Acting on Analytics - How to Build a Data-Driven Enterprise - Brighttalk webinar Sept 2013
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Acting on Analytics - How to Build a Data-Driven Enterprise - Brighttalk webinar Sept 2013

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Big Data and Analytics have become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, there is one ...

Big Data and Analytics have become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, there is one component that can halt any initiative: YOUR COMPANY's ANALYTICS MATURITY.

How prepared is your company to implement and use the available data, the high end modeling techniques and the data as well as analytics expertise?

From what I have learned and experienced, companies are still not adequately prepared, hurting their ability to compete in the market.

This presentation will help executives and data professionals to understand the steps needed to create an analytics organization, and provide some real life examples on companies who succeeded and some who failed miserably.

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Acting on Analytics - How to Build a Data-Driven Enterprise - Brighttalk webinar Sept 2013 Presentation Transcript

  • 1. Mario Faria 1 Acting on Analytics Building a Data-Driven Enterprise Sept 11th, 2013 Mario Faria http://www.linkedin.com/in/mariofaria/ www.slideshare.com/fariamario Twitter : @mariofaria
  • 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 at the Bill and Melinda Gates Foundation •  Member of the MIT Data Science Initiative •  Helping companies cross the Big Data Chasm
  • 3. Mario Faria 3 Objectives •  Clarify Analytics •  Present some analytics terms that will help professionals do their jobs better •  Provide insights on how you should successfully create a data & analytics organization •  Present some concepts to help you prepare to implement a data-driven organization •  Bring some attention on how to properly use data •  Show a few options available for your professional future in today’s world
  • 4. Mario Faria 4
  • 5. Mario Faria 5 Where we stand today •  Fragmented technology ecosystem •  Over usage of the Big Data term •  The “how to compete on analytics” is still hard to achieve •  In most companies, data is still managed with an IT mind set
  • 6. Big  Data  will     fade  away     to  Analy1cs  
  • 7. Mario Faria 7 Analytics is transforming data assets into competitive insights, that will drive business decisions and actions, using people, processes and technologies
  • 8. Mario Faria 8 People Data Management Domain Expertise Statistics On Analytics
  • 9. Mario Faria 9
  • 10. Mario Faria 10 Data, Information, Analytics, Business Intelligence and Performance Management
  • 11. Mario Faria 11 The Hawthorne Effect (1924-1933), at a Western Electric factory outside Chicago
  • 12. Mario Faria 12 “Measurement drives behavior, and if we don’t understand how, it drives behaviors in mysterious ways” by Frank Buytendijk, Beingfrank Research, Sept 2012 1.  Understand the decision making process 2.  Understand what the many frameworks in measurement do for us 3.  Understand what can go wrong 4.  Understand the culture
  • 13. Mario Faria 13 Data is the oil of the 21st century
  • 14.   Data  access  is  quite  easy  to   achieve     Transforming  data  into   something  useful  in  a  1mely   manner  is  tough      
  • 15. Mario Faria 15 The 5 Steps from Data to Decisions Souce : Aryng Analytics Consulting
  • 16. Data  Science       The  process  of  taking  raw  data,   producing  informa1on  from  data,   and  using  this  informa1on  to   guide  ac1ons  that  will  bring   financial  benefits  to  business  
  • 17. Data  Mining  is  the   explora1on  and  analysis   of  large  quan11es  of   data  to  discover   meaningful  paCerns     and  rules  
  • 18. Mario Faria 18 Source : Dr.Saed Sayad
  • 19. Mario Faria 19 The Data Mining Goals •  Explain the past •  Predict the future
  • 20. Mario Faria 20 The Data Mining Process 1.  Project definition 2.  Data exploration 3.  Data preparation 4.  Model creation 5.  Model deployment 6.  Model management
  • 21. Machine  learning,  part  of   ar1ficial  intelligence,  is   about  the  construc1on   and  study  of  systems   that  can  learn  from  data  
  • 22. Mario Faria 22 Machine Learning is the "Field of study that gives computers the ability to learn without being explicitly programmed" Arthur Samuel, 1959
  • 23. Machine  Learning  is  about   predic1on  and  learning   from  data     Data  Mining  is  about     finding  unknown  proper1es   of  the  data    
  • 24. Mario Faria 24 Deep X Operational Analytics •  Deep Analytics –  Few users –  Complex queries –  Worried about response time –  Dedicated data marts •  Operational Analytics –  Lots users –  Simple queries –  Worried about throughput –  Real time data flow
  • 25. It takes more than just data and analytics for results to be achieved
  • 26. Mario Faria 26 The 3 Architectures a Company needs to succeed Business Architecture Technology Architecture Data Architecture
  • 27. Mario Faria 27 The Data Value Chain
  • 28. Mario Faria 28
  • 29. Mario Faria 29 Chief Data Officer / Chief Analytics Officer Data Architect Data Quality Data Scientist Data GovernanceData Operations
  • 30. Mario Faria 30 The roles of an Analytics organization 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 Chief Data/Analytics Officer 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
  • 31. Mario Faria 31
  • 32. Mario Faria 32 A Chief Data/ Analytics Officer is the executive responsible to manage these areas
  • 33. Mario Faria 33 The Best of the Breed Data & Analytics Leader •  Knows how to talk business with business people; technology with techies •  Runs his/her team as a business unit •  Has multi-discipline skilled people (technologists, mathematicians, statisticians, business people as well) •  Manages quite well the back office and front office functions
  • 34. Mario Faria 34 The checklist to hire data & analytics professionals Chami Akmeemana Managing Director Huntel Global •  Where are they ? Think global •  What are they interested in ? It is not just money, it is the possibility to “create” something new or transform •  What are they going to do ? Gain clarity on your business analytics strategy •  What are they going to work with ? Set budget for technology tools, data acquisition and other resources needed Link to download the full paper : http://goo.gl/Fc8Xl
  • 35. Mario Faria 35 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
  • 36. How  to  promote   Business  Maturity   through  Analy1cs  ?  
  • 37. Mario Faria 37 Analytic Maturity Curve
  • 38. Mario Faria 38 The Four Types of Analytics
  • 39. Mario Faria 39 Data and Analytics professionals are not helping others to understand it
  • 40. Mario Faria 40 Is it possible to promote Business Maturity using Analytics, or is it the other way around? •  Your company must have some Business Maturity to take a step and start using Analytics. •  When you if take the path of using the right people, good technology, proven methodologies and changing the current processes, you will gain more Business Maturity. •  After you have gained more Business Maturity, naturally you will start to evolve to more complex Analytics usage •  It is a self-promoting system following exactly the Deming PDCA method
  • 41. Mario Faria 41 The 3 ingredients to make Advanced Analytics work •  Choosing the right data and managing multiple data sources •  Having the capability to build advanced models that turn the data into insights •  Management must undertake a transformational-change program so that the insights translate into effective action
  • 42. Mario Faria 42 Managing an Analytics project •  Focus on the execution •  Well-defined and realistic scope •  Put all activities on the schedule •  Budget control
  • 43. Mario Faria 43 Technology alone will not change the previous results To succeed in Analytics, an organization will be required to change some of its current internal processes
  • 44. Good  KPIs  will  not  save   your  business,  but  they   will  be  able  to  tell  how   screwed-­‐up  your   business  is  
  • 45. Conclusions  
  • 46. Mario Faria 46 Which companies will thrive in the near future ? •  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 Is your company going to lead, influence or follow when using data and analytics to drive results ?
  • 47. Mario Faria 47 Lessons to Analytical Success •  People •  Change Management (on going program) •  Culture •  Organization Model •  Processes •  Architecture •  and Analytics, of course
  • 48. Mario Faria 48 “Successful people shoot for the stars, put their hearts on the line in every battle, and ultimately discover that the lessons learned from the pursuit of excellence mean much more than the immediate trophies and glory” Josh Waitzkin, The Art of Learning
  • 49. Q&A
  • 50. Mario Faria 50 Thank you Mario Faria Chief Data Officer and Analytics 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