An introduction to HR analytics

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human resource analytics is a new and fast growing industry. This presentation tries to introduce the reader to a few terms.

An introduction to HR analytics

  1. 1. HR Analytics and Everything you wanted to know about
  2. 2. Organizational Analytics Marketing Analytics Financial Analytics Operations Analytics HR Analytics ?
  3. 3. HR Analytics drawback of spreadsheets http://blog.revolutionanalytics.com/2012/11/using-r-in-the-human-resources-department.html There is a lot of data out there and it’s stored in different formats. Spreadsheets have their uses but they’re limited in what they can do. The spreadsheet is bad when getting over 5000 or 10000 rows – it slows down. It’s just not designed for that. It was designed for much higher levels of interaction. drawback of traditional modes BUT ----HR Team is rarely trained in analytics
  4. 4. HR Analytics sourcing performance & compensation attrition
  5. 5. HR Analytics sourcing he use of assessment tools to “pre-assess” a candidate’s potential to be successful within a specific role within the organization. These tools are already providing a “predictive” look at the candidate skills and abilities by modeling their responses against the best scenario for success http://www.sas.com/knowledge-exchange/business-analytics/innovation/analytics-creates-a-healthier-workforce-%E2%80%A6- and-bottom-line
  6. 6. HR Analytics performance and compensation http://www.hrexecutivecircle.com/pdf/SAS_HCM_White_Paper.pdf and http://www.payscale.com/compensation-today/2009/11/the-value-of-compensation-analytics and http://www-03.ibm.com/software/products/en/cognos-incentive-compensation-management
  7. 7. HR Analytics attrition also known as Predictive Retention Modeling http://www.hrintelligenceblog.com/en/?p=658 Track and analyze critical skills, and predict which skills will be lost and when by predicting turnover.
  8. 8. HR Analytics Providers http://hexaware.com/hr-analytics-services.htm http://www.accenture.com/us-en/Pages/service-human- capital-workforce.aspx https://support.sas.com/software/products/hcm/index.html Training on HR Analytics ? http://jigsawacademy.com/lp/2014/HR/
  9. 9. HR Analytics HR Analytics: Driving Return on Human Capital Investment http://www.oracle.com/us/solutions/ent-performance-bi/045039.pdf Studies show that companies that use HR analytics have: ● 8% higher sales growth ● 24% higher net operating income growth ● 58% higher sales per employee
  10. 10. Case Studies Trendwise Analytics -Banglore India http://www.slideshare.net/TrendwiseAnalytics/trendwise-hr-analytics
  11. 11. Case Studies Google - Project Oxygen http://www.nytimes.com/2011/03/13/business/13hire.html?_r=0 1. Be a good coach. 2. Empower; don't micromanage. 3. Be interested in direct reports, success and well-being. 4. Don't be a sissy: Be productive and results-oriented. 5. Be a good communicator and listen to your team. 6. Help your employees with career development. 7. Have a clear vision and strategy for the team. 8. Have key technical skills so you can advise the team.
  12. 12. Case Studies Google - HR an HBR Case Study http://hbr.org/product/google-s-project-oxygen-do-managers-matter/an/313110-PDF-ENG and http://www.forbes.com/sites/meghancasserly/2013/07/17/google-management-is-evil-harvard-study-startups/ People Analytics @ Google http://www.amcham.ie/download/Helen%20Tynan%20AmCham%20talk%2031102013%20(1)%20(1).pdf http://googleresearch.blogspot.in/2012/06/hello-sciencemeet-hr.html Google People and Innovation Lab (Google Pi Lab)
  13. 13. Additional Links https://www.nordlab.co/pages/people_lab
  14. 14. Case Studies Using R in the HR http://blog.revolutionanalytics.com/2012/11/using-r-in-the-human-resources-department.html R gets over the limitations of spreadsheets: In the business world we really don’t need to know every row of data, we need to summarise it, we need to visualise it and put it into a powerpoint to show to colleagues or clients. http://blog.hrtecheurope.com/2012/08/more-r-in-hr/ The great thing about R is that it cuts down the software budget to zero, and with GUIs cut down training to weeks. You can quickly move from spreadsheet world to being able to build models and predicting outcomes.
  15. 15. Analytics Techniques Used (mostly) Decision Trees - for segmentation http://www.statmethods.net/advstats/cart.html Regression -attrition http://www.statmethods.net/stats/regression.html Data Visualization - including spatial and interactive Trend , Outlier and Patterns (TOP)
  16. 16. From a Google HR ppt Googled Thanks
  17. 17. Thanks compiled by Decisionstats contact https://www.linkedin.com/in/ajayohri

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