the Dr Eugene Dubossarsky zen Find out more! data science

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1 the zen of data science Dr Eugene Dubossarsky Find out more!

2 Are you a data scientist?

3 Are you managing a data scientist?

4 Are you looking to hire a data scientist?

5 Well you might have some questions What software should I buy? Who should I hire? How do I retain data scientists? How do I make use of all my data?

6 nope.

7 How to manage and leverage the value of data science in business.

8 Today s talk is about: Myself (shameless promotion) Difference between data scientists & engineers Where does data science fit in a business? Data science & analytics today Do you need data science? Finding, training & managing data scientists

9 Shameless self promotion Ask me about

10 Shameless self promotion Training Ask me about

11 Shameless self promotion Training Consulting Ask me about

12 Shameless self promotion Training Consulting Ask me about Working

13 Shameless self promotion Training Consulting R Users Data Visualisation Quantum Computing Data Science Ask me about Working Networking

14 You have to build an airport.

15 But everybody thinks this is an aeroplane.

16 Uh oh a fundamental category error.

17 Anything done with this misconception in place will be a waste of time, money and resources...

18 Work around it. Be realistic about the client s expectations.

19 Getting the fundamental issue sorted out would seem to be the first order of business

20 Because uh: elephant

21 There are several fundamental category errors affecting the field of data analytics

22 What is a data scientist?

23 Businessy words What is a data scientist? 3 X Olympic gold medalist Communication skills Big data Big data science Blockchain Hadoop IoT

24 Scientists & engineers Really important! Science and engineering are different.

25 Scientists & engineers Really important! Science and engineering are complementary opposites.

26 Scientists & engineers Source: The Difference Between Science And Engineering Farnam

27 Scientists & engineers Source: The Difference Between Science And Engineering Farnam

28 Scientists & engineers Start with identified idea End with a design Source: The Difference Between Science And Engineering Farnam

29 Scientists & engineers Start with identified idea Build or maintain something to pre-defined parameters End with a design Source: The Difference Between Science And Engineering Farnam

30 Scientists & engineers Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design Source: The Difference Between Science And Engineering Farnam

31 Scientists & engineers Engineers Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design

32 Scientists & engineers Engineers Start with identified idea For engineers data is a commodity that flows through the system. The focus is on the system. Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design

33 Scientists & engineers Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design Source: The Difference Between Science and Engineering Farnam Street

34 Scientists & engineers Develop an understanding of the world Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters Start with reality - derive new insights End with a design Source: The Difference Between Science and Engineering Farnam Street

35 Scientists & engineers Develop an understanding of the world Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Explore and interrogate data for insights Start with reality - derive new insights Build or maintain something to pre-defined parameters End with a design Source: The Difference Between Science and Engineering Farnam Street

36 Scientists & engineers Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design Source: The Difference Between Science and Engineering Farnam Street

37 Scientists & engineers Scientists Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights

38 Scientists & engineers Scientists Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights Scientists are focused on the data to be explored. Their objective is to tell the story of the data to someone who cares and matters (ideally CEO).

39 Scientists & engineers Scientists Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights Engineers Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design

40 Scientists & engineers Scientists Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights Engineers Start with identified idea Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design

41 Scientists & engineers Scientists Engineers Develop an understanding of the world Uncertainty is the job (outputs and their consequences are unknown ahead of time) Explore and interrogate data for insights Start with reality - derive new insights Start with identified idea Why the confusion? Uncertainty is the enemy (time, budget, resources, performance) Build or maintain something to pre-defined parameters End with a design

42 Scientists & engineers Engineers? It s all technical, apparently Titles have the word data in it Some groups benefit from the confusion (vendors / management) Those responsible for hiring are out of their depth (HR / recruiters / management) Scientists?

43 Scientists & engineers Scientists Real businesses need both skill sets Science is crucial for real competition and risk Science is irrelevant otherwise Engineers

44 Scientists & engineers Scientists Science is irrelevant otherwise Delivery Science is crucial for real competition and risk Intelligence Real businesses need both skill sets Engineers

45 Data science fits where? Now, where to put the data scientists?

46 Data science fits where? Here?

47 Data science fits where? Here?

48 Data science fits where? Here?

49 Data science fits where? A mix of Scully (rational), Shuri (innovative) and Steve Jobs (business acumen)

50 Data science fits where? I m no ti T

51 Data science fits where? se u t E2 n o C I d RIN P

52 Data science fits where? U A tb I m no

53 Data science & analytics So what is data science and analytics?

54 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Asset or Vanity? Engaged or Disengaged? Measured?

55 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Insights are different every time Asset or Vanity? Data Science can help set up BAU Engaged or Disengaged? processes Deriving value from insights from a Measured? model is not a repeatable process Analytics derive value from predictive targeting: a repeatable, mechanical Valued by businesses which are process under competitive pressure to change Requires engineering function

56 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Conceptual skills are required Value to Data science Hadoop (or other or Compliance? achieve data science outcomes: Asset or Vanity? tools) Engaged or Disengaged? Ability to explore data Tools change Measured? Statistical skills Tools are important for some Flexible in switching from established BAU processes engineering to science functions (engineering) as required Tool agnostic

57 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Asset or Vanity? Data science adds value through: Function exists due to government Engaged or Disengaged? Measured? (or other regulatory) requirements Targetting or other improvements to BAU functions This looks like analytics and science, Delivering strategic insights but is not a science function

58 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Asset or Vanity? Engaged or Disengaged? About finding truth (which may be Measured? Data science function can exists as a inconvenient) vanity project For organisational benefit - fits a business need No business need, no understanding in management Clear goals, clear reporting lines, clear purpose Typically unclear: scientific goals, accountability & purpose

59 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Asset or Vanity? Engaged or Disengaged? Measured? Management / CEO / internal Management / CEO / internal customers want answers / insights customers unaware of data science from data scientists function Management think and make decisions based on outcomes

60 Data science & analytics Insights or Process? Outcomes or Tools? Transformation or BAU? Value or Compliance? Asset or Vanity? Engaged or Disengaged? Measured? There are KPIs related to the data science function (e.g. hedge funds) There are no KPIs relating to the data science function The KPIs are sensible (e.g. accuracy) Or, there are ridiculous KPIs (timeframes, models output per week) CEO cares about the KPIs

61 Do you need data science? Do you need a data science function?

62 Do you need data science? Yes if your business is facing real competition, real threats, real uncertainty and real change.

63 Do you need data science? Yes if your business is facing real competition, real threats, real uncertainty and real change. No for everyone else

64 Find and keep data scientists Looking for this?

65 Find and keep data scientists Skills are rare

66 Find and keep data scientists Most people hiring are out of their depth We need you to dynamically orchestrate extensible value

67 Find and keep data scientists Managers are out of their depth Science me 1000 models by Friday

68 Find and keep data scientists What does good look like? Sample job description Case study: small financial services company Summary

69 Find and keep data scientists Case study: job description

70 Find and keep data scientists Case study: job description Advertisement is short and to the point.

71 Find and keep data scientists Case study: job description Clear vision

72 Find and keep data scientists Case study: job description Have data

73 Find and keep data scientists Case study: job description Well defined stakeholders

74 Find and keep data scientists Case study: job description Clearly articulated outcomes which align with stated vision

75 Find and keep data scientists Case study: job description Defined relevant metrics aligned with goals

76 Find and keep data scientists Case study: job description Less is more. Reasonable list of tools as relates directly to the job

77 Find and keep data scientists Case study: job description Use open source tools

78 Find and keep data scientists Case study: job description Less is more. Don t ask for the expert who does not exist

79 Find and keep data scientists Case study: job description Clear distinction between required skills and desirable experience

80 Find and keep data scientists Case study: job description Information not directly related to the position is listed after the job description (not shown)

81 Find and keep data scientists Case study: job description Short concise description introducing: Field Stakeholders Data High level goals Role has clear outcomes and metrics Reasonable list of: Tools Skills Clear separation of requirements from wish list

82 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data science function Had a clear goal: predict defaults Outcome Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed format of output (e.g. excel file) and all other tools left to data scientist Data scientist built model, and consults as required

83 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data science function Had a clear goal: predict defaults Outcome Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

84 Find and keep data scientists Case study: small financial services company Background: But! Had an analytics-aware CEO Young company with little / no data science function Had a clear goal: predict defaults Outcome Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

85 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data Had a clear goal: predict defaults Clearly articulated science function outcomes Outcome Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

86 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data science function Had a clear goal: predict defaults Outcome Relevant metrics aligned with outcomes Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

87 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data science function Had a clear goal: predict defaults Outcome CEO has skin in the game, is personally invested in the project and genuinely interested in the success of the project Hired data scientist as a consultant good models >>metrics good decisions >> profit >> business growth Data scientist and business defined useful Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

88 Find and keep data scientists Case study: small financial services company Background: Young company with little / no data science function Had a clear goal: predict defaults Don t need to hurry to hire someone! Outcome Good consulting services are useful for any business starting out in analytics Hired data scientist as a consultant Data scientist and business defined useful metrics Data scientist and business discussed outputs and how they would feed into business decisions Data scientist built model, and consults as required

89 In summary...

90 business environment from competition

91

92 intense competition Disruptors dominant business players in various markets

93 Data science done well =

94 Data science done well = I need to Make important decisions informed by reliable analysis based on real world data

95 Data science done well =...management clearly define objectives and Outcomes Make important decision/s

96 Data science done well = The success of the outcomes are measured Make by clear, well understood important and relevant decision/s Outcomes Metrics

97 Management check Data exists Data science done well = and have the political will to ensure it is accessible to data scientists Make important decision/s Outcomes Metrics

98 Data Data science done well = Make important decision/s I have everything I need! Let the Data science happen Outcomes Metrics

99 Data Data science done well = Make important decision/s I have everything I need! Let the Data science happen Outcomes and as their manager I know: Metrics

100 Data science done well = Data is given to data scientists Data science happens achieving Make important decision/s Outcomes measured by share outcomes Metrics reviewed by informed stakeholder YES NO Accept metrics?

101 Vision realised Data science done well = Data is given to data scientists Data science happens achieving Make important decision/s Outcomes measured by share outcomes Metrics reviewed by informed stakeholder YES NO Accept metrics?

102 Data Cool new thing! There are KPIs for measuring the Data science done well effectiveness of strategic decisions and decision makers! Vision realised = is given to data scientists Make important decision/s For another talk (May 29, Data Science Sydney) Data science happens achieving Outcomes measured by share outcomes Metrics reviewed by informed stakeholder YES NO Accept metrics?

103 Dr Eugene Dubossarsky Find out more! Questions?