How to Create and Manage a Successful Analytics Organization

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For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.

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How to Create and Manage a Successful Analytics Organization

  1. 1. Mario Faria 1 How to Create and Manage a Successful Analytics Organization Mario Faria fariamario@hotmail.com +1 - (425) 628-3517 @mariofaria
  2. 2. Mario Faria 2 Who am I ? •  MIT recognition as one of the 1st Chief Data Officers and Data Scientist Leaders in the world (just Google “Mario Faria Chief Data Officer”) •  20+ years working with Information Technology, Management Consulting, Financial Services, Retail, CPG and Private Equity •  Proven expertise in Data Management, Data Science, Analytics, CRM and Supply Chain Management •  Speaker at several conferences on the subject in USA, Europe and Latin America •  Contributor to magazines and publications •  Big Data Advisor TPN at the Bill and Melinda Gates Foundation •  Member of the MIT Data Science Initiative •  Helping companies cross the Big Data Chasm
  3. 3. Mario Faria 3 Objectives of this webinar •  Provide insights on how you should successfully create a Data organization •  With that in place, you will be able to work effectively with Big Data projects
  4. 4. Mario Faria 4 My mission : To help the data community evolve with sustainability
  5. 5. Mario Faria 5 By being a consultant, I want to say 3 things ...
  6. 6. Mario Faria 6 The 3 things: •  Situation : where the market is at this point •  Complication : current issues with Data Management, Big Data and Analytics •  Solution : what I recommend you to do and how to do it
  7. 7. Mario Faria 7 Situation
  8. 8. Mario Faria 8 How we got here
  9. 9. Mario Faria 9 Evolution of Business Intelligence
  10. 10. Mario Faria 10 The 4 driving factors that are changing the technology industry as we know it •  Social •  Mobile •  Cloud •  Information
  11. 11. Mario Faria 11 This brave new world we are living in •  How does success look like in a world where consumers are now marketers ? •  Where a trillion data points are available, alive and transforming decisions (preference / purchase) and relationships as we speak ? •  How to understand, connect and consistently engage with consumers and customers creating loyalty and recommendations ?
  12. 12. Mario Faria 12
  13. 13. Mario Faria 13 “The balance of power in the 21st century is influenced by the ability to leverage information assets” – Gwen Thomas, CEO of The Data Governance Institute
  14. 14. Mario Faria 14 Data is about •  People •  Technology •  Processes •  Modeling •  Statistics •  Communication •  Decisions •  Actions A data-driven culture is a disruptive factor for entire industries
  15. 15. Mario Faria 15 SQL MAPREDUCE HADOOP CLOUDSCALE MPI BSP PREGEL DREMEL PERCOLATOR What is Big Data?
  16. 16. Mario Faria 16
  17. 17. Mario Faria 17
  18. 18. Mario Faria 18 What is Analytics ? “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” – Thomas Davenport
  19. 19. Mario Faria 19 Analytics is transforming data assets into competitive insights, that will drive business decisions and actions, using people, processes and technologies
  20. 20. Mario Faria 20 Analytic Maturity Curve
  21. 21. Mario Faria 21 Analytics is not just about : •  Large volumes •  Greater scope of information •  Real time access to information •  New kind of data and analytics •  Data influx from new technologies •  Non-traditional forms of media •  Variety of sources It all of the above, plus a transformation in processes and culture, and it is a disruptive factor for entire industries
  22. 22. Mario Faria 22 Analytics is about customer centricity •  Supply Chain forecasting •  Behavioral analysis •  Operations improvement •  Marketing targeting / decisions •  Real-time pricing / promotions •  Customer experience analysis •  Customer insights •  Customer lifecycle management •  Fraud prevention and analysis •  Network monitoring
  23. 23. Mario Faria 23 Predictive Analytics •  Prediction is powered by the world's most potent, booming unnatural resource: data •  Predictive analytics is the science that unleashes the power of data Dr.Eric Siegel
  24. 24. Mario Faria 24 Big Data & Analytics = Human Behaviour
  25. 25. Mario Faria 25 Data Monitoring Centers
  26. 26. Mario Faria 26 Complication
  27. 27. Mario Faria 27 Land of Confusion
  28. 28. Mario Faria 28 Who owns the Data inside an organization ?
  29. 29. Mario Faria 29 Some problems, at this point, in most organizations •  Data is fragmented and scattered •  Silos of information hanging around •  Like the truth, data has many versions •  The Data Lifecycle is a complex process •  Data projects being managed by IT •  A formal process to manage data is a requirement in order to do Analytics
  30. 30. Mario Faria 30 The problem : data is an abstract concept
  31. 31. Mario Faria 31 The complexity of the Data Life Cycle
  32. 32. The Big Data Technology Players
  33. 33. Mario Faria 33 The evolution path to Big Data
  34. 34. Mario Faria 34 Confusion between Big Data and Hadoop •  Hadoop is being wrongly treated as a synonym of Big Data •  Hadoop is one of the technologies to be used at Big Data projects •  Hadoop is a great technology for storing unstructured data in an expensive and scalable manner, in a high granularity •  What Linux did to Operating Systems, Hadoop is bringing to Information Management
  35. 35. Mario Faria 35 The Hadoop Ecosystem : growing everyday
  36. 36. Mario Faria 36 The Big Data Fragmented Tech Vendors : data life cycle process view
  37. 37. Mario Faria 37 Understanding Hadoop/MapReduce Usage Output/ Input (records) Job Input Size GB PB Best case scenario
  38. 38. Mario Faria 38 An analogy of using MapReduce Traditional usage MapReduce usage
  39. 39. Mario Faria 39 And, unfortunately, technology alone will not change the previous results To succeed in Data & Analytics, an organization will be required to change some of its current internal processes
  40. 40. Mario Faria 40 The catch : just a few companies (users and consulting) understood the nits and grits about Analytics : it requires you to moving from a simple data management vision (tactical) to an information management vision (strategic)
  41. 41. Mario Faria 41 Solution
  42. 42. Mario Faria 42 Find a real object that people can relate to
  43. 43. Mario Faria 43 The Data Value Chain
  44. 44. Mario Faria 44 The Deming Model : Production Viewed as a System
  45. 45. Mario Faria 45 What is Data Quality ? •  Quality is a customer perception •  A few dimensions: freshness, coverage, completeness, accuracy •  It is a never ending job
  46. 46. Mario Faria 46 Usage of wrong data can destroy credibility
  47. 47. Mario Faria 47 A Few Quality Programs TDQM TIQM
  48. 48. Mario Faria 48 More and more, Data Leaders are being hired to think strategically think about all the steps from getting raw data and making it useful to business users
  49. 49. Mario Faria 49 Foundations of the Analytics team responsibilities •  Data Strategy •  Data Analytics •  Data Insights •  Data Architecture •  Data Governance •  Data Quality •  Data Acquisitions •  Data Operations •  Data Policies •  Data Security •  Data Protection
  50. 50. Chief  Data  Officer  /     Head  of  Analy6cs  /     Data  Scien6sts  
  51. 51. Mario Faria 51 Chief Data Officer (CDO) / Chief Analytics Officer (CAO) / Lead Data Scientist
  52. 52. Mario Faria 52 The role of a Chief Data Officer or Lead Data Scientist A data scientist is the one who looks for insights The insight is operationalized in BI/DW products, by data architects The insight is shared with the enterprise The CDO or Lead Data Scientist is the executive responsible and accountable for the data life cycle inside the organization, managing the people involved in the data activities, such as acquisitions, analytics, processes, governance, quality, technology and budget
  53. 53. Mario Faria 53
  54. 54. Mario Faria 54 Why should not IT be managing this transition ? Because data projects are business projects, not IT projects and the CDO/Data teams are the bridge between IT and Business Units
  55. 55. Mario Faria 55 The Chief Data Officer Role
  56. 56. Mario Faria 56 The 3 Architectures a Company needs to succeed Business Architecture Technology Architecture Data Architecture
  57. 57. Mario Faria 57 Why do you need a Chief Data Officer ?
  58. 58. Mario Faria 58 Why do you need a Chief Data Officer ? •  Data is about business, it's not about IT •  Data is an economic asset, so you need a senior person to handle the data initiatives. •  As an economic asset, data needs: control, show value and monetization •  There is now way you can do Advanced Analytics unless you have some data management practices in place.
  59. 59. Mario Faria 59 “Organizations are about to be swamped with massive data tsunamis. The Chief Data Officer is responsible for engineering, architecting, and delivering organizational data success” – Peter Aiken, PhD
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  61. 61. Mario Faria 61 A Chief Data Officer is the executive responsible to manage these areas
  62. 62. Mario Faria 62 •  A good CDO can implement a data & analytics organization with success •  A great CDO has the ability to turn raw data into large revenue streams for the business •  Components such as technology and methodologies are important, but they are just enablers •  The CDO focus is delivering enterprise value to the business (not writing code or SQL scripts) From good to great CDO
  63. 63. Mario Faria 63 The evolving CDO role will challenge structure, scope and power relationships between executive committee members. The scarcity of information leader talent will require executive leaders to develop it as much as hire it.
  64. 64. Mario Faria 64 At the end, on Big Data, a CDO and the team should •  Support the data initiatives, using the assets from different sources, with quality as a requirement •  Drive business insights, so the users can act promptly •  Execute his/her tasks fast, in real-time if possible
  65. 65. Mario Faria 65 The main drivers for Analytics projects •  Make more money •  Reduce current costs •  Improve efficiency
  66. 66. Mario Faria 66 What it takes to make Analytics projects drive results •  Data – understand what they have and how to be creative when it comes to using internal and external data •  Models – focus on developing models that predict and optimize •  People – transform their organizations with tools and effective training so that managers can take advantage of Big Data's insights.
  67. 67. Mario Faria 67 To start an Analytics Team inside, there are 4 main things to consider People Technology Process to implement the Practice Methodology for the Delivery
  68. 68. Mario Faria 68 From good to great, an analytics team must have: •  Passion for analytics and data •  Never stop learning •  Always be there for tough analytics questions •  Ask questions until everything makes sense and you are satisfied with the answers and analyses •  Learn how to develop prototypes quickly •  Be an advocate for building a strong foundation in corporate analytics •  Be a "bridge builder" between IT and business users
  69. 69. Mario Faria 69 Looking ahead in the near future …
  70. 70. Mario Faria 70 Which companies will thrive in 2015? •  The ones which will understand how to adapt faster to this new scenario •  The ones which will have successful Analytics implementations •  The ones with great human capital, which understand how to leverage their resources and with proven methodologies to embrace this change
  71. 71. Mario Faria 71 Is your company going to lead, influence or follow when using data and analytics to drive results ?
  72. 72. What does it take to succeed in this Analytics journey ?
  73. 73. Mario Faria 73 Major points on how to structure an Analytics program •  Upper management buying and support •  Do not reinvent the wheel : use and abuse of best practices that already exist •  Communicate always and be transparent •  Quick wins And …
  74. 74. Mario Faria 74 Hire the best and most eager resources you can find
  75. 75. Mario Faria 76 “Business are complex systems, optimizing a single element rarely creates lasting value”- Peter Drucker, the father of modern management
  76. 76. Q&A
  77. 77. Mario Faria 78 Thank you Mario Faria Data Strategy Advisor http://www.linkedin.com/in/mariofaria/ Founder of the Digital Mad Men www.slideshare.com/fariamario Twitter : @mariofaria fariamario@hotmail.com +1 (425) 628-3517

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