the datafication of hr: graduating from metrics to analytics
DESCRIPTION
Datafication is a new term used to describe the process of turning an existing business into a “data business.” In HR it refers to our increasing ability to use Talent Analytics to understand more and more about our people, HR practices and processes, and external demographics. Global competition for talent, outsourcing labor, compliance legislation, remote workers, aging populations – these are just a few of the daunting challenges faced by HR organizations today. Yet the most commonly monitored workforce metrics do very little to deliver true insight into these topics. Leaders need to graduate from metrics to analytics, surfacing the important connections and patterns in their data to make better workforce decisions. Learn the difference between metrics and analytics, as well as key analytics and their values in these core areas: Recruiting Effectiveness Performance Talent Retention Employee Movement Total Rewards The challenges in today’s business environment require new approaches to remain competitive in an ever-shrinking world of global competition. By graduating from metrics to analytics, HR professionals and leaders can better understand the contributing factors that are impacting their organization, and take the right actions to implement programs that will provide a true competitive advantage. View the full webinar recording here: http://www.visier.com/lp/the-datafication-of-hr-graduating-from-metrics-to-analytics/ Download the companion white paper here: http://www.visier.com/lp/wp-datafication-of-hr/TRANSCRIPT
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THE “DATAFICATION” OF HR: GRADUATING FROM METRICS TO ANALYTICS
Ian J. Cook Director, Product Management, Visier
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Workforce Analytics and Planning. Smart. Intuitive. Complete.
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TODAY’S AGENDA
§ Trends Shaping the “Datafica&on” of HR § How to Graduate from Metrics to Analy&cs:
– Talent Reten&on – Recrui&ng Effec&veness – Performance – Total Rewards – Employee Movement
§ Common PiMalls to Avoid
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TRENDS SHAPING THE “DATAFICATION” OF HR
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ECONOMIC DRIVERS
Hire Right
Demographic ShiD
Retain Top Talent
Skills Shortages
Ensure Diversity
Economic Flux
Op&mize Spending
CompeKKve Pressures
more than ever before workforce insight and planning agility are crucial to business performance
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FACTORS DRIVING CHANGE: HEIGHTENED COMPETITION
“… stock market returns are 30% higher than the S&P 500, they are twice as likely to be delivering high impact recrui&ng solu&ons, and their leadership pipelines are 2.5X healthier.”
Josh Bersin, October 2013
“…have a hard-‐to-‐replicate compeKKve advantage.”
Harvard Business Review Compe&ng on Talent Analy&cs,
October 2013
“… improve talent outcomes by 12%, leading to a 6% improvement in gross profit margin, which translated into $18.9M in savings for every $1B in revenue.
CEB, Analy&cs Survey, 2013
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FACTORS DRIVING CHANGE: ECONOMIC INFLUENCE OF HR SUCCESS
“Compared with low performing companies, high performing companies..
1. Build stronger people leaders 2. Do more to a]ract and retain talented people 3. Treat and track performance with transparency”
Source: BCG, From capability to profitability, 2012
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“THE WAR FOR DATA IS ON” JOSH BERSIN, BERSIN BY DELOITTE (OCTOBER 2013)
Level 1: Reac&ve – Opera&onal Repor&ng Ad hoc, reac&onary
Level 2: Proac&ve – Advanced Repor&ng Rou&ne, benchmarking, dashboards
Level 3: Strategic Analy&cs Segmenta&on, analysis, people models
Level 4: Predic&ve Analy&cs Predic&ve models, scenario planning
Source: Bersin by Deloi]e 2013
56%
4%
10%
30%
If you are not investing in an integrated analytics capability within HR and creating a Big Data solution … you’re going to fall behind.
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BIG DATA GOES MAINSTREAM
§ Big Data has one or more of: – Volume: large, or rapidly increasing, amounts of data
– Velocity: rapid response or movement of data in and out – Variety: large differences in types or sources of data
§ Big Data lets you ask and answer ques&ons that historically were impossible, or prohibi&vely expensive – thanks for hardware and sodware technology innova&ons
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IN-‐MEMORY “BIG DATA READY” TECHNOLOGY
CPU
The “brain” Short-‐term memory Long-‐term memory Like:
Can: Do 1 billion things a second
Fetch 25 million pieces of data a second
Fetch 100 pieces of data a second
250,000 Kmes faster
It takes: 1 second 2.9 days 1 minute 25 weeks
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DEFINITIONS
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DEFINITIONS
Metrics
§ A system or standard of
measurement
AnalyKcs
§ The systema&c computa&onal analysis of data or sta&s&cs
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HOW TO GRADUATE FROM METRICS TO ANALYTICS
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RETENTION ≠ TURNOVER
§ Turnover is not sufficient because….
§ Lots of reasons people turnover – some good / some bad
§ Once someone has led it is hard to get them back
§ One number tells you nothing about how to change the outcome
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RETENTION ANALYTICS
Modern algorithms deliver a far more sophis&cated analysis of exits and provide insight into how to reduce them.
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EFFECTIVE HIRING ≠ TIME TO HIRE
FAST
GOOD
CHEAP
§ Speed is highly dependent on the market condi&ons affec&ng the type of talent being hired
§ Priori&zing speed over quality can
have nega&ve results
§ EffecKveness is not a single concept § For example, hourly paid staff vs.
execu&ve level hires
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RECRUITING ANALYTICS
Analy&cs applies powerful visualiza&on techniques to put cri&cal business answers in front of decision makers – in an intui&ve way.
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PERFORMANCE ≠ APPRAISAL PARTICIPATION
§ The change in focus for performance is the essence of the shid in HR from transac&onal to strategic
§ It is more important to analyze the impact, quality and fairness of your performance process… than to count the number of people who took part!
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PERFORMANCE ANALYTICS
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TOTAL REWARDS ANALYZED
Analy&cs are designed to provide answers to important business ques&ons like:-‐ “What caused our compensa&on budget to change in Q1?”
By providing these types of answers the business can make be]er decisions – leading
to be]er results.
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HEADCOUNT REPORTING
Business Unit Q1 2013 Q2 2013 Q3 2013 Q4 2013 Q1 2014 Sales 554 549 557 560 550 Manufacturing 1320 1314 1328 1345 1355 Services 432 430 424 420 425 R&D 45 40 44 48 40 Finance 15 15 14 15 14 HR 17 15 16 18 16 Total 2383 2363 2383 2406 2398 Forecast 2440 2420 2390 2398 2409 Difference -‐57 -‐57 -‐7 8 -‐11
This is an example of the typical headcount report. It is extremely limited in its ability to support decisions and can hide
important detail.
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HEADCOUNT ANALYZED
Analy&cs shows you the whole story related to the change in headcount. There are a total of 546 moves that make up a net change of 3.
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COMMON PITFALLS TO AVOID
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MY DATA IS BAD, I NEED TO CLEAN IT FIRST….
§ You are not alone
§ HR data is inherently “bad” and difficult to integrate
§ But you do not need to let this hold you up with analy&cs
“Our workforce data is bad, inconsistent, incomplete, constantly changing….”
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DO NOT LET BAD DATA HOLD YOU UP
§ Analy&cs is about making decisions, but not all decisions are equal
Impact of decision
Quality of data Inefficient
Risky decision
Aim for the green zone!
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DO NOT LET BAD DATA HOLD YOU UP
§ People enter data, therefore, Bad Data is a given § Aim for con&nuous improvement § Create auto-‐rules that correct common mistakes
NYC Manhattan
New York City NY Queens
Big Apple
Bronx
N. York Harlem
Midtown Chelsea Battery Park
= New York
N.Y.
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MY IT DEPARTMENT IS TOO BUSY
§ IT oden lacks the resources to support HR beyond transac&onal systems
§ Tradi&onal Business Intelligence / analy&cs solu&ons take a year+ and $1 Million+ to implement, and more to maintain
§ Look for cloud solu&ons, provided as a service, which remove the burden and cost from IT
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WE NEED TO CREATE A DATA WAREHOUSE
§ More than 50% of data warehouse projects have limited acceptance or fail (Gartner)
§ Between 70% to 80% of corporate business intelligence projects fail (Gartner)
§ The average price for a data warehouse is $2.3M (IDC)
§ The &me to implement a data warehouse ranges from 12-‐36 months
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INSTEAD OF TRADITIONAL DATA WAREHOUSE…
§ Look at cloud solu&ons that: – Use modern technologies – in-‐memory data warehouse
– Have dedicated expert resources who have implemented many &mes before
– Have a well-‐defined but flexible data model • Pre-‐built = speed, low risk • Flexible = adjust to your business needs. Change as your business changes (new ques&ons, new sources of data)