big data in hr: insight on the meaning and the opportunity

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Many companies today are talking about the opportunities associated with Big Data, but what are they doing about it? This webinar provides answers through first-hand insight on practical innovation approaches to putting todays data-rich HR environment to work. Donna Quintal, senior manager of strategic sourcing at Sears Holdings Corporation joins Glen Cathey, VP, global sourcing and talent strategy with Randstad Sourceright, to explore how companies and recruiters are exploring vast stores of human data capital, including that found on job sites, social media and other sources, to find, attract, retain, and promote best-in-class employees.

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  • Big Data in HR: Insight on theMeaning and the OpportunityDonna QuintalSenior Manager of Strategic SourcingSears Holdings CorporationGlen CatheyVice President, Sourcing andRecruiting Center of ExpertiseRandstad Sourceright
  • 2Agenda The Moneyball phenomenon What do we mean by Big Data? The Opportunity Big data in action: Moneyball recruiting Creating real workforce intelligence Wrap up: transforming HR and changing the conversation
  • 3The Meaning
  • 4Moneyball: The Art of Winning an UnfairGame, a book by Michael Lewis about the OaklandAthletics baseball team, its general manager BillyBeane and his assistant Paul DePodesta premise: the collected wisdom of baseball insiders(including players, managers, coaches, scouts, andthe front office) over the past century with regardto player selection is subjective and often flawed.Moneyball
  • 5The Oakland As didnt have the money tobuy top players, so they had to findanother way to be competitive.Billy and Paul took ananalytical, statistical, sabermetric*approach to assembling their team, pickingplayers based on qualities that defiedconventional wisdom and the beliefs ofmany baseball scouts and executives.Moneyball*Sabermetrics is the specialized analysis of baseball through objective evidence, especially baseballstatistics that measure in-game activity. The term is derived from the acronym SABR, which stands forthe Society for American Baseball Research.
  • 6In 2002, with approximately $41million in salary, the Oakland Aswere competitive with larger marketteams such as the New YorkYankees, who spent over $125million in payroll that same season.They finished 1st in the AmericanLeague West and set an AL record of20 consecutive wins.Moneyball
  • 7Much of what is accepted assourcing, recruiting, interviewingand hiring, and talentmanagement best practices todayis largely based uponconventional wisdom - ideas orexplanations that are generallyaccepted as true.However, the problem with anyconventional wisdom is thoughthe ideas or explanations arewidely held, they are also largelyunexamined and untested, andthus not necessarily true.MoneyballThe Moneyball approach a real opportunity forcompanies today
  • 8Analyzing massive data sets (30K 100Kemployees), Evolv has identified undervaluedcharacteristics and discovered non-intuitiveinsights, such as: For hourly workers, people who fill out onlineapplications with 3rd party browsers (Firefox orChrome) rather than IE perform better andchange jobs less often For call center employees, people with acriminal background actually perform a bitbetter than those who do not, and "jobhoppers" are no more likely to quickly quitthan those who have stayed in previous jobsfor long periods of timeMoneyballSource: The Economist, Robot Hiringhttp://www.economist.com/news/business/21575820-how-software-helps-firms-hire-workers-more-efficiently-robot-recruiters
  • 9A large financial services firm believed that employees with goodgrades who came from highly respected universities made goodsales performers.MoneyballSource: Forbes, Josh Bersinhttp://www.forbes.com/sites/joshbersin/2013/02/17/bigdata-in-human-resources-talent-analytics-comes-of-age/Sales productivity andturnover analysis wasperformed for newsales employees overtheir first 2 years ofemployment andcorrelated with totalperformance andretention againstvarious demographicfactors.
  • 10Big Data
  • 11What Big Data IsWikipedia claims that "Big data is a term applied to datasets whose size is beyond the ability of commonly usedsoftware tools to capture, manage, and process the datawithin a tolerable elapsed time.""Big data sizes are a constantly moving target currentlyranging from a few dozen terabytes to many petabytes ofdata in a single data set.
  • 12What Big Data IsOther sources attempting to define big data include "thetools, processes and procedures allowing an organizationto create, manipulate, and manage very large data sets"Regardless ofdefinition, the bigdata concept centersaround huge amountsof data that are notonly increasing involume, but also invelocity and variety.
  • 13Data VolumeSource: Mashable
  • 14The data velocity aspect is the speed at which new data isgenerated. One example of the increasing velocity ofhuman capital data would be social media posts/updates.For example, Twitter crossed the 400,000,000 tweets/daymark on March 21, 2013 - thats 2.8 billion updates everyweek!Data Velocity
  • 15Human Capital Data: ATS CVs LinkedIn, Facebook, Twitter, Google+, etc. profiles and updates Youtube, Quora, Flickr, Github, Stack Overflow, etc. Mobile check-ins and updates Recommendations/awards/endorsements Blog posts and comments Press releases/announcements and much, much more!Data Variety
  • 16Big Data & AnalyticsMany people use the term "big data" when theyre reallyreferring to analytics.Big data refers to data sets that are typically high in volume,variety and velocity. A large volume of data doesnt qualify as"big data" unless the other attributes are present velocityand variety (structured and unstructured).Analytics is the discovery and communication of meaningfulpatterns in data, which can be achieved with any data set.Correlating employee performance and retention data withdemographic data or assessments is an example of analytics,but not "big data."
  • 17The Opportunity
  • 18Big Data in Action: How can the Moneyball approachimprove your competitive edge in talent acquisition?A few ways we could apply the Moneyballconcept/analogy to talent acquisition:1. Assessing Talent: Moving away from usinglargely subjective means of assessing talent andmaking hiring decisions to more objective, factand empirical data-based means2. Out-recruiting Traditional TalentAcquisition: Identifying and acquiring top talentlooking for traits, experience, accomplishmentsand information overlooked by traditionalrecruiting and assessment methods3. Looking in New Places: Challengingconventional wisdom of what top talent looks likeand where it comes from (Ivy league schools, highG.P.A., certifications, M.B.As, experience atcertain companies, etc.)
  • 19Big Data in Action: What Could Moneyball RecruitingLook Like?Talent Competitive Edge (contd.):4. Real Measures of Performance:Developing objective performancemeasurements that are relevant acrossany role, responsibility, company, andindustry and that stick with each personas they move through their career, similarto a credit score5. Secret Sauce: Individual companiesdeveloping secret sauces for sourcing,analyzing and evaluating potential hiresbased on their own data and factualstatistical analysis of the makeup of theirideal hire and employee
  • 20Can you answer? How many current employees areretiring in 2013? How many current employees areunder preforming? What companies provided your topand bottom performers in 2012? What skills do current incumbentshave in common with one another? What are each managers 360Leadership scores or rank?
  • 21Moneyball in Action: What data should be shared?Personnel Data Education Level/SchoolOutside Work History 360 Leadership ScoresTalent Management Mobility Review ScoresBusiness Results SkillsInfluenceProactivePredictiveTransparency
  • 22Moneyball in Action: Getting the DataTalent Management Data, HRIS, & ATSData WarehouseCreateReportingShareAnalyticsMakeDecisionsTakeActionShowValue
  • 23Gaining Details of Competitive Hires2.5 & Below Below AVG 3.2 & Above Above AVG Grand Total Total RankCompetitor #1 10 16.13% 6 9.68% 62 -6.45%Competitor #2 2 5.00% 16 40.00% 40 35.00%Competitor #3 6 15.38% 8 20.51% 39 5.13%Competitor #4 8 24.24% 13 39.39% 33 15.15%Competitor #5 9 31.03% 11 37.93% 29 6.90%Competitor #6 2 7.41% 6 22.22% 27 14.81%Competitor #7 6 23.08% 10 38.46% 26 15.38%*Example Only Data Invalid
  • 24Example of Skills and Competencies by Position*Example Only Data InvalidManaging Hourly TeamsMicrosoft Word or equivalentManaging Salaried TeamsMicrosoft Excel or equivalentDelivering/facilitating training to othersServing as a MentorManaging a P&L statementTurning around a Poor PerformingMicrosoft PowerPoint or equivalentDelivering formal/informal presentations to various audiences
  • 25What Schools Did Top Performers attend?*Example Only Data InvalidUniv of South Alabama ALFlorida State University FLUniv of Iowa IAFlorida A & M University FLPennsylvania State University PA
  • 26

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