Big data introduction - Big Data from a Consulting perspective - Sogeti
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Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5 ...

Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5

This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.

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  • Kopieer onderstaande regel in de adresregel van je browser voor de gebruikershandleiding van deze template: https://einstein.sogeti.nl/sites/einstein.sogeti.nl/files/page_attachments/PP%20handleiding%20130318.pdf

Big data introduction - Big Data from a Consulting perspective - Sogeti Presentation Transcript

  • 1. Big Data A start
  • 2. Big Data from a Consulting perspective Edzo Botjes Business Analyst, Sogeti Consulting Services Amersfoort 2013 05 28
  • 3. 3Titel | Onderwerp | Plaats | Datum | DATA is the NEW OIL
  • 4. 4Big Data a Start | People Consulted | Amersfoort | 2013 05 28 | People Consulted Big Data experts IT Data Experts Business Information Architects Big Data experts Business Data Experts Information Management Architects Business Big Data experts VINT Big Data expert R20 Desk Research
  • 5. 5Big Data a Start | Content | Amersfoort | 2013 05 28 | What were the questions from The management team? Content Conclusion / Answers Actions to take as MT
  • 6. 6Big Data a Start | What were the questions | Amersfoort | 2013 05 28 | What were the questions • Question 1 - What is Big Data? • Question 2 - Big Data in current organization? • Question 3 - What is the future role of the Information Management department in the subject Big Data?
  • 7. 7Big Data a Start | Content | Amersfoort | 2013 05 28 | Content Conclusion / Answers Actions to take as MT What were the questions from The management team?
  • 8. 8Big Data a Start | What were the questions | Amersfoort | 2013 05 28 | Why Big Data is a MT subject Source: http://www.myforrester.net/big-data-webinar ; http://www.airlineleader.com/pdfs/Airline%20Leader%20Issue%2014.pdf
  • 9. 9Big Data a Start | What were the questions | Amersfoort | 2013 05 28 | Why Big Data is a MT subject Source: https://www.bmelv.de/SharedDocs/Downloads/Verbraucherschutz/Internet-Telekommunikation/SaferInternetDay2013Ksker.pdf?__blob=publicationFile page 14 “Big Data, was ist das?", Dr. Holger Kisker, VP and Research Director Forrester. February 2013
  • 10. 10Big Data a Start | What is data | Amersfoort | 2013 05 28 | What is data / information ?
  • 11. 11Big Data a Start | What is data | Amersfoort | 2013 05 28 | From data to wisdom Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 6 "Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem. et al. June 2012
  • 12. 12Big Data a Start | What is data | Amersfoort | 2013 05 28 | Role of insight Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 8 "Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem. et al. June 2012 Source: http://cci.uncc.edu/sites/cci.uncc.edu/files/media/pdf_files/MIT-SMR-IBM-Analytics-The-New-Path-to-Value-Fall-2010.pdf page 4
  • 13. 13Big Data a Start | Definition | Amersfoort | 2013 05 28 | Definition of Big Data ?
  • 14. 14Big Data a Start | Definition | Amersfoort | 2013 05 28 | The Attack of the exponentials Source: http://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean slide 4 "Building Data Start-ups: Fast, Big and Focused" by Michael E. Driscoll CTO Metamarkets, May 2011
  • 15. 15Big Data a Start | Definition | Amersfoort | 2013 05 28 | 3 V’s that define Big Data (or 4?) VALUE Source: http://www.slideshare.net/multiscope/data-pioneers-sander-duivestein-vint-future-of-data slide 9 “The future of data” by Sander Duivestein , June 2012
  • 16. 16Big Data a Start | Definition | Amersfoort | 2013 05 28 | Big Data definition at Goldman Sachs et al. BIG DATA == Transaction + Interaction + Observation Source: http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/ "7 Key Drivers for the Big Data Market" by Shaun Connolly at the Goldman Sachs Cloud Conference May 2012
  • 17. 17Big Data a Start | Definition | Amersfoort | 2013 05 28 | Big Data Definition by Edzo BIG DATA == Real time data + Real time analysis (graph data) + Real time reaction (feedback loop) Source: Edzo Botjes
  • 18. 18Big Data a Start | Examples | Amersfoort | 2013 05 28 | Examples of the 3 V's
  • 19. 19Big Data a Start | Examples | Amersfoort | 2013 05 28 | Examples of Size and Source Source: http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/ Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf
  • 20. 20Big Data a Start | Examples | Amersfoort | 2013 05 28 | Examples of Big Data Analytics Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 18 "Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al.. June 2012
  • 21. 21Big Data a Start | Examples | Amersfoort | 2013 05 28 | Examples Big Data
  • 22. 22Big Data a Start | Examples | Amersfoort | 2013 05 28 | Examples of Big Data in the real life Source:http://www.computerworld.com/s/article/9233587/Barack_Obama_39_s_Big_Data_won_the_US_election http://www.infoworld.com/d/big-data/the-real-story-of-how-big-data-analytics-helped-obama-win-212862 http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ http://commons.wikimedia.org/wiki/File:Target_logo.svg http://www.rfgen.com/blog/bid/285148/Tesco-Improves-Supply-Chain-with-Big-Data-Automated-Data- Collection http://www.computerweekly.com/news/2240184482/Tesco-uses-big-data-to-cut-cooling-costs-by-up-to-20m http://img.dooyoo.co.uk/GB_EN/orig/0/7/7/7/3/777389.jpg
  • 23. 23Big Data a Start | Examples | Amersfoort | 2013 05 28 | Big Data ready?
  • 24. 24Big Data a Start | Examples | Amersfoort | 2013 05 28 | Your Big Data profile: what does that look like? Big Data is concerned with exceptionally large, often widespread bundles of semi structured or unstructured data. In addition, they are often incomplete and not readily accessible. “Exceptionally large” means the following, measured against the extreme boundaries of current standard it and relational databases: petabytes of data or more, millions of people or more, billions of records or more, and a complex combination of all these. With fewer data and greater complexity, you will encounter a serious Big Data challenge, certainly if your tools, knowledge and expertise are not fully up to date. Moreover, if this is the case, you are not prepared for future data developments. Semi-structured or unstructured means that the connections between data elements are not clear, and probabilities will have to be determined. Further to read: B. Ten Big Data management challenges: what are your issues? C. Five requirements for your Big Data project: are you ready? Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 18 "Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al. June 2012 Are you Big Data ready? Or to big a leap? “Big”
  • 25. 25Big Data a Start | Tips | Amersfoort | 2013 05 28 | Most important Tip (s)
  • 26. 26Big Data a Start | Tips | Amersfoort | 2013 05 28 | Tips • Never, Ever, start without a Business Case and thus a business sponsor. • Added value of Big Data is combination of “External” Sources. Think outside the box, outside your silo. • Maturity is key. - Start with identifying - then go optimizing, scale to BI, BI++ and - then to real time added value Big Data feedback loops
  • 27. 27Big Data a Start | Tips | Amersfoort | 2013 05 28 | Maturity (Big Data is young and quick) The notion that opportunities to capitalize on Big Data are simply lying there, ready to be seized, is echoing everywhere. In 2011, the McKinsey Global Institute called Big Data “the next frontier for innovation, competition, and productivity” and the Economist Intelligence Unit spoke unequivocally of “a game-changing asset.” These are quotes taken from titles of two directive reports on Big Data, a topical theme that is developing vigorously, and about which the last word has certainly not been uttered. McKinsey states it very explicitly: This research by no means represents the final word on big data; instead, we see it as a beginning. We fully anticipate that this is a story that will continue to evolve as technologies and techniques using big data develop and data, their uses, and their economic benefits grow (alongside associated challenges and risks). •“Innovation” •“Competition” •“Productivity” Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 18 "Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al. June 2012
  • 28. 28Big Data a Start | Questions | Amersfoort | 2013 05 28 | What were the questions • Question 1 - What is Big Data? • Question 2 - Big Data in current organization? • Question 3 - What is the future role of the Information Management department in the subject Big Data?
  • 29. 29Big Data a Start | Current Organization | Amersfoort | 2013 05 28 | Big data in current organization CRM Internal R&D Internal BI Social Media Data Virtualization
  • 30. 30Big Data a Start | Questions | Amersfoort | 2013 05 28 | What were the questions • Question 1 - What is Big Data? • Question 2 - Big Data in current organization? • Question 3 - What is the future role of the Information Management department in the subject Big Data?
  • 31. 31Big Data a Start | Role | Amersfoort | 2013 05 28 | Vision / Role
  • 32. 32Big Data a Start | Role | Amersfoort | 2013 05 28 | Information Management Role be an advising guide Bring together Create innovation environment Bring success to production
  • 33. 33Big Data a Start | Role | Amersfoort | 2013 05 28 | Information Management Role Facilitate Execute Be a leader Bring together Create innovation environment Bring success to production Source: http://www.alfredoartist.com/Optimized%20Images/Rodriguez-InSearchOfGold.jpg http://resources1.news.com.au/images/2011/07/27/1226102/848013-gold-prospecting.jpg http://www.refinedinvestments.com/wp-content/uploads/2012/10/gold-mining400x282.jpg http://en.rockscrusher.com/wp-content/uploads/2011/04/gold-mining-plant.jpg
  • 34. 34Big Data a Start | Role | Amersfoort | 2013 05 28 | Information Management Role Not the Information Management Role 1.Employ Data scientists 2.Develop new data analyses technique’s 3.Be a business sponsor Information Management Role 1.Facilitate the gold finding process (POCs)  Bring data scientist in touch with business 2.Be owner of the gold mining process (projects) 3.Have and Execute a vision on data governance and data virtualization. (reduce future costs on projects, POCs and changes etc.)
  • 35. 35Big Data a Start | Questions | Amersfoort | 2013 05 28 | What were the questions • Question 1 - What is Big Data? • Question 2 - Big Data in current organization? • Question 3 - What is the future role of the Information Management division in the subject Big Data?
  • 36. 36Big Data a Start | Content | Amersfoort | 2013 05 28 | Content Conclusion / Answers Actions to take as MT What were the questions from The management team?
  • 37. 37Big Data a Start | Actions | Amersfoort | 2013 05 28 | Big Data Actions Data Board Data Governance Data Virtualization Create a Network
  • 38. 38Big Data a Start | Actions | Amersfoort | 2013 05 28 | Goals of the Data board • Role of a Steering Committee / Governance • Once a month (2 months) meeting • Advice to POCs, brainstorm for POCs, Assist breaking silos, create a platform for governance issues (Possible KPI.. 3 POCs per year?) • Great Variety inside Organization and outside (for example a professor, young people, R&D and business and more experienced internal employees)
  • 39. 39Big Data a Start | Actions | Amersfoort | 2013 05 28 | Subjects – Data Governance • Where is what data ? • Who owns the data ? • Who owns the application that stores the data ? • Who can access the data ? • Who is responsible of data quality (and how) ? • What are the legal implications and boundaries ?
  • 40. 40Big Data a Start | Actions | Amersfoort | 2013 05 28 | Subjects – Data Virtualization • Future enormous cost reduction • Improvement of MI • Faster data centric solution • Lower cost of projects Source: http://res.sys-con.com/story/may11/1849158/data%20virt%20image_0.jpg
  • 41. 41Big Data a Start | Actions | Amersfoort | 2013 05 28 | Subjects – Create a Network Create connections with and between: • Universities • External experts / stakeholders • (Small) specialized companies • Internal experts / stakeholders Source: http://learnthat.com/files/2008/06/people-network1.jpg
  • 42. 42Big Data a Start | Content | Amersfoort | 2013 05 28 | Content Conclusion / Answers Actions to take as MT What were the questions from The management team?
  • 43. 43Big Data a Start | Actions | Amersfoort | 2013 05 28 | Big Data in the Enterprise Data Board Data Governance Data Virtualization Create a network Facilitate Execute
  • 44. This is just the beginning