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Do Universities Dream of Big Data

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Historically, the University of Alberta lacked a centrally managed repository for reporting data, resulting in inconsistency and disparity in access for units across campus. Meaningful and actionable reports were limited, and only focused on the interests and goals of the few units with data analysts who could synthesize the information.

Over the last couple of years, the University of Alberta has undertaken major changes in how information is managed and utilized. At the forefront of this change has been an increased interest in supporting the development of analytics and supporting tools. Beginning with the implementation of a centrally managed data warehouse with self-service capabilities, and the introduction of cloud services with business process analysis tools, the University is just starting down the road of big data.

This presentation explores opportunities and challenges for the University of Alberta in utilizing big data.

Published in: Data & Analytics
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Do Universities Dream of Big Data

  1. 1. BIG DATA DOUNIVERSITIES dream of BIG DATA
  2. 2. 30definitions OPENTRACKER.NET
  3. 3. in excel i don’t FIT
  4. 4. Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... DAN ARIELY
  5. 5. consensus
  6. 6. DATA DRIVEN ENTERPRISE UNIVERSITY DEVELOPMENTS
  7. 7. data driven ENTERPRISE
  8. 8. volume, velocity, and variety
  9. 9. “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.” ERIC SCHMIDT
  10. 10. decision making INFORM
  11. 11. business STRATEGIES
  12. 12. culture RIGHT
  13. 13. 40% 43%ENTERPRISE INITIATIVE TECHNICAL INITIATIVE NOT SEEING EYE-TO-EYE EAB
  14. 14. where is the value?
  15. 15. STRATEGIC use metrics with the high level objectives and look at aggregated results with long time frames.
  16. 16. TACTICAL use measures focused on shorter time periods (days or weeks) and are used by managers to help meet business targets.
  17. 17. OPERATIONAL measures are focused on real-time or transaction data with attention on exception alerting.
  18. 18. ANALYTICSis the new INTELLIGENCE
  19. 19. highlow low high information and analytical maturity businessvalue IBM INFORMATION ENABLED ENTERPRISE HISTORICAL REPORTING SENSE AND REPORT ANTICIPATE AND SHAPE
  20. 20. barriers constraints &
  21. 21. role-based access data governance data quality education
  22. 22. dedicated leadership support dedicated leadership support
  23. 23. university DEVELOPMENTS
  24. 24. weare here
  25. 25. we are building...
  26. 26. ANALYTICS HERO
  27. 27. ready set DATA
  28. 28. EXPANDING ON 3 FRONTS AT AN INCREASING RATE mb gb tb pb real time near real time periodic batch table database photo, web, audio social, video, mobile VOLUME VELOCITY VARIETY
  29. 29. 2000 2005 2011 2013 2014 PEOPLESOFT MOODLE EDRMS GOOGLE DATA WAREHOUSE future
  30. 30. V olume
  31. 31. VELOCITY
  32. 32. reporting and analysis lead to insightful action
  33. 33. Data Decision Decision Support Action Decision Automation Predictive What will happen? Diagnostic Why did it happen? Prescriptive What should happen? Descriptive What happened? Business Intelligence Business Analytics Human Input Reporting or Analytics GARTNER
  34. 34. an STRATEGIC TACTICAL OPERATIONAL
  35. 35. Strategic Analysis and Data Warehouse Office
  36. 36. 2012 2015 2014 data books via: website source: peoplesoft excel powerpivot via: sharepoint source: acorn tableau workbooks (student) via: tableau server source: acorn via: reporting site add: tableau workbooks (hr & financial
  37. 37. FALL HEADCOUNT BY NATIONAL STATUS
  38. 38. real-time machine data splunk traverse OPERATIONAL INTELLIGENCE
  39. 39. intelligence TACTICAL
  40. 40. are we heading W H ERE
  41. 41. not all the pieces are put together.
  42. 42. integration
  43. 43. SELF SERVICE
  44. 44. ANALYTICS MATURITY OF
  45. 45. “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” DONALD RUMSFELD
  46. 46. DATA MANAGEMENT ANALYSIS governance
  47. 47. DATA DRIVEN ENTERPRISE UNIVERSITY DEVELOPMENTS
  48. 48. CREDITS https://www.flickr.com/photos/sniegowski/8355161895/ https://www.flickr.com/photos/nichodesign/10429930073/ http://www.freepik.com/free-vector/ https://www.flickr.com/photos/officialgdc/6235163921/ https://www.flickr.com/photos/julochka/12205021584/ http://www.analyticshero.com/ https://www.flickr.com/photos/orangegreenblue/9217944381/

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