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Big Data and Predictive Analytics Sept 2014 Helsinki #AgileHR


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Agile HR

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Big Data and Predictive Analytics Sept 2014 Helsinki #AgileHR

  1. 1. 1 Predictive Analytics for HR and Recruitment Aki Kakko Co-founder, Head of Product 3rd of September, 2014 Helsinki
  2. 2. Introduction 2 Aki Kakko Serial Entrepreneur Co-Founder, Head of Product, Joberate • 2010 a recruitment agency that was used as a platform to explore scalable business opportunities within the recruitment industry • 2011 spin-off of the social job advertisement service that is now operating as an independent company under Candarine ( brand • 2014 spin-off of Joberate ( - Predictive Analytics for HR and Recruitment • Partner of a globally operating HR event company GlobalHRU (www.globalhru) & HRTechTank (
  3. 3. Two quick words about our company 3
  4. 4. Two quick words about our company 4
  5. 5. A secular shift has occurred, data is now everywhere Companies need to track external people data, in addition to their HRMS data Attract people to follow you Start following interesting people attract talent to take interest take interest in talent Age of corporate dominance Age of knowledge workers 5
  6. 6. Big Data has become a disruptor for HR A constantly evolving data stream that is “external” to current HRMS, holds tremendous potential I think      I know (current state) (future state) Investment Flow 6
  7. 7. So, we must start with understanding Big Data? • Not looking for a needle in a haystack (that’s easy…can you spot it?) - Looking for a unique piece of hay in hundreds of millions of haystacks • Differs from tradition data in three main ways (four V’s) 7
  8. 8. 8 Source: IBM
  9. 9. 9 Source: IBM
  10. 10. 10 Source: IBM
  11. 11. 11 Source: IBM
  12. 12. Predictive Analytics increase value of HR services 12 Predictive Analytics • Predictive models (i.e. credit score, life events) • Probability of events and/or their timing Data Analysis • Statistical analysis, and relational models • Understanding cause and effect Dynamic Reporting • Aggregate view of data sources • Benchmarking or validation (Traditional) Reporting • Measure results • Efficiency, compliance • What can happen? • What is happening now? • Why did it happen? • What happened? Extracting value from Big Data
  13. 13. Non-HR example of a Predictive Analytic 13
  14. 14. Q&A 14 Example business problems predictive analytics can help with…
  15. 15. HR related: • Likelihood that someone will be a successful employee? - Prediction of high performers for our organization / team - Forecasting how competences we have meets the future needs • Understanding people’s job seeking behaviors so that you can intervene and retain potential leavers - Ideal time to promote someone? • Health and stress level of our people, trends and forecasts • What could be good team combination? • What drives innovations in the company? - What motivates people? 15
  16. 16. Recruitment related: • What is the ideal time to contact someone with a job offer? • What are the best sources of candidates for specific roles? • Automating matching of jobs with relevant CV profiles • Developing an ideal job description that will generate interest • How and where do we get more engaged with potential candidates? • Who is attracted to us compared to the competitors? • Likely length of employment? • How to attract for diversity? • How do I identify team players? 16
  17. 17. Individual level: • How can I be more successful, motivated, happy, healthy? - What success means for me? • How do I best “trick” the system? • How do I collaborate better? • What competences are needed in the future and I should develop? 17
  18. 18. Q&A 18 Opportunities are only limited by our imagination…
  19. 19. The Predictive Analytics lifecycle 19 Complements of the SAS Institute Source: SAS Institute
  20. 20. How predictive analytics works • Aggregate, input, scrape, import, or track information sources Information (could be Big Data) Machine learning • Makes decisions based on previously validated outcomes • Learn new outcomes that will be used in future decision making • Feed/output data to visualization or rendering software • Archive decision results for future query Display predictive analytics 20
  21. 21. Overall technology hierarchy 21 Client HRIS or recruitment systems Client’s User Interface variations API and Web Services Joberate machine Data validation services learning predictive analytics engine (further explained on next slide)
  22. 22. Data validation services simplified 22 Data validation services
  23. 23. Some practical examples Analyze any number of variables to understand employee job seeking trends Analyze trends in specific groups Trends view instantly shows how actively your employees are looking for work, over a period of time from three months to five years. Quickly and intuitively identify cyclicality or seasonality to job seeking behaviors, and correlate data to other company initiatives. 23
  24. 24. Some practical examples Support Workforce Planning by analyzing attrition and retention rates based on job seeking behavior Monitor workforce development plan The inclusion of analytics into a workforce planning initiative are essential to mapping the most accurate current workforce profile of any organization. 24
  25. 25. Q&A 25 Business case examples
  26. 26. • The average cost of replacing an employee is 29%-46% of salary • At a wage of 30k€ per annum, cost to replace is 9-12k€ • Average attrition of 8% across 3,000 employees equals 240 leavers - Cost to replace 240 leavers x 10k€ is 2.4m€ - Cost of predictive analytics software per annum 30-80k€ 26 Reduce voluntary attrition
  27. 27. 27 Reduce recruiting costs • Most of (outbound) recruiters/researchers time is spent talking with candidates who are not ready to make a move • Calculate avoided time (or people) x cost = savings
  28. 28. Q&A 28 Remember, CFO’s care about €’s not promises 
  29. 29. 29 Thank you! Questions, comments?
  30. 30. 30 Aki Kakko, Head of Product [m] +44 7887 473424 [e] [t] @akikakko