenges in getting value from data science teams?

  8. How do you Scale Data Science: A Tale of 2 Organizations vs

  9. 5 Key Challenges in structuring Data Science Teams Poor Business Alignment Solutions going unused Siloed Operation Duplicated tech investments stagnating data maturity

  10. Pic – The Great Dilemma What’s the right way to organize your data science team?

  11. Value from Data Science Organizing Data Teams Promoting collaboration

  12. Centralized Teams start Data Science top-down What works? • Knowledge retention and sharing • Lower redundancy • Best talent for most important projects • Aligned with corporate priorities What doesn’t? • Starts with the leadership’s belief in data • Often housed within the IT organization • Team priorities driven by broader org needs • Suboptimal business alignment • Often seen as slow-moving/bureaucratic • Spread out too thin

  13. Decentralized Teams are driven by business unit priorities What works? • Controlled & prioritized by business • Better to demonstrate quick-wins • Teams gain domain knowledge • Lesser bureaucracy What doesn’t? • Siloed talent & knowledge • Could lead to conflicting ‘truths’ • Often lacks executive support • Aligned with the Business units • Often seen in mid-to-large organizations • Multiple parallel data teams scale up

  14. Hybrid teams balance specialization with connection What works? • Best of both worlds. Can tailor based on org size, know-how & maturity • Talent has ready access to business and a connect with data competency • Balances control and efficiency What doesn’t? • Often led by a data executive • Teams and tools shared • Dual reporting structures • Ambiguity in roles & ownership could take away the gains …here’s an effective Hybrid model..

  15. A Hybrid model that works: Hub-and-Spoke Hub Central group headed by a C-level analytics executive Spoke Market-facing Business unit to own & manage solutions Gray area Work with overlapping responsibilities Execution teams Dynamic teams assembled from hub, spoke & gray area Source: “Building the AI Powered Organization”, HBR, Aug 2019

  16. How do the Hubs and Spokes Scale? Data Engineering Data Science Data as ‘Culture’ Data Collection Data Storage Data Transformation Reporting Insights Consumption Decisions Sales Sales Sales Analytics CoE Analytics CoE Assets Corp Assets Corp Corp Assets Analytics CoE Legal Mktg Fin Fin Mktg Mktg Fin Legal Legal HR Risk Risk HR Risk HR Reference: “Building the AI Powered Organization”, HBR, Aug 2019

  17. Case Study: Evolution of the Gramener Org Structure Media Media Tech Pharma Tech Pharma Labs Advisory Analytics Design Analytics Design Analytics Design Product Eng. Product Eng. Product Eng. Public Emerging Public Emerging Early-stage Specialization Verticalization Mature Execution • All rolled in one • Generalist-heavy • Create COEs • Onboard specialists • Align with 5 verticals • Hub-and-spoke structure • Org-wide processes • Better processes, frameworks • Deeper COEs • Experiments – AI, Story labs • Improve value thru Advisory

  18. “ You can manage a large force the same way you manage a small one. It is a matter of communication and formations. - Sun Tzu

  19. Here’s a short & simple poll to help you reflect. Poll #2 What is your current organizational structure?

  20. Value from Data Science Organizing Data Teams Promoting collaboration

  21. Why Focus on Collaboration with Data? Shared Context Ensure Business ROI Scale Organizations …5 Steps to Promote Collaboration

  22. 1. Form multi-functional Teams led by a Business specialist

  23. 2. Ensure Accountability with full Decision-making rights

  24. 3. Empower Users and keep them in the loop to build Trust

  25. 4. Adopt Process Frameworks for Repeatability and Scale

  26. 5. Invest in an Ecosystem of Tools for open Conversations

  27. #1: Form multi-functional Teams led by a Business specialist #2: Ensure Accountability with full Decision-making rights #3: Empower Users and keep them in the loop to build Trust #4: Adopt Process Frameworks for Repeatability and Scale #5: Invest in an Ecosystem of Tools for open Conversations

  28. Value from Data Science: A study in Contrasts Data-aware Organization Data-driven Organization Data • Used opportunistically • Funds initiatives • Technology-projects • Not multi-functional • Centralized or Decentralized • Unstructured conversations • Resists data • Leveraged strategically • Funds & owns initiatives • Business-programs • Staffs 5 key roles • Hub-and-spoke • Explicitly planned for • Embraces data Leadership Initiatives Roles Org Structure Collaboration Culture

  29. “ The war is not won with bayonets, but with effective organization. - Anonymous

  30. To Learn more.. Check out our other Webinars! How to promote a Culture of Data in your Organization View recording View recording Coming soon… Register for updates!

  31. Here’s a short & simple poll to help you reflect. Poll #3 Feedback Survey

  32. Thank You! /gkesari @kesaritweets gramener.com

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