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Quantifying HR: Basics of HR Analytics

David B Turetsky 15 May 2015

© Copyright 2015 ADP, LLC. Proprietary and Confidential Information.

Contact Information

David B Turetsky, HCS

ADP, Product Management

Vice President, Chief Product Officer, ADP DataCloud

!Cell: +1 (508) 287-3474

Email: [email protected]

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mailto:[email protected]


About David B Turetsky

As the Vice President, Chief Product Officer, ADP DataCloud, David is responsible for developing products and services to deliver innovative products and services for ADP clients. He provides vision, leadership and strategy for ADP’s efforts around Big Data.

David has more than 25 years of experience in HCM, HR, HRIS, Talent Management, Compensation and Big Data as an analyst, manager, executive, consultant and entrepreneur. His passion is manager empowerment and employee engagement through total rewards management. It has been the topic that he often presents to large audiences at national HR conferences such as HR Technology, SHRM, PIHRA, and APA. In addition, David has been published in WorldatWork Journal, HR Executive magazine and other industry publications.

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Goal for this presentation

• Understand the business environment we are operating in

• Common understanding of the problem Quantifying HR today

• Drive practical examples of how HR can bring value to the business

• How we can help our organizations prepare for this challenge

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Agenda

• Trends affecting HR

• Small Data/Data/Big Data

• Strategy Supported by Data

• Business Intersection with HR Strategy

• HR Driving Business Value

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Trends affecting HR

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ADP Pro User Conference


Taking Control of Healthcare Reform

Making Smart Investments in People

Personalization of Employee Experience

Will you provide benefits? Will you limit hours? Do you have CDHP and wellness? Will you outsource?

Do managers have the right tools? Are you retaining top talent? What is your comp strategy? Do you have succession plans?

Do you have the right tools? What is your social media strategy? Can your culture support personalization? Do generations demand differentiation?

The Cause for Change

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Trends in the Marketplace

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Business Leaders“ ”Data is driving our businesses to differentiate from competitors where margin and cost pressures are forcing innovation from every conceivable direction.

Sales“ ”Better targeting of sales pursuits to those individuals who search for our products, go to our websites, and participate in our webinars.

“ ”Clients are no longer satisfied with receiving tables of data or transactional reports showing administrative-‐focused information.

HR Leaders

What have we heard?

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Small Data/Data/Big Data

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Data

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Data Strategy

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Data Scientist

Job Description

As a member of a Business Intelligence and Analytics Team, you will analyze customer data and build high-end analytical models for solving business problems, such as customer interaction, behavior, portfolio, etc. The duties will include data preparation & analysis using statistical software. The candidate should be able to interpret the results in a clear and concise format to be presented to the Senior Executives in business terms.

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Essential Qualifications

At least 5 years of experience such as analyzing data and/or building analytical models using SAS. Master's degree in statistics, mathematics or computer science. Demonstrated ability to apply statistical or data mining techniques to solve business problems. Ability to extract and prep data from relational databases, Excel and text files for use with SAS Software. Thorough knowledge of supervised and unsupervised modeling techniques. Ability to work independently in all aspects of data modeling, from sourcing through final presentation of the findings. Experience with SAS Enterprise Guide. Ability to work with the business users and IT. Excellent verbal and written communication skills. Ability to communicate with people of various technical and business backgrounds, including the ability to explain difficult technical concepts in simple terms to business users. Ability to work independently and with a team environment.

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Small Data Spreadsheets

Word Documents Access Databases

Data Order processing

CRM General Ledger

Big Data Linkages

Visualization Tools

ValueIntegration

SAVED LOCALLY SAVED CENTRALLY REFERENCED COLLECTIVELY

Business Data Systems

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Integration and Value

By themselves, the data are transactional “receipts” from processes and not intended as measurements of the transaction:

• Business plan

• Presentation to potential clients

• Expense reimbursement

• Sales transaction

• Cost of Goods Sold

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Small Data Compensation Spreadsheets Performance Evaluations

Succession Planning

Data HRIS Payroll

Benefits System

Big Data Scorecards Dashboards Analytics

ValueIntegration

SAVED LOCALLY SAVED CENTRALLY REFERENCED COLLECTIVELY

Human Resource Data Systems

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Recruiting Requisitions Candidates Time to Hire Applications

Learning Course Attendance

Enrollments Development Plan

Progress

Performance Goals

Goal Percent Completion Evaluation Completion

Compensation Budgets Guidelines Spend

Red Circles

Terminations # of Voluntary/Involuntary Total Terms Term Reasons

Succession Successors

Candidate Diversity Successor Readiness

Common HR Processes and Data

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HR Data Challenges

• HR Data is a collection of transactions

• HR, Finance, Supply Chain, and CRM capabilities have specific missions out of sync

• Financial Tracking

• Record Keeping

• Service levels

• Inconsistent data definitions, vocabulary, and mental models

• First exposure of data opportunities provides a glimpse into the abyss

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The value of core HR Programs and Processes are often eroded by, rather than supported by these activities.

How HR is involved in a Business Crisis/Change

Major HR Projects are often driven by business needs while under severe time pressure

• “…integrate 500 acquired associates.”

• “…reduce our people costs by 5% within six weeks.”

• “…consolidate sales, marketing, and product teams.”

• “…reengineer our service delivery model.”

• “…relocate our plant…”

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Business Intersection with HR

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Business Strategy Measurement

• Scorecard Metrics

• Earnings Per Share

• Net Income

• Earnings Before Interest Tax Depreciation and Amortization

• Metrics are tracked over time and major changes and/or variances against plan are examined

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Effects of a change in business strategy

• Communications to groups affected by the change

• Systems, processes, philosophies and operating plans change

• Operational Changes

• Finance changes

• HR changes

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HR Strategy Measurement

• HR Scorecards

• Turnover

• Engagement

• Employee Performance

• Metrics were tracked over time and major changes examined

• Did this result in changes to HR Process or Strategy?

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HR Scorecard/Dashboard

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Talent Management Process and Data Issues

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Recruiting Learning Performance Succession Compensation

Where are “successful” employees found? !

Are employees taking the right courses?

How can ratings be calibrated across a business/the organization?

Do we have the right people identified as successors?

Are we compensating the right people?

What skills does a “successful” employee exhibit?

Can we better leverage our learning investments?

Where are concentrations of lowest performers?

What key positions do not have successors identified?

Do we pay too much for certain roles?


HR using metrics

• To move away from “Reporting” metrics and towards more useful business support:

• Educate HR on business metrics meaning and impact

• Facilitate discussions with business to stay current on changes to strategy

• Provide correlations between HR Metrics and the Business Strategy

• Redevelop processes to align with a focus on alignment, improvement and measurement

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HR and Business Strategy

Alignment of HR Process with Business Strategy:

• Review the goals of the organization

• Test for alignment

• Redevelop guiding principles of HR to match business strategy

• Goal to be that Talent Strategy should drive the business forward

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Strategy Supported by Data

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Applied to the business world

Analyze the results

Observe and measure sample of the population

Make a hypothesis

Infer conclusions from the results

Develop a business strategy

Observe and measure the effect of the strategy

Analyze the strategy

Infer the effectiveness of the strategy and potentially change strategy

Scientific Method

Scientific Method

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INCREASE MARGIN

REDUCE COST

INCREASE PROFIT

Key Business Metrics

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Increase Margin

Reduce turnover

Introduce safety training Reduce

skill gaps

Measurement Driving Business Strategy

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Measurements: • Turnover

– Year-over-Year – Include industry and

company benchmarks • Safety Incidence

– Year-over-Year – Include industry and

company benchmarks • Competency Gaps

– Highest gaps/strengths – Organizations with biggest

gaps



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Skill Improvement

Measurement

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Skill Improvement

Observations

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Analysis

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Draw conclusions

Our hypothesis was that to increase margins, we should reduce turnover, increase safety training, and reduce skill gaps.

Through observation, we can infer that this may work, if we focus our efforts on…

• Improving the skills of our current workers

• Hiring workers with better skills

• Using contractors to fill immediate gaps

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Reduce Costs

Hire different skills Increase

manager training

Lower time to hire


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Measurements: • Time to hire

– Period-over-period – Include industry and company

benchmarks • Number of New Hire Terms

– Organizations with losses – Average Skill Profile of Loss/

Success • Courses attended by Managers

– Performance – Employee and Group

– Engagement Scores


KPIs Turnover Net IncomeCustomer Satisfaction

Impact of change

Methods Measurements

Safety Incentives

Lower Higher Higher Medium

Add goal to "Manage Costs" Medium Higher Medium High

Reduce Merit Budget

Higher Medium Lower Medium

Increase manager training

Lower Lower Medium Low

Reducing Costs

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Increase Profits

Retain key staff/Hi-‐Pos

Incent Innovation

Improve Performance

Mgmt


Measurements: • Turnover of Key Talent/Hi-

Pos – Period-over-period – Include industry and

company benchmarks • Incentives Earned

– # of Patents – P&L of Business

• Ratings Distribution – Performance –

Employee and Group – Engagement Scores

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We have a strategy, now we measure it

• Constantly monitor criteria established during the business case/decision

• Make course corrections and measure their impact on the goal and strategy

• Measure again at a future time and reexamine the goal and strategy

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What do I do with this?

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Start to jot down what you would ask

• Do happy employees translate to more productive employees?

• Can compensation growth be slowed without loss of performance growth?

• Do “better” managers make for higher employee engagement scores?

• What leading indicators best predict successful employees in our company?

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Deconstruct the question…

What leading indicators best predict successful employees in our company?

• Definition of a “successful employee” • Construct the hypothesis “We can predict who is a successful

employee by looking at background information we already collect in pre and post employment circumstances.”

• Collect data: Performance scores by employee historically, Warnings or other documented Employee Actions, Promotions, job changes, Schools attended, Skill gaps, Absence/Lateness data, Manager changes, Department profits, etc.

• Derive the “successful employee” variable as the dependent variable

• Find co-linear or data that answers the same question twice • Run analyses to show the variables that best predict “successful

employees” • Conclude analyses with a model of how a successful employee is

predicted • Announce the results and test methods of replicating the

successes in future hiring and promotion activities

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Ten Steps to use this information

• Learn what your business does by reading publicly available information (proxy/10-K/Annual Report) or by asking the owner/leaders - parters or mentors

• Do independent research on your industry • Discuss with your leaders current issues • Take on-line courses on Data Science, HRIT

managers/leaders get a certificate on Data Science

• Interdisciplinary internal corporate discussion regarding data with the goal of developing a Big Data corporate model

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Ten Steps to use this information

• Get on the team and lead discussions of how HR should lead, not just “help” the Big Data initiatives - settle for being on the team

• At every chance, rebuild HR processes to consider measurable business outcomes

• HR data systems should be enhanced with the capabilities to support the business changes

• Proactively develop models for future growth, change and alternative business scenarios in order to provide appropriate support for business strategies

• Continually pursue excellence in providing outstanding customer service through data

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Five “easy” Steps to successful HR Data Science

• Wear a lab coat - dress for the job you want • Data Science for Dummies • Ask deeper questions when a report request

comes in… Think about how you use data and can make suggestions about structuring an analysis

• Listen to what is going on in your organization • Make suggestions about how you would see data

working better based on what you hear/know

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In Conclusion…

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Summary

• Educate HR on Business metrics

• Talent and HR processes are considered Business Processes

• HR providing Stakeholders with appropriate data to make good BUSINESS DECISIONS

• Think like a data scientist and finding relationships in data to solve real business problems

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Thank youADP Pro User Conference

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