How to Use Big Data for Better Compensation Benchmarking
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The document outlines a webinar and podcast series on using big data for compensation benchmarking, presented by Cary Sparrow, founder and CEO of Greenwich.hr. It covers the impact of data on pay benchmarking, trends in talent markets, and strategies to maximize the value of pay data for different talent segments, including current employees, job changers, and temporary workers. Key themes include reducing complexity in job pricing, leveraging new data sources, and fostering collaboration among stakeholders.
How to Use Big Data for Better Compensation Benchmarking
- 1. TalentTakeaways webinar & podcast series How To Use Big Data For Better Compensation Benchmarking Guest Presenter: Cary Sparrow Founder & CEO, Greenwich.HR
- 2. AGENDA The Series TalentTakeaways webinar & podcast series Talent Takeaways Series
- 3. AGENDAAGENDA The Sponsor Talent Takeaways Series Talent Management Made for Managers Compensation Planning Total Rewards Stay Interviews
- 4. Introduction Solutions that bring next-generation labor market intelligence to all audiences Personal • CEO and Founder Greenwich.HR • VP (HR, IT) at Cargill, Inc. • Global Practice Leader at Towers Perrin (now Willis Towers Watson) • Submarine Officer • Engineer and Geek Dad
- 5. Today’s Discussion • Setting The Stage • How the growing array of powerful data options is impacting how we need to approach pay benchmarking • A framework for segmenting talent markets based on economic drivers • What’s Out There • The current landscape for pay data, including trends, risks, and opportunities • Getting The Most Value • Approaches to help you get the most value from new and more powerful data (for the business and your comp team)
- 6. Understanding Pay Markets Then (Focal Point) Well-defined sources and processes (surveys and benchmarking) Owned by Compensation Department Highly manual Now (Many Silos) Explosion of ‘market data’ from online sources Many stakeholders owning processes that control the supply and pay of talent Still very manual Future (Integrated) A suite of data sources best suited to specific situations Tighter collaboration across stakeholders Stronger automation
- 7. Talent Markets With Distinct Economics • Talent At Rest – Current Employees • Talent In Motion – Employees Changing Jobs (typically to a different company) • Talent For Rent – Contract, Temporary, and Freelance workers
- 8. Based On A True Story • A large company was expanding operations into a new country, requiring them to add about 300 positions locally • Key management and technical positions were sourced from existing employees – pay, benefit, relocation decisions were based on existing policies • All other positions were intended to be recruited locally and paid according to published market data • 2-3 years ago, this would be the end of the discussion. And the company would have failed to launch its new business.
- 9. Based On A True Story BUT In This Case…. • Analysis of the local talent supply indicated the number of recruiters with requisite skills employed in the market was 30, and this company needed to fill 7 recruiting positions • The company adopted a more aggressive compensation approach for these positions • To manage risk, they also engaged a local staffing company to fill interim talent and recruiting needs, expecting a more challenging talent situation than they originally planned • The new site launched on time.
- 10. Talent At Rest Who Are They: Current employees Economics: Internal labor market; often very stable • Exceptions: Hot Skills, talent segments experiencing high turnover, and countries with high wage inflation or key skills shortages Typical Rewards Goals: Retention, Alignment Data Sources: Compensation surveys and online databases usually administered through compensation departments • Large survey firms • Boutique survey shops specializing in specific markets/industries • Associations • Online data sources (e.g., salary.com, Payscale.com) Strengths: • Robust analysis • Range of providers to fit budgets and desired precision Limitations: • Time delay between initial collection and final outcomes • Administrative burden • Inconsistent sampling/precision • Limitations with lower-cost providers • Excess data in some countries (e.g., US), but limited data in others Emerging Trends: • Online data services are increasingly marketing themselves for this segment • Use of Survey Aggregators (e.g., MarketPay) • Removing data complexity
- 11. Maximizing Value Of Data For Talent At Rest Main Themes 1. Reduce Complexity 2. Leverage Power From New ’Talent In Motion’ Data Simpler More Powerful Processes Simpler More Powerful Data Structures
- 12. Maximizing Value Of Data For Talent At Rest Developer Median 105K Developer – Full Stack Median 110K Developer – Backend Median 100K Developer – Frontend Median 102K Job (75th-25th)/Median △Median Full Stack Developer 31.4% 4.5% Backend Developer 38.0% 4.5% Frontend Developer 34.3% 2.9% Developer 36.2% 0% Reduce complexity of job pricing and the number of benchmark jobs Source: Greenwich.HR
- 13. Maximizing Value Of Data For Talent At Rest Reduce Complexity In Job Pricing Through A Job Catalog Job Catalog: A synchronized framework that defines a standard list of benchmark jobs that have been aligned to the company’s salary structure Impact: - Significantly reduces the complexity of market pricing - Reduces complexity of systems administration and data maintenance - Serves as a framework for other talent management processes - Breaks down silos across the organization Job CatalogJob CatalogJob CatalogJob Catalog Leveling Framework Job Family Taxonomy Salary Structure Job Catalog Fit Position Requirement s Market Data Position Pricing and Banding Catalog Structure Position Pricing Process
- 14. Talent In Motion Who Are They: Employees changing jobs (typically by changing companies) Economics: External labor market • More dynamic and subject to localized supply/demand conditions • Has a built-in premium for switching cost and in- demand experience Typical Rewards Goals: Attraction Data Sources: Online services that measure job posting data and online recruiting profile data Emerging Trends: • Significant investment in data providers for this segment Typical Data Providers: • Burning Glass • CEB • Job Boards Strengths: • Dynamic and real-time • Solutions geared for talent sourcing requirements • Powerful business intelligence capabilities Limitations: • Market data is often ‘inferred’ from posting data • Some providers use self-reported data from individuals • Very limited predictive usefulness
- 15. Maximizing Value Of Data For Talent In Motion Source: Greenwich.HR • Become familiar with the new strengths of online sources that can be applied more broadly (e.g., ability to show sensitivity of pay to skills, etc.) • For any business situation, recognize the labor market that needs to be considered and choose the data source that best fits • Strengthen partnerships with recruiting organizations and foster data transparency
- 16. Talent For Rent Who Are They: Contractor, temporary, and freelance workers Economics: External labor market for temporary talent • Very dynamic • Has premiums for administration and non- employee status • Supply chain for talent can be very complex Typical Rewards Goals: Control spending while maintaining access to critical skills Data Sources: In general the data picture for this space is immature and highly fragmented Emerging Trends: • Larger providers deliver market data based on their own client base • Vendor Management System (VMS) providers are beginning to develop their own data solutions Typical Data Providers: • Large contract recruiting firms • Large temporary staffing firms Strengths: • Larger providers are investing in their data/analytics capabilities Limitations: • Very limited data sources • No standards – therefore many competing views • Very limited matching precision • Very limited visibility – audience is typically procurement manager or operations leader and is shared situationally
- 17. The Talent Economist • Compensation professionals in particular have the opportunity to be the stewards of talent economics across the organization, e.g., • Building awareness of the ‘whole picture’ of the labor market • Advising on the interplay of decisions for each market segment on near-term and longer-term business outcomes • Integrating labor market data • Fostering collaboration across talent process owners • Demonstrating the power of an expanded set of tools
- 18. Recap • Bring perspective and use data more powerfully across all three talent segments • Talent at Rest • Talent In Motion • Talent For Rent • Become savvy accessing and appropriately using data for each of these segments • Adjust data structures and processes to simplify administration • Serve as the stewards for the ’whole picture’ and collaborate closely with other process owners
- 19. “Big Data” and Compensation Benchmarking Cary Sparrow Founder and CEO Greenwich.HR
- 21. AGENDAAGENDA Downloads Recorded Webinars Product Tour & Demo Resources & Support Talent Takeaways Series
- 22. COMPview | Compensation Planning Software Talent Takeaways Series Automate, Align & Simplify Complex Compensation Planning