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Do data leaders face unique challenges as leaders?

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Do data leaders face unique challenges as leaders?

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A leadership development talk given to private event for senior data leaders - it sparked some really useful conversations. To find out more please checkout: http://laughlinconsultancy.com

A leadership development talk given to private event for senior data leaders - it sparked some really useful conversations. To find out more please checkout: http://laughlinconsultancy.com

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Do data leaders face unique challenges as leaders?

  1. 1. Paul Laughlin, a talk for the MBN Technology Supper Club, 23/07/20 Leadership Development Do Data Leaders face unique challenges as leaders?
  2. 2. Client-side to Agency-side Created and lead data & analytics teams, for all general & life insurance businesses across Lloyds Bank Group, over 13 years. Added over £11m incremental profit to bottom line annually. Developed team of 44 & mentored future leaders. Background “Helping exceptional teams master the people side of data & analytics” 2
  3. 3. Why do I now spend my time mentoring data leaders? 3
  4. 4. 4
  5. 5. 5
  6. 6. Do data leaders face different challenges compared to other leaders? 6
  7. 7. How many hats/masks do you wear? 7 Tech Leader / Pioneer Finance Leader Customer Leader Data Counsel Marketing & Product Leader Sales Leader / Influencer HR / Talent Pipeline Leader Knowledge Leader / Curator & Artist Strategy Leader / Trusted Adviser
  8. 8. Any hats I’ve missed?
  9. 9. What have I learnt from leading & training data, data science & analytics teams? 9
  10. 10. The 9 Step Model for effective analysis Introducing a model to explain the Softer Skills needed at each stage for analysts or Data Science teams to achieve impact.
  11. 11. (1) Questioning to get to the real business need Socratic Questioning skills to get beneath the request to what the business really needs and how what is delivered will be used.
  12. 12. (2) Planning to ensure reliable delivery Appropriate use of traditional project management and/or agile working methodologies. Learning from the past & planning ahead.
  13. 13. (3) Securing buy-in from the key players Identifying, prioritising and managing stakeholder relationships to ensure you manage expectations & communicate/collaborate well.
  14. 14. (4) Use appropriate data methodology Use of appropriate methodology to ensure reliable repeatable provision of appropriate data, transformed into a format suitable for analytical work.
  15. 15. (5) Use of analytical methods to answer question Use of Exploratory Data Analysis, together with appropriate methods to answer question / test hypothesis / produce model.
  16. 16. (6) Generate insights to understand behaviour Generation of deeper insights into motivation and triggers for behaviour seen in analysis, using structured questioning & converging evidence.
  17. 17. (7) Secure sign-off for implications of findings Navigate organisational politics to agree the implications of what has been learnt & how these will be communicated to achieve recommended action.
  18. 18. (8) Communicate a compelling visual story Use best practice storytelling and data visualisation skills to communicate key findings, implications for business and recommended action(s).
  19. 19. (9) Ensure action as a result to deliver solution Follow-up on recommendations and influence key players to ensure appropriate action that meets the business need. Plus, implement feedback loops so you continue to learn from what happens as a result of actions.
  20. 20. Inform every step with Commercial Awareness Ensure maintain and apply commercial awareness to guide each of the 9 steps. Understanding financial performance, key risks/issues, profit levers, business strategy, competitor intelligence & market changes.
  21. 21. 9 Step Model for effective analysis The Softer Skills needed at each stage for analysts or Data Science teams to achieve impact. A complete development programme, including introduction and deep dive content to develop the skills for each step. Available as offline & online live training from Laughlin Consultancy.
  22. 22. Which Softer Skills matter for leaders?
  23. 23. What have I learnt from mentoring data, data science & analytics leaders? 23
  24. 24. Educate & Challenge other leaders Challenge Team & Others Educate C-Suite on what’s possible ‘Drum up’ suitable Demand Manage Expectations
  25. 25. Do what matters most & break some rules Dare to be Different Break Rules to make a way Manage Priorities &Time Making Agile work for you
  26. 26. Get to know yourself & stakeholders better Intentional Stakeholder plan (PR) Raise Self Awareness (inc. EQ) Flex style to help others (inc. EQ) ‘Person Marking’ relationship management
  27. 27. Build and protect a safe space to learn Achieve greater autonomy Bring together the roles needed Implement Systems & Controls Protect space to test & learn
  28. 28. Stay technical but avoid micro managing Keep up- to-date with best practice Step back from hands-on work Learn how to empower team Protect technical from Perf. Mgt. Sys.
  29. 29. Develop an attitude of insight & influence Inspire learning from others Use a Competency Framework Avoid “back room boffins” Develop a focus on outcomes
  30. 30. Develop your confidence & long-term investment Manage like a Profit Centre Develop Your Confidence Act-up & mentor team to act-up Secure investment for long- term
  31. 31. Role-model professional communication Communicate succinctly Find your own voice & gravitas Improve Professional impact Role-model using visual stories
  32. 32. Think outside silos and be accountable for impact Plan a long-term talent pipeline Influence leaders to take action Join dots & act beyond silos Hold self & team accountable for impact
  33. 33. Be more commercially minded than your peers Establish Commercial credibility - on the spike Develop external profile & knowledge Be really interested in your business Calculate & demonstrate an ROI from team
  34. 34. 34
  35. 35. Which data leader skills matter most?

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