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Pt 11931 cipd conference - final-ba-mm_2

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Measure the effectiveness of Learning Technologies

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Pt 11931 cipd conference - final-ba-mm_2

  1. 1. Measuring the Effectiveness of Learning Technologies to Increase Their Business Impact Andy Wooler, Hitachi Data Systems 14th May 2015
  2. 2. About Hitachi Data Systems Hitachi Data Systems provides information technologies, services and solutions that help companies improve IT costs and agility, and innovate with information to make a difference in the world. Our customers gain compelling return on investment (ROI), unmatched return on assets (ROA), and demonstrable business impact. With approximately 6,100 employees worldwide, Hitachi Data Systems does business in more than 100 countries and regions. Our products, services and solutions are trusted by the world's leading enterprises, including more than 70% of the Fortune 100 and more than 80% of the Fortune Global 100. Visit us at www.HDS.com.
  3. 3. To Materially Improve Business Performance:  Customer satisfaction – UP  Revenue – UP  Costs – DOWN By  Linking business data to learning data  Using analytics to generate actionable insight Why Are We Doing This?
  4. 4. We Hear a Lot About “Big Data” in HR  "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.“ Laney, Douglas. "The Importance of 'Big Data': A Definition". Gartner. 2012  Variety – Not just social media but text files, email, machine log files  Velocity – Not just how quick but also data bursts  Volume – The sheer amount of data So, What Exactly is Big Data?
  5. 5. The Data Multiplier Effect at Work 4-ENGINE BOEING JET: 25,000 flights 1920TB daily TWITTER: 200M users 90M "tweets" 8TB daily NEW YORK STOCK EXCHANGE: 2.7 billion shares 1TB daily BUSINESS DRIVEN HUMAN DRIVEN MACHINE DRIVEN WHAT DOES 24 HOURS OF DATA LOOK LIKE? LARGE SYNOPTIC SURVEY TELESCOPE (LSST): Galaxy map 3200 MP 30,000 pics 3,000TB daily
  6. 6. Where Are You On This Scale? Source: http://www.forbes.com/sites/joshbersin/2013/10/07/big-data-in-human-resources-a- world-of-haves-and-have-nots/
  7. 7. Big Data or Big Insights? “It’s not the size of your source but the size of the insight that really matters.” Michael Hay, Vice President of Product Planning, Hitachi Data Systems http://blogs.hds.com/hdsblog/2012/11/title-big-data-its-not-the-size-of-your- source-but-the-size-of-the-insight-that-really-matters.html
  8. 8.  Revenue related  Number of courses delivered  Number of training hours delivered  Annual competency review data  % of staff who have completed compliance courses All of these have their place – but how many deliver actionable insights in a timely manner? What Are Our Current Insights? Some Examples
  9. 9. Challenges 1. Highly heterogeneous workforce - every learner has unique needs; need to put an end to “one size fits none” 2. We need to deliver globally, at scale, cost effectively 3. Managers should care about capability and competence, not transcripts and “bums on seats” 4. Current teaching approaches are not effective, and definitely not efficient
  10. 10. The Goal
  11. 11. Can We Address These Challenges Using:  Educational Research?  Neuroscience Research?  Computer Science Research?
  12. 12.  Computer- or mobile-based asynchronous training that adapts to the needs of EACH learner  Memory decay model built-in so knowledge is retained and not forgotten over time, using deliberate practice and spacing  Use machine learning to dynamically build an understanding of the learner’s knowledge to aid generation of new memories  Use formative testing to engage the learner to drive attention  Detailed reporting to monitor results and ensure competence Adaptive Learning
  13. 13. Adaptive Learning Engine Right or Wrong My Confidence How Long I Take How Others Did Added a white box here to cover the animated strip so it views only in the window, and centered over all art.
  14. 14. SmartBuilder from Area9Learning.com
  15. 15. Metacognition UNconsciously INcompetent Consciously INcompetent Consciously Competent
  16. 16. Student Feedback
  17. 17. The Future: Competency-Based Learning
  18. 18. xAPI – A Game Changer? Source: http://tincanapi.com/overview/
  19. 19. Questions and Discussion
  20. 20. Thank You

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