NHRDN Virtual Learning Session on HR Analytics
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NHRDN Virtual Learning Session on HR Analytics

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Thought Leader: Mr. SV Nathan

Thought Leader: Mr. SV Nathan

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  • Full Name Full Name Comment goes here.
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  • Attended SAS forum in India recently; however could not gather so much inputs on analytics even there, a terse presentation with great insights. Good Job and a lot of food for thoughts !
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  • So, I looked at some of the things that I faced as an HR professional….
  • I love sports and am always amazed by what happens all around and the way athletes and sportsmen grow in their lives. The IPL gave way to a greater fantasy of watching teams come together and play. In 2007, the Rajasthan Royals won the IPL. They were the unlikely winners by a long shot. There were no million dollar pinch hitters, they had an old Shane Warne who was not known to be an inspiring leader. Not sure if Shilpa Shetty had a role but, it just seemed impossible for them to win. A couple of years later I read a book by Michael Lewis called ‘Moneyball’. It was about a baseball team- Oakland Athletics, who won 20 games on trot, an unlikely event, almost impossible. But it happened. Owed largely to a Manager Billy Beane ( played by Brad Pitt). Billy was pushed to the corner because his owner did not buy expensive players, his team had lost some players to other teams and they were left with a bunch of not so great guys. Beane is committed to win and has an ambition to beat the best, in an economical way. He had to make some drastic change to his strategy. He had met a Yale grad, Peter Brand, who based on some statistics, said that there were some fantastic players available. Billy went through reams of data, agreed with his young partner and made the decision. Together they recruit a bunch of undervalued players, misfit boys, who have the potential to be match winners. Billy had to take those chances. Translating theory into practice is not easy. He did and the rest is history. Rajasthan Royals perhaps had such a story, except it was never told this way!!This story has relevance to our current state: big plans, small purse, big ambition, no money. But we have to win. No Brand, small brand, have to recruit, and the best, and win the war for talent. There are many areas where this plays out. Where do we recruit? Who do we retain? Do we have to go after all the big boys? And all of this in the most economical way!!. Organizations spend millions of dollars to make business work, and yet Talent remains their number one concern area. This webinar is about to let you know what you can do to make your CEO listen to you and get it right. Beane’s problem was that wealthier teams such as the New York Yankees,With many multiples of the A’s salary Teams such as the New York Yankees, with many multiples of the A’s salary budget, could out-bid the A’s when scouting for new talent. Beane addressed this problem with crucial insight: baseball scouts often use flawed reasoning and fallible “gut feelings” or “professional judgment” when selecting baseball players. FROM MONEYBALL TO WORKFORCE INTELLIGENCEThe story of how Billy Beane used analytics to identify undervalued baseball players has far-reaching implications for many industries. The most obvious parallel is the war for talent. Baseball is not the only domain where the stakes are high when it comes to attracting and retaining talented employees. Consider the following facts: Most companies must devote anywhere between 40 and 70 percent of their operating expenses to compensation, benefits and other employee-related expenses.8 In many domains, a rule of thumb estimate of the cost of replacing an employee is 1.5 times that employee’s salary. Finally, the business press is replete with warnings that as the population ages, the competition to attract and retaintalented workers will intensify. Yet most large organizations still make their hiring decisions using a highly labor-intensive and subjective approach often centering on subjective evaluations of candidates’ performances at interviews.
  • So, I wish to speak to the fewmost important things here:First things first:We must know what data we have that we report out.Do we draw insights from it? This is something that is key to your career…INSIGHTS.Do you have a problem statement that can be spelt out?In short:A clear understanding of the problem at hand.The ability to speak to the Business and let them know the value once there is a problem that has been solved.The greatest value of Analytics is the ability to predict the future…that is INSIGHT.Eg. I had a salary issue on hand…..and the way I asked for a 12 % hike was not what everyone was doing…..that would be blue murder !! So I erad up a lot , looked at history, drew a model of the past, and predicted that we would more likely end up at an x% in the next few years. I spoke to the CFO of the company and had my data pat on….at the end of 30 mts, the CFO was so pleased that he wondered why I didn’t ask for more, and I said that I had a Business to protect  it drew a loud laugh in him.So, let us speak of how analytics has evolved:
  • This is where it all begins : if we have Time on one axis and Value on the other, this is how it would look. We start with the basics reporting. I remember I had to report out each day of the number of workers that were in the factory, and the overall numbers that we had as our headcount. Later, I gave that reprot as a Monthly report. It occurred to me that there was a pattern of those present and it led to some analysis. And if I were to look at other plants / depts around, it would be cross functional. The final one is when I am able to predict the attendance on any particular day of the year, given the data that I have. Aah !! Now I had my bosses attention.
  • There is a new buzz word in the market : Context Intelligence !! It means that if we have all the parameters and build a heuristic model around it, it may well be that we could predict outcomes. Eg. How many of those offered would actually turn up / do well etc.Where can we use this intelliegence? Recruitment, Talent Analytics and Planning, Resource Allocation, Career Planning, Leadership pipeline, Engagement and Retention, Compensation etc.And this is not impossible, as I will show you later in the presentation.
  • I had to make a presentation to my CFO on whether the salaries in India would remain competitive for the next few years viz a vis the US. To me this was a great opportunity to show that even though we had an increase of about 10 to 12 % hikes, India would remain competitive for us, and that the actual cagr on compensation would be 4 % !!

NHRDN Virtual Learning Session on HR Analytics NHRDN Virtual Learning Session on HR Analytics Presentation Transcript

  • Talent AnalyticsThe elixir to a successful HR careerFeb 2012 - NHRD
  • Do You….• Often catch yourself saying ―I feel this is the right thing to do‖?• Still use interview conversations to predict the suitability of a potential employee with a reasonable degree of certainty?• Track attrition statistics on percentage points and feel happy if it shows a downward trend?• Have a plethora of training programs but no clear link to business impact?• Want to make an impact but did not know how to do it?…….And, If I wanted to lift my sagging career, what would I do?1 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Moneyball…….• What does one needs to do when one does not have the money muscle to get talent ?• Has a boss who shows no support for any expense !• The business and your self respect depends on an outcome that seems challenging to achieve?• And you wish to make a difference !!• THINK DIFFERENTLY and use WHAT is AVAILABLE to you…DATA In God we trust, all others bring Data – Edward Demming2 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Talent analytics – your barometer Workforce planning and deployment Talent management elements Talent sourcing and selection People/leadership development Performance management Rewards and recognition Knowledge management3 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Weather reports – track the talent radar • Those which summarize and compare operational and/or financial data on key workforce variables within defined time frames. – e.g., totals, averages, percentages, and trends. Today • Mathematical models that use multiple internal and external data sources to predict future talent events. • Those which apply one or – e.g., a predictive model two internal data sources to that uses internal and Talent derive useful information. … external employee level analytics data to predict the – e.g., the past education likelihood that a particular experience of job employee will resign in the candidates is compared to next six months and job performance during the supply the reasons for the first year of employment. prediction (e.g., long Maximize commute). performance Yesterday Tomorrow4 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Back toManagement reports Talent AnalyticsAttrition waterfall 2,161 198 9,642 10,000 8,480 801 8,000 6,000 Headcount 4,000 2,000 FY11 Attrition New starts New starts FY12 March attrition Feb5 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Back toManagement reports (cont.) Talent AnalyticsPerformance rating movements 1 0 | 0% 2 0 | 0% 3 0 | 0% 0 | 0% 0 | 0% 0 | 0% 0 | 0% Outstanding Very Very good 0 | 0% 0 | 0% good Good 0 | 0% 0 | 0% Good 0 | 0% 0 | 0% Average AverageFY09 000 000 000 000 000 000 000FY08 000 000 000 000 000 000 000 0 | 0% 0 | 0% Avg. Avg. 0 | 0%6 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Weather reports – track the talent radar • Those which summarize and compare operational and/or financial data on key workforce variables within defined time frames. – e.g., totals, averages, percentages, and trends. Today • Mathematical models that use multiple internal and external data sources to predict future talent events. • Those which apply one or – e.g., a predictive model two internal data sources to that uses internal and Talent derive useful information. … external employee level analytics data to predict the – e.g., the past education likelihood that a particular experience of job employee will resign in the candidates is compared to next six months and job performance during the supply the reasons for the first year of employment. prediction (e.g., long Maximize commute). performance Yesterday Tomorrow7 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Back toEmployee commitment Talent Analytics Human Resource (HR)/ Culture Employee Switching people practices (Drivers) (Moderators) commitment factors (Goal/Outcome) (Commitment moderators) • Service leadership • Change management • Employee communications Employee Focus • Immediate supervision Employee • Employee growth and development Commitment Switching Costs • Training and education Customer Focus • Affective • Performance evaluation Attachment • Recognition of employee • Willing to be performance Community Focus Proactive Switching Alternatives • Compensation • Intent to Stay • Benefits • Career-life fit Financial Focus • Teamwork/team management • Diversity management • Customer relationship management8 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Weather reports – track the talent radar • Those which summarize and compare operational and/or financial data on key workforce variables within defined time frames. – e.g., totals, averages, percentages, and trends. Today • Mathematical models that use multiple internal and external data sources to predict future talent events. • Those which apply one or – e.g., a predictive model two internal data sources to that uses internal and Talent derive useful information. … external employee level analytics data to predict the – e.g., the past education likelihood that a particular experience of job employee will resign in the candidates is compared to next six months and job performance during the supply the reasons for the first year of employment. prediction (e.g., long Maximize commute). performance Yesterday Tomorrow9 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Back toRetention tracker Talent AnalyticsLift chart – demonstrates the effectiveness/benefit of the retention model 40 30 20 Extremely high High attrition risk Relative attrition risk attrition risk 10 segment segment 0 -10 Low attrition risk Moderate attrition risk -20 segment segment -30 -40 1 2 3 4 5 6 7 8 9 10 Deciles Model Equation target = a+b1(tenure)+b2(commute to work)+ b3(pay)+ b4(rating)+b5(training hours)+… This equation is used to give a score to each employee.10 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Back toRetention tracker (cont.) Talent AnalyticsSolution set to provide an overview of the attrition risk in the organization Organization 4% Attrition risk projections can be analyzed at organizational level Business1 Business 2 Business 3 3% 2% 4% Attrition risk projections can be analyzed at a business level Team 1 Team 2 Team 3 Team 4 1% 2% 4% 5% Attrition risk projections can be analyzed at a team level Attrition risk probabilities are generated for each employee in the organization11 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Weather reports – track the talent radar • Those which summarize and compare operational and/or financial data on key workforce variables within defined time frames. – e.g., totals, averages, percentages, and trends. Today • Mathematical models that use multiple internal and external data sources to predict future talent events. • Those which apply one or – e.g., a predictive model two internal data sources to that uses internal and Talent derive useful information. … external employee level analytics data to predict the – e.g., the past education likelihood that a particular experience of job employee will resign in the candidates is compared to next six months and job performance during the supply the reasons for the first year of employment. prediction (e.g., long Maximize commute). performance Yesterday Tomorrow12 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Talent Analytics – A Model• What is the business issue that we are looking to address? • What kind of data is required to analyze the issue? • How do we look at the data to draw meaningful conclusions?• How are we measuring the effectiveness of these measures?• Can we measure it in $ terms and report to the business? • What insights can we draw from the analysis? • Is it possible to extrapolate past data to predict future outcomes? 13 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Analytics - The Past and TheFuture
  • Evolution of Analytics Predictive Analytics Cross process and functional Analytics Basic Analytics Consolidated reporting Data and Basic Reporting15 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Predictive Modeling DefinedPredictive modeling uses available internal and external data to predict future eventsat an applicant, employee, or claimant level. Models can be designed to predict avariety of outcomes and have broad based applications. Data Mining — a process, which utilizes a number of mathematical techniques, to analyze large quantities of internal and external data, in order to unlock previously unknown and meaningful business relationships. Predictive Modeling — the application of data mining techniques and algorithms to produce a mathematical model that can effectively predict and segment future events.16 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • The 4 Critical Modeling Questions1. Is there a compelling problem or opportunity? • What is the business case scope and size? • What are the costs, including opportunity costs?2. Do we have the data we need? • What is the state of available data and our ability to access it?3. Can we segment or predict potential outcomes and does it put us in a position to make a difference? • Is there a basis to build a predictive model? • Will the model output help us solve the defined business problem?4. Can we effectively act upon the predictive model output? • What is our change readiness and ability to implement? • Will implementation drive the required ROI? • What is our political and legal climate?17 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • What Can Predictive Modeling Do? • Predictive models use available internal and external data to predict the likelihood of future events at the customer or employee level • Models are deployed by businesses to direct limited resources to the actions that will yield the largest economic benefitFor example… Similarly… A business unit uses predictive HR can use predictive models to models to maximize ‘customer maximize ‘employee lifetime lifetime value’ value’ • Targeting new customers • Recruiting and hiring new employees • Optimizing pricing, customer service and costs • Optimizing development and performance • Focused customer retention • Focused employee retention18 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Talent Acquisition – A Case in Point Problem Statement: How do we review more qualified candidates, faster, with improved accuracy, and with less cost? 150,000 applications p.a. which results in an eligible candidate pool of 81000 Initial screening brings this number down to 8000 Further filtration occurs through multiple rounds of interviews 2100 Offers / 1675 accepted19 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Traditional Recruiting Data• Who would be the most successful and Who would be the long term employee? Sachin Saina Rahul 10 years of work experience 10 years of work experience 10 years of work experience 4 previous employers in past 10 2 previous employers in past 10 1 previous employer in the past years years 10 years Current employer is large Current company is small Current company is a microchip technology company technology company mfg. company Attended Tier I Engineering Attended Tier II Engineering Attended Tier III Engineering College College College B.E. in Electronics & B.E. in Information Technology B.E. in Computer Science Communication Engineering Society member NA Engineering Society member Limitations: • Simple set of rules comparing education level and work experience • Uniform approach across candidate base • Customary education/work experience • Difficult to differentiate people20 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Talent Analytics – Expand the data set• How long has the candidate been residing in the city?• Does he / she own a house?• Is he / she a member of any external agencies / non-profit ventures?• What is the commute time to the office?• How many promotions has the person had in the last 5 years?• What is the average compensation increase that he / she has received in the last 3 years?• Does the person belong to a Tier I or a Tier II city? (aspirations) Predictive models built from these and hundreds of other data elements can better quantify the likelihood and reasoning of future individual employee events.21 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Analytics Dashboard – Illustration Sachin Saina Rahul 20% less likely than 80% more likely than 30% more likely than average to be a average to be a average to be a Likelihood of future successful hire and stay successful hire and stay successful hire and event with the company for 3 with the company for 3 performance rated years years above average • Sub-optimal • Optimal past • Sub-optimal employment history employment history employment history • Long Commute – • Short Commute – 1 • Medium Commute – Top 3 reasons 40 miles 15 miles mile • Has been a • Owns a house in the • Has been a resident resident of this city of this city for 5 city for 2 years years • Possible pursuit - • Unlikely pursuit – • Actively pursue for Second tier Possible actions national position – (possible option for third Tier Primary Tier local/regional position)If the Predictive Analytics Model is effectively implemented, it allows scarce resources to be betterfocused, resulting in measurable benefits.22 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Building a Predictive Model
  • Predicting Attrition – The Holy GrailExample of potential model variables for active and terminated employees thatmay already be available in existing HR systems: Employment Employee Time and Compensation Performance Data specific data expense• Office Address • Home Address • Hours Worked • Salary • Performance Rating• Department • Age • Number of Training • Bonus Previous 5 Years• Date of hire • Gender Days • ESOPs • Expected promotion• Supervisor • Education Level • Vacation/Sick Days • Performance Rating date• Supervisor’s • Marital Status Taken • Recognition Awards • Date of Last Performance Rating Promotion • Number of Dependents • Date of 2nd to Last Promotion • Date of 3rd to Last PromotionExample of potential model variables from external sources: External Data Elements• GDP Growth Rate • Average salary increase• Unemployment Rate • Niche skill vs Easily replaceable skill• Number of Talent Competitors in the same city • Additional Macro Economic Variables24 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Produce Attrition-Impact Reports for Employees ILLUSTRATIVE Sample Model Input Sample Model OutputEmployee Name Saina Risk Segment HighEmployee ID 1234 Risk of Leaving 88%Location/Region Hyderabad / AP Actual Cost of Replacement $6,000Employees in Location 6,000 Expected Cost $84,480Date of Hire 01-03-2006 (Risk * Actual Cost)Rating 3 First Most Important Reason Time until for Risk of Leaving promotionTenure 5 years Supervisor’s pastBase Pay $64,000 Second Most Important retention rate is Reason for Risk of LeavingPosition Level Analyst lowWorking Hours/Yr 1,920 Third Most Important Long commute Reason for Risk of LeavingTraining Weeks/Yr 2Expected Promotion Year 2011Vacation Days Taken to Date 16Commute to Work > 25 miles Actual cost is based on performance rating, level and hiring costs. Reason codes are developed from statistically significant terms in the predictive model.25 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Key Roadblocks for Implementing Talent Analytics Roadblock Suggested Approach Lack of Sponsorship Gain support from those who derive value from the work being done. Unreliable Data Quality & Ensure data requirements and data integrity are addressed. Availability Not Aligned to Strategy Develop metrics from a very clear understanding of the company’s strategy. Not Understanding the Keep in mind the audience and what they might value. ―Customer’s‖ Needs No Accountability Set guidelines of expectations and create responsibility among people.Not Starting Simple and Small Focus on the ―vital few‖ measures that really make a difference.26 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Points for DiscussionQ1 At the current time,what are your key needs in Q3 How do you think yourterms of analysis that you needs/wishes around HRwould like to be carried out analytics will evolve, whenon your HR data, and what the economy improves?are the key outcomes andbusiness decisions that youare trying to address with Needs Toolsthat analysis?Q2 What are your views inrelation to the quality and Issues Outcomes Q4 What is your “holycompleteness of the data grail” of HR analytics thatthat you have available to you would like to carrycarry out a) basic analysis out, but don’t have theof your HR data and b) time/data/resources to domore advanced analytics on so?your HR data?27 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • Success of Talent Analytics (TA) TA = MC2 Measure X Context X CompetenceIncisive insights embedded in the right context can drive immense value!28 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.
  • What Are the Key Takeaways?• Analytics can support and drive change and can be quantified .• Analytics can assess issues objectively and consistently• Analytics can add sophistication for HR to manage talent and perception.• Analytics can serve executive leadership in a strategic advisory role• Analytics can make you look good and reboot your career 29 Footer Copyright © 2011 Deloitte Development LLC. All rights reserved.