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Como criar e gerenciar com sucesso uma organização de dados


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  • 1. Mario Faria1How to Create and Manage aSuccessful Data OrganizationMario - (425) 628-3517@mariofaria
  • 2. Mario Faria2Who am I ?•  MIT recognition as one of the 1st Chief Data Officers and Lead DataScientists in the world (just Google “Mario Faria Chief Data Officer”)•  20+ years working with Information Technology, ManagementConsulting, Financial Services, Retail, CPG and Private Equity•  Proven expertise in Data Management, Data Science, Analytics andSupply Chain Management•  Speaker at several conferences on the subject in USA, Europe andLatin America•  Contributor to magazines and publications•  Big Data Advisor at the Bill and Melinda Gates Foundation•  Member of the MIT Data Science Initiative
  • 3. Mario Faria3Objectives of this webinar•  Provide insights on how you should successfully create aData organization•  With that in place, you will be able to work effectively withBig Data projects
  • 4. Mario Faria4My mission :To help the data communityevolve with sustainability
  • 5. Mario Faria5By being a consultant,I want to say 3 things ...
  • 6. Mario Faria6The 3 things:•  Situation : where the market is at this point•  Complication : current issues with datamanagement and Big Data•  Solution : what I recommend you to do and howto do it
  • 7. Mario Faria7Situation
  • 8. Mario Faria8How we gothere in terms ofBig Data
  • 9. Mario Faria9Evolution of Business Intelligence
  • 10. Mario Faria10The 4 driving factors that arechanging the technology industry aswe know it•  Social•  Mobile•  Cloud•  Information
  • 11. Mario Faria11This brave new world we are living in•  How does success look like in aworld where consumers are nowmarketers ?•  Where a trillion data points areavailable, alive and transformingdecisions (preference /purchase) and relationships aswe speak ?•  How to understand, connect andconsistently engage withconsumers and customerscreating loyalty andrecommendations ?
  • 12. Mario Faria12
  • 13. Mario Faria13“The balance of power in the 21stcentury is influenced by the abilityto leverage information assets” –Gwen Thomas, CEO of The DataGovernance Institute
  • 14. Mario Faria14Data is about•  People•  Technology•  Processes•  Modeling•  Analytics•  Communication•  Decisions•  ActionsA data-driven culture is a disruptive factor for entire industries
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  • 18. Mario Faria18From BusinessIntelligence toBig Data
  • 19. Mario Faria19What is Analytics ?“The extensive use of data, statisticaland quantitative analysis, explanatoryand predictive models, and fact-basedmanagement to drive decisions andactions” – Thomas Davenport
  • 20. Mario Faria20Analytic Maturity Curve
  • 21. Mario Faria21The Four Types of Analytics
  • 22. Mario Faria22Differences between Big Dataand Traditional BI projects
  • 23. Mario Faria23Analytics 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 sourcesIt all of the above, plus a transformation in processes andculture, and it is a disruptive factor for entire industries
  • 24. Mario Faria24Analytics 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
  • 25. Mario Faria25Predictive Analytics•  Prediction is powered by the worlds most potent,booming unnatural resource: data•  Predictive analytics is the science that unleashes thepower of dataDr.Eric Siegel
  • 26. Mario Faria26The 3 ingredients to makeAdvanced Analytics work•  Choosing the right data and managing multiple datasources•  Having the capability to build advanced models that turnthe data into insights•  Management must undertake a transformational-changeprogram so that the insights translate into effective action
  • 27. Mario Faria27Big Data=Human Behaviour
  • 28. Mario Faria28Data Monitoring Centers
  • 29. Mario Faria29Complication
  • 30. Mario Faria30Land of Confusion
  • 31. Mario Faria31Who owns the Data inside anorganization ?
  • 32. Mario Faria32Some problems, at this point, inmost 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 arequirement in order to do Analytics
  • 33. Mario Faria33The problem : data is anabstract concept
  • 34. Mario Faria34The complexity of the Data Life Cycle
  • 35. TheBig DataTechnologyPlayers
  • 36. Mario Faria36The evolution path to Big Data
  • 37. Mario Faria37Confusion between Big Data andHadoop•  Hadoop is being wrongly treated as a synonym ofBig Data•  Hadoop is one of the technologies to be used atBig Data projects•  Hadoop is a great technology for storingunstructured data in an expensive and scalablemanner, in a high granularity•  What Linux did to Operating Systems, Hadoop isbringing to Information Management
  • 38. Mario Faria38The Hadoop Ecosystem : growingeveryday
  • 39. Mario Faria39The Big Data Fragmented Tech Vendors : data life cycleprocess view
  • 40. Mario Faria40UnderstandingHadoop/MapReduceUsageOutput/Input(records)Job Input SizeGB PBBest case scenario
  • 41. Mario Faria41An analogy of using MapReduceTraditional usageMapReduce usage
  • 42. Mario Faria42TheBig DataArchitectureTransformationand AnalysisYou may trade offconsistency and integrityfor speed and flexibility
  • 43. Mario Faria43Big Data Analytics Projects
  • 44. Mario Faria44And, unfortunately, technology alone willnot change the previous resultsTo succeed in Data & Analytics, an organization will berequired to change some of its current internal processes
  • 45. Mario Faria45The catch : just a few companies (usersand consulting) understood the nits andgrits about Data Analytics : it requires youto moving from a simple data managementvision (tactical) to an informationmanagement vision (strategic)
  • 46. Mario Faria46Solution
  • 47. Mario Faria47Find a real object that peoplecan relate to
  • 48. Mario Faria48The Data Value Chain
  • 49. Mario Faria49The Deming Model :Production Viewed as a System
  • 50. Mario Faria50What is Data Quality ?•  Quality is a customer perception•  A few dimensions: freshness, coverage,completeness, accuracy•  It is a never ending job
  • 51. Mario Faria51Usage of wrong data can destroycredibility
  • 52. Mario Faria52A Few Quality ProgramsTDQMTIQM
  • 53. Mario Faria53More and more, Data Leaders are being hiredto think strategically think about all the stepsfrom getting raw data and making it useful tobusiness users
  • 54. Mario Faria54Foundations of the Data teamresponsibilities•  Data Strategy•  Data Analytics•  Data Insights•  Data Architecture•  Data Governance•  Data Quality•  Data Acquisitions•  Data Operations•  Data Policies•  Data Security•  Data Protection
  • 55. Chief  Data  Officer  /    Head  of  Analy6cs  /    Data  Scien6sts  
  • 56. Mario Faria56Chief Data Officer (CDO) /Chief Analytics Officer (CAO) /Lead Data Scientist
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  • 58. Mario Faria58Chief Data Officer (CDO) /Chief Analytics Officer (CAO) /Lead Data Scientist•  A new profession that is becoming very common incorporations•  He/she is a corporate officer who is the businessleader for enterprise-wide data processing and datamining.•  The CDO typically reports to the CEO or the COOand is a member of the executive management teamof a company or business unit.•  CDOs leverage their organizations data assets tosupport the business strategy. He/she managesenterprise-wide data administration and is thechampion of enterprise information management•  CIOs are very concerned with this new role, becauseof the threat to their current power
  • 59. Mario Faria59The role of a Chief Data Officer orLead Data ScientistA data scientist is the onewho looks for insightsThe insight is operationalizedin BI/DW products, by data architectsThe insight is sharedwith the enterpriseThe CDO or Lead Data Scientist is theexecutive responsible and accountable forthe data life cycle inside the organization,managing the people involved in the dataactivities, such as acquisitions, analytics,processes, governance, quality, technologyand budget
  • 60. Mario Faria60Why should not IT be managingthis transition ?Because data projects are businessprojects, not IT projects and the CDO/Datateams are the bridge between IT andBusiness Units
  • 61. Mario Faria61The ChiefDataOfficerRole
  • 62. Mario Faria62The 3 Architectures a Company needsto succeedBusinessArchitectureTechnologyArchitectureDataArchitecture
  • 63. Mario Faria63Data/Information Architecture
  • 64. Mario Faria64Why do you need a Chief Data Officer ?
  • 65. Mario Faria65Why do you need a Chief Data Officer ?•  Data is about business, its not aboutIT•  Data is an economic asset, so youneed a senior person to handle thedata initiatives.•  As an economic asset, data needs:control, show value and monetization•  There is now way you can doAdvanced Analytics unless you havesome data management practices inplace.
  • 66. Mario Faria66“Organizations are about to beswamped with massive datatsunamis. The Chief Data Officeris responsible for engineering,architecting, and deliveringorganizational data success” –Peter Aiken, PhD
  • 67. Data  Science      The  process  of  taking  raw  data,  producing  informa6on  from  data,  and  using  this  informa6on  to  guide  ac6ons  that  will  bring  financial  benefits  to  business  
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  • 70. Mario Faria70A Chief Data Officeris the executiveresponsible tomanage these areas
  • 71. Mario Faria71•  A good CDO can implement a data organizationwith success•  A great CDO has the ability to turn raw data intolarge revenue streams for the business•  Components such as technology andmethodologies are important, but they are justenablers•  The CDO focus is delivering enterprise value to thebusiness (not writing code or SQL scripts)From good to great CDO
  • 72. Mario Faria72The evolving CDO role will challenge structure, scope and powerrelationships between executive committee members.The scarcity of information leader talent will require executiveleaders to develop it as much as hire it.
  • 73. Mario Faria73At the end, on Big Data, a CDO and theteam should•  Support the data initiatives, using the assets fromdifferent sources, with quality as a requirement•  Drive business insights, so the users can actpromptly•  Execute his/her tasks fast, in real-time if possible
  • 74. Mario Faria74The main drivers forData/Big Data projects•  Make more money•  Reduce current costs•  Improve efficiency
  • 75. Mario Faria75What it takes to make Big Data projectsdrive results•  Data – understand what they have andhow to be creative when it comes tousing internal and external data•  Models – focus on developing modelsthat predict and optimize•  People – transform their organizationswith tools and effective training so thatmanagers can take advantage of BigDatas insights.
  • 76. Mario Faria76Data, Information, Analytics, BusinessIntelligence and Performance Management
  • 77. Mario Faria77To start an Analytics Team inside, there are 4main things to considerPeopleTechnologyProcess toimplement thePracticeMethodology forthe Delivery
  • 78. Mario Faria78From good to great, an analytics teammust have:•  Passion for analytics and data•  Never stop learning•  Always be there for tough analyticsquestions•  Ask questions until everything makes senseand you are satisfied with the answers andanalyses•  Learn how to develop prototypes quickly•  Be an advocate for building a strongfoundation in corporate analytics•  Be a "bridge builder" between IT andbusiness users
  • 79. Mario Faria79Looking ahead in the near future …
  • 80. Mario Faria80Which companies will thrive in 2015?•  The ones which will understand how to adapt faster tothis new scenario•  The ones which will have successful Analyticsimplementations•  The ones with great human capital, which understandhow to leverage their resources and with provenmethodologies to embrace this change
  • 81. Mario Faria81Is your company going to lead,influence or follow when using dataand analytics to drive results ?
  • 82. What does ittake to succeed inthis data journey ?
  • 83. Mario Faria83Major points on how to structurea data governance program•  Upper management buying and support•  Do not reinvent the wheel : use and abuse of bestpractices that already exist•  Communicate always and be transparent•  Quick winsAnd …
  • 84. Mario Faria84Hire the best and most eagerresources you can find
  • 85. Mario Faria86“Successful people shoot for the stars,put their hearts on the line in everybattle, and ultimately discover that thelessons learned from the pursuit ofexcellence mean much more than theimmediate trophies and glory”Josh Waitzkin, The Art of Learning
  • 86. Mario Faria87Thank youMario FariaData Strategy Advisor of the Digital Mad : (425) 628-3517
  • 87. Q&A