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Are you still working for a data justified company?

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Advice for junior web analysts to avoid spending your time with reporting, huge data extracts, monitoring a large number of KPIs and getting your recommendations ignored

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Are you still working for a data justified company?

  1. 1. Are you still working for a data-justified company? Alban Gérôme @albangerome MeasureCamp Moscow 7 April 2018
  2. 2. Data-driven vs data-justified
  3. 3. Data-driven vs data-justified • Start with a business question
  4. 4. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need
  5. 5. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture
  6. 6. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis
  7. 7. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test
  8. 8. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation
  9. 9. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes
  10. 10. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like
  11. 11. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support
  12. 12. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support • Request more data than what you really need
  13. 13. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support • Request more data than what you really need • Cherry-pick the data that justifies your prior beliefs, discard the rest
  14. 14. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support • Request more data than what you really need • Cherry-pick the data that justifies your prior beliefs, discard the rest • Make your business case
  15. 15. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support • Request more data than what you really need • Cherry-pick the data that justifies your prior beliefs, discard the rest • Make your business case • Implement changes
  16. 16. Data-driven vs data-justified • Start with a business question • Brainstorm about the data we will need • Implement the data capture • Formulate an hypothesis • Run a test • Make a recommendation • Implement changes This what data-driven looks like • Start with beliefs and the idea you want to support • Request more data than what you really need • Cherry-pick the data that justifies your prior beliefs, discard the rest • Make your business case • Implement changes This is what data-justified looks like
  17. 17. My company is data-justified!
  18. 18. My company is data-justified!
  19. 19. My company is data-justified! data-justified immature
  20. 20. My company is data-justified! data-justified immature data-driven mature
  21. 21. Analytics talent shortage
  22. 22. Analytics talent shortage Let’s build an analytics team with internal employees
  23. 23. Analytics talent shortage Let’s build an analytics team with internal employees • Great understanding of the business
  24. 24. Analytics talent shortage Let’s build an analytics team with internal employees • Great understanding of the business • Poor data literacy and objectivity
  25. 25. Analytics talent shortage Let’s build an analytics team with internal employees • Great understanding of the business • Poor data literacy and objectivity Let’s hire external talent
  26. 26. Analytics talent shortage Let’s build an analytics team with internal employees • Great understanding of the business • Poor data literacy and objectivity Let’s hire external talent • Hard to find, expensive
  27. 27. Analytics talent shortage Let’s build an analytics team with internal employees • Great understanding of the business • Poor data literacy and objectivity Let’s hire external talent • Hard to find, expensive • Lack of domain knowledge
  28. 28. Just a thought…
  29. 29. Just a thought… A shortage of analytics talent
  30. 30. Just a thought… A shortage of analytics talent Few data-driven companies
  31. 31. Just a thought… A shortage of analytics talent Few data-driven companies What if the former worked for the latter, only the latter?
  32. 32. Just a thought… data-justified immature data-driven mature
  33. 33. Just a thought… data-justified immature data-driven mature
  34. 34. Just a thought… data-justified immature data-driven mature Zero web analysts work there
  35. 35. Just a thought… data-justified immature data-driven mature All web analysts work there Zero web analysts work there
  36. 36. Making your own luck
  37. 37. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts
  38. 38. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts • The analysts, sick of boritoring, start building networks and
  39. 39. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts • The analysts, sick of boritoring, start building networks and exchange opinions and information about better places to work for and
  40. 40. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts • The analysts, sick of boritoring, start building networks and exchange opinions and information about better places to work for and how much they are worth
  41. 41. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts • The analysts, sick of boritoring, start building networks and exchange opinions and information about better places to work for and how much they are worth • Experienced analytics practitioners know which managers have a proven data-driven record
  42. 42. Making your own luck • Data-justified companies can only recruit by taking advantage of ill- informed analysts • The analysts, sick of boritoring, start building networks and exchange opinions and information about better places to work for and how much they are worth • Experienced analytics practitioners know which managers have a proven data-driven record. Anybody else, the answer is нет (nyet)
  43. 43. Managers with analytics skills?
  44. 44. Managers with analytics skills? • Many top-performing employees fail their transition to management
  45. 45. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles
  46. 46. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity
  47. 47. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity I can’t become a head of analytics? Oh well, hello data science!
  48. 48. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity I can’t become a head of analytics? Oh well, hello data science! • Every year Big Four consultants look for client-side manager roles
  49. 49. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity I can’t become a head of analytics? Oh well, hello data science! • Every year Big Four consultants look for client-side manager roles • They will then rotate every couple of years until a CXO role opportunity comes
  50. 50. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity I can’t become a head of analytics? Oh well, hello data science! • Every year Big Four consultants look for client-side manager roles • They will then rotate every couple of years until a CXO role opportunity comes • Therefore prior analytics experience is irrelevant and perhaps even bad
  51. 51. Managers with analytics skills? • Many top-performing employees fail their transition to management • Geeks deemed as unsuitable candidates for managerial roles • Hard to replace an analyst once promoted because of talent scarcity I can’t become a head of analytics? Oh well, hello data science! • Every year Big Four consultants look for client-side manager roles • They will then rotate every couple of years until a CXO role opportunity comes • Therefore prior analytics experience is irrelevant and perhaps even bad Head of analytics? What the heck is that? I will rotate in 2 years, right?
  52. 52. Expert leadership
  53. 53. Expert leadership More and more experienced analytics practitioners are finally getting promoted Head of Analytics and implement a genuinely data-driven approach and transform the analytics department into a profit centre
  54. 54. Expert leadership More and more experienced analytics practitioners are finally getting promoted Head of Analytics and implement a genuinely data-driven approach and transform the analytics department into a profit centre Expert leaders are a great motivator for more junior analysts who can look up to someone who was just like them 5 or 10 years ago
  55. 55. Expert leadership More and more experienced analytics practitioners are finally getting promoted Head of Analytics and implement a genuinely data-driven approach and transform the analytics department into a profit centre Expert leaders are a great motivator for more junior analysts who can look up to someone who was just like them 5 or 10 years ago In cities where flats are ridiculously expensive, expert leadership could help a mid-weight analyst stop renting and get a mortgage instead
  56. 56. Remember this?
  57. 57. Remember this? What if all the web and data analysts worked only for data-driven companies?
  58. 58. Remember this? What if all the web and data analysts worked only for data-driven companies? If you are working in a data-justified department
  59. 59. Remember this? What if all the web and data analysts worked only for data-driven companies? If you are working in a data-justified department, this department only exists
  60. 60. Remember this? What if all the web and data analysts worked only for data-driven companies? If you are working in a data-justified department, this department only exists because you and your colleagues took their job
  61. 61. Remember this? What if all the web and data analysts worked only for data-driven companies? If you are working in a data-justified department, this department only exists because you and your colleagues took their job instead of the same job but at a data-driven company
  62. 62. Nobody wants to work for us?
  63. 63. Nobody wants to work for us? • I told him “That’s how we do web analytics here”. A week later, he handed me his resignation, he had three job offers elsewhere. He was still in his probation period
  64. 64. Nobody wants to work for us? • I told him “That’s how we do web analytics here”. A week later, he handed me his resignation, he had three job offers elsewhere. He was still in his probation period • I don’t understand what’s going on, I’m only getting junior candidates from the career pages and the recruiters say that nobody is interested
  65. 65. Nobody wants to work for us? • I told him “That’s how we do web analytics here”. A week later, he handed me his resignation, he had three job offers elsewhere. He was still in his probation period • I don’t understand what’s going on, I’m only getting junior candidates from the career pages and the recruiters say that nobody is interested • I thought the interview went well, she was a strong candidate. Then the recruiter said she told him after that I could not name one single thought-leader in analytics and she won’t work for us
  66. 66. Identify data-justified companies
  67. 67. Identify data-justified companies • Find other people in analytics
  68. 68. Identify data-justified companies • Find other people in analytics • Figure out how much you are really worth
  69. 69. Identify data-justified companies • Find other people in analytics • Figure out how much you are really worth • Identify the companies and managers who are data-driven in our field
  70. 70. Identify data-justified companies • Find other people in analytics • Figure out how much you are really worth • Identify the companies and managers who are data-driven in our field • When a company is hiring, try to find the name of the manager and check their credentials and reputation
  71. 71. Identify data-justified companies • Find other people in analytics • Figure out how much you are really worth • Identify the companies and managers who are data-driven in our field • When a company is hiring, try to find the name of the manager and check their credentials and reputation • A company had Adobe Analytics and migrated to Google Analytics = symptom of a company that could not deliver value from analytics
  72. 72. At your next interview, ask them
  73. 73. At your next interview, ask them • So, what’s your definition of analytics?
  74. 74. At your next interview, ask them • So, what’s your definition of analytics? • Can you name one thought-leader in the field of analytics?
  75. 75. At your next interview, ask them • So, what’s your definition of analytics? • Can you name one thought-leader in the field of analytics? • What’s the last analytics blog or book you have read in the past 3 months?
  76. 76. At your next interview, ask them • So, what’s your definition of analytics? • Can you name one thought-leader in the field of analytics? • What’s the last analytics blog or book you have read in the past 3 months? If they answer wrong, they fail the interview
  77. 77. большое спасибо! http://www.albangerome.com @albangerome

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