Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, emerging technologies and massive amounts of current and historical data. Predictive analytics, which will be featured in much of the research below, is a type of big data analytics that predicts an outcome by correlating the relationships of various factors. These predictions can be made utilizing a variety of organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.

Lee, Soojung

Masannat, Jonathan

English

Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, emerging technologies and massive amounts of current and historical data. Predictive analytics, which will be featured in much of the research below, is a type of big data analytics that predicts an outcome by correlating the relationships of various factors. These predictions can be made utilizing a variety of organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.)What_Ways_Can_we_Use_Big_Data_to_Offer_More_Personalized_and_Tailored_HR_Services_to_Our_Employees.pdf: 799 downloads, before Oct. 1, 2020

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What Ways Can We Use Big Data to Offer More Personalized and Tailored HR Services to our Employees?

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What Ways Can We Use Big Data to Offer More Personalized and 
Tailored HR Services to our Employees? 
Soojung Lee 
Cornell University 
Jonathan Masannat 
Cornell University 
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What Ways Can We Use Big Data to Offer More Personalized and Tailored HR 
Services to our Employees? 
Abstract 
Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-
making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, 
emerging technologies and massive amounts of current and historical data. Predictive analytics, which 
will be featured in much of the research below, is a type of big data analytics that predicts an outcome by 
correlating the relationships of various factors. These predictions can be made utilizing a variety of 
organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.) 
Keywords 
human resources, big data, HR analytics, analytics, tailored employee services, personalized employee 
services 
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Lee, S., & Masannat, J. (2014). What ways can we use big data to offer more personalized and tailored HR 
services to our employees?Retrieved [insert date] from Cornell University, ILR School site: 
http://digitalcommons.ilr.cornell.edu/student/64 
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Copyright by the authors. 
This article is available at DigitalCommons@ILR: https://digitalcommons.ilr.cornell.edu/student/64 
Cornell University ILRHR 6640: 2013-03-07 
Soojung Lee and Jonathan Masannat 
EXECUTIVE SUMMARY 
 
RESEARCH QUESTION          
What ways can we use big data to offer more personalized and tailored HR services to our 
employees? 
 
DESIRED RESEARCH          
A company wants to learn how other firms are using big data to provide better and customized HR 
services to their employees. Specific case studies and general research on the capabilities 
of big data relating to personalization are desired. 
 
INTRODUCTION TO BIG DATA         
Big data analytics—analytic techniques operating on big data—is continuing to disrupt the 
way decision-making is occurring. Instead of relying on intuition, decisions are made 
based on statistical analysis, emerging technologies and massive amounts of current and 
historical data. Predictive analytics, which will be featured in much of the research below, 
is a type of big data analytics that predicts an outcome by correlating the relationships of 
various factors. These predictions can be made utilizing a variety of organized structured 
data and disorganized unstructured data (i.e. social media posts, surveys, etc.)1 
 
CASES: REAL-WORLD EXAMPLES        
Health Services 
Collecting big data on employees’ well being can help employers develop customized 
health services. Dell launched a company program called “Well at Dell” to encourage 
healthy lifestyles by harnessing employees’ medical data. Based on medical data and 
health risk assessments, employees receive alerts and information customized to their 
health issues, incorporating their latest test results and treatments, allowing them to make 
more informed decisions. A newly diagnosed diabetic, for example, might get information 
about how to monitor blood sugar and watch out for the circulatory problems that often 
accompany the disease. A health strategy comprising of carefully selected benefits that 
target the entire workforce and catered to specific employees’ wellbeing issues can be 
hugely beneficial to staff, while providing a return on investment for employers.2 
 
Facilitating Collaboration and Simplifying Workflow Processes 
Using HR big data to simplify workflow processes and clarify points of contact is one 
method of personalizing HR services. McKinsey Quarterly demonstrates how an 
organization can foster collaboration through big data. After identifying a company’s 
functions or activities where connectivity seems most relevant, it can then map 
relationships within those priority areas. Options for obtaining the necessary information 
included tracking e-mail, social network tools, observing employees, using existing data 
(such as time cards and project charge codes), and administering short (5- to 20-minute) 
questionnaires. With the information in hand, a company can create network maps 
illustrating relationships and clarified contact points within the organization on each 
subject matter. This analysis driven by big data will help the company by eliminating 
collaborative inefficiencies and be beneficial to each employee by simplifying process 
workflow processes.3 
 
Policy Alteration 
After noticing women were departing at a higher-than-average rate, Google analyzed its 
HR big data. It discovered that new mothers were at the root of the issue. As a result, 
Google altered its maternity leave policy and reduced the attrition rate for new mothers by 
50%.4 
 
Training Programs 
Additionally, other data showed that women were promoted at a lower rate than men 
because women were less likely to nominate themselves for a promotion. Consequently, 
Google began providing workshops to encourage women to nominate themselves for 
promotional opportunities. Google also used structured and unstructured data (i.e. 
interviews, evaluations, etc.) to find the traits of good managers. With this type of 
information, firms could create customized training programs for employees to develop the 
traits employees may lack.5,6 
 
BIG DATA PERSONALIZATION CAPABILITIES      
Because of the scarcity of information on big data for the personalization of HR services, 
this section will seek to illustrate the capabilities of big data that can be tapped into and 
applied to the personalization of HR services: 
 
(1) Utilizing various data points, Target was able to create a model that predicted when a 
customer was pregnant, allowing Target to send coupons tailored to pregnant women (See 
Appendix 2). Leveraging logged data from HR, this firm can identify past behaviors, use this 
knowledge to predict future needs of services, and tailor/provide these services 
accordingly.7 
(2) One personal-line insurer uses comprehensive customer profiles, with data points that 
are constantly updated, to tailor insurance policies accordingly for each customer. If this firm 
encourages its employees to update their employee profiles as they relate to HR services 
(i.e. education schedule→tuition reimbursement and flexible work arrangements; 
illnesses→FMLA and reasonable accommodation), it can personalize its services quickly 
and notify the relevant employees.8 
(3) Big data allows firms to build scripts that proactively identify and mitigate issues 
causing dissatisfaction; they are also used to deliver campaign messages. These tactics can 
be used to follow up on employees’ requests and populate personalized information in 
correspondence.9  
 
LEVERAGING BIG DATA GOING FORWARD      
The talk about Big Data is getting louder by the minute. In order for firms to provide the 
best, tailored services possible, they must harness the power of big data analytics. 
Structured data is important but unstructured data is crucial for personalization, as it 
demonstrates the sentiments of employees. The cloud is making access to, and 
management of, such data easier. In summary, to best leverage big data analytics for 
personalization, consider the three V’s (See Appendix 3), utilize emerging technologies, 
and be creative.  
 
 
References 	  1.	  "Anatomy	  of	  the	  New	  Decision:	  5	  Hot	  Trends	  Are	  Shaping	  The	  Future	  Of	  Business	  Analytics."	  Information	  builders.	  N.p.,	  n.d.	  Web.	  1	  Mar.	  2014.	  <http://www.we-­‐conect.com/cms/media/uploads/events/328/dokumente/IB_Anatomy_of_the_New_Decision.pdf>.	  	  2.	  Sumser, J. (2012, January 17). Big Data and HRTech | HR Examiner. HR Examiner. 
Retrieved March 1, 2014, from http://www.hrexaminer.com/big-data-and-hrtech/ 
 3.	  Paterson, J. (2013, March 13). Use employee data to target health strategy. Employee 
Benefits. Retrieved March 6, 2014  
 4.	  Weiss, L., Martin, R., & Cross, R. (n.d.). Insights & Publications. Mapping the value of 
employee collaboration. Retrieved March 1, 2014, from 
http://www.mckinsey.com/insights/organization/mapping_the_value_of_employee_collab
orat 
 5.	  Miller, C. (2012, August 22). In Google’s Inner Circle, a Falling Number of Women. 
The New York Times. Retrieved March 1, 2014, from 
http://www.nytimes.com/2012/08/23/technology/in-googles-inner-circle-a-falling-number-
of-women.html?pagewanted=all&_r=0 
 6.	  Garvin, D. (n.d.). December 2013. Harvard Business Review. Retrieved February 28, 
2014, from http://hbr.org/2013/12/how-google-sold-its-engineers-on-management/ar/3 
 7.	  Duhigg, C. (2012, February 18). How Companies Learn Your Secrets. The New York 
Times. Retrieved March 1, 2014, from 
http://www.nytimes.com/2012/02/19/magazine/shopping-
habits.html?_r=1&pagewanted=all 
 
8. Brown, B., Chui, M., & Manyika, J. (n.d.). Insights & Publications. Are you ready for 
the era of 'big data'?. Retrieved March 1, 2014, from 
http://www.mckinsey.com/insights/strategy/are_you_ready_for_the_era_of_big_data 
 
 9. Smith, D. (2012). Taking Advantage of Predictive Analytics. National Underwriter / 
P&C, 116(7), 25.  
 
 
 
 
 
Appendices 	  
(1) Jewett, D. (2013). 7 Tips to Succeed with Big Data in 2014 
  
(2) Excerpt: 
“If companies can identify pregnant shoppers, they can earn millions.  
The only problem is that identifying pregnant customers is harder than it sounds. Target 
has a baby-shower registry, and Pole started there, observing how shopping habits changed 
as a woman approached her due date, which women on the registry had willingly 
disclosed. He ran test after test, analyzing the data, and before long some useful patterns 
emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues 
noticed that women on the baby registry were buying larger quantities of unscented lotion 
around the beginning of their second trimester. Another analyst noted that sometime in the 
first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and 
zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts 
buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand 
sanitizers and washcloths, it signals they could be getting close to their delivery date.  
As Pole’s computers crawled through the data, he was able to identify about 25 products 
that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” 
score. More important, he could also estimate her due date to within a small window, so 
Target could send coupons timed to very specific stages of her pregnancy. “ 
(3)	  "3Vs (volume, variety and velocity)." What is ?. N.p., n.d. Web. 7 Mar. 2014. 
<http://whatis.techtarget.com/definition/3Vs>. 
  
 
 
 


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