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

1. HR Analytics
and Everything you wanted to know about

2. Organizational Analytics
Marketing Analytics
Financial Analytics
Operations Analytics
HR Analytics ?

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. HR Analytics
sourcing
performance
& compensation
attrition

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. 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. 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. 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. 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. Case Studies
Trendwise Analytics -Banglore India
http://www.slideshare.net/TrendwiseAnalytics/trendwise-hr-analytics

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. 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. Additional Links
https://www.nordlab.co/pages/people_lab

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. 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. From a Google HR ppt Googled
Thanks

17. Thanks
compiled by Decisionstats
contact https://www.linkedin.com/in/ajayohri