Guide to People Analytics

People analytics, HR analytics, talent analytics or workforce analytics?

People analytics was and is sometimes referred to as HR analytics, talent analytics or workforce analytics.

Up until about 2014, these various terms were somewhat interchangeable - used by early vendors trying to mark space and name the segment. It was at this time industry analyst heavyweight and long-term proponent of data-driven HR, Josh Bersin, weighed in and colloquially anointed the space People analytics.

While the people analytics category of HR tech was emerging in the 2012-2015 period, data-driven HR had been around for a while but having a people analytics function was still pretty much an anomaly within most organizations. During these early days, when people analytics was part of HR it was typically played out as a niche (and often one-off) activity by individuals with technical skills and an interest in data. People analytics was growing but it was not yet a widely practiced discipline.

What was happening during this time however, was the coming together of a number of influences that helped make people analytics an imperative for HR:

• People data was starting to be considered of much higher value in organizations given the war for talent, the high proportions that we spend on “human resources”, and that CEOs were becoming more and more convinced and vocal that people were/are their most crucial asset.
• Sources of people data (candidate and employee) were rapidly expanding and this data was more accessible due to cloud-based apps being bought by HR, in addition to more advanced data management, integrations and APIs.
• HR tech vendors jumping on the bandwagon of analytics claiming they offered people analytics solutions.
• Nearly every HR tech vendor started to claim they had people analytics, big data and predictive in an effort to pump valuations and ride the marketing wave but caused tremendous noise and marketplace confusion. In reality, most of them didn’t have anything beyond single dimension reporting on the data which their transactional system generated - a far cry from people analytics. All but a few of these claimants were transactional HR systems and therefore, did not employ one of the most crucial components of any people analytics technology - an HR-specific data warehouse for optimizing the complex structure of multi-sourced HR data. Here’s an article about the differences between simple reporting delivered within a transactional HR system, and true people analytics.

The result is that HR started to have more data and more systems - but they weren’t able to move beyond single-dimension reporting on the data isolated in their transactional systems.

This leads us to what we call the universal problem in HR.

HR Metrics Versus People Analytics

We must move beyond HR Metrics and into People Analytics

While this may be up for discussion, at PeopleInsight we believe HR metrics can be characterized by single dimension and simple measurements about HR practices, processes and transactions. 

HR metrics is really for HR. People analytics is for the business-at-large.

HR metrics is about HR efficiency. Which is all good, but it needs to go further.

So the next level, and consistent with our definition, is people analytics - which is more about measuring the outcomes which HR, organizational, and talent practices, processes, programs and transactions generate.

People analytics therefore, is more about HR effectiveness - which leads to a very important, and foundational principle:

For example to measure and understand recruitment processing like time to fill, you can adopt some simple HR metrics.

However, to understand which recruitment channels are your best source of a quality hire and what is the cost of a quality hire, you will need data to be combined from various sources and delivered as people analytics.

You’ll need the ability to generate more business-relevant insights through deep segmentation, arithmetic or statistical analyses, and enabling an analyst to view the data from multiple lenses or dimensions (for example, show quality of hire by location, role, level, cost, recruiter, hiring manager, source, assessment tool used, assessment results, etc.).

Bottom line, if HR wants to be more of a business partner, value-creator and key enabler of corporate strategy, we must move beyond HR metrics and into people analytics.

The People Analytics Industry - Latest Independent Research

Just Released: Seminal Research on the People Analytics Technology Landscape

Throughout 2019, industry analyst veterans and experts from RedThread Research, Stacia Sherman Garr and Priyanka Mehrotra, conducted the market’s first comprehensive research study across the people analytics landscape generating two final reports; one focused on the people analytics technology market at-large, and the other focused on the key people analytics technology vendors.

This, by far, is the most comprehensive, unbiased and independent research conducted to date (i.e. not paid for by any vendor). 

Not only is it independent, but the former Bersin By Deloitte analysts take leadership in the effort to categorize the space. 

The study engaged all key vendors in the people analytics space with the objective of muffling the noise, understanding and seeing first-hand what these products do and don't do, and eradicating the cross-category confusion.

This is especially important because the emergent people analytics space has been muddied by far too many HR tech marketing and product management teams claiming to deliver people analytics capabilities when they actually don’t. The RedThread report calls this out.

Not All People Analytics Tools are Equal

In the research, there is a distinction made between people analytics tools and technology across 2 continua:

• Are you a data creator or a data aggregator?
• Do you enable frequent (periodic, like monthly, quarterly) analysis or continuous (constantly refreshed) analysis?

As such, ten subcategories of people analytics are identified by RedThread: 

1. Employee Engagement/Experience
2. Multi-Source People Analytics Platforms
3. Organization Network Analysis (ONA)
4. Workforce Planning
5. Labor Market Analysis
6. Learning Analysis
7. D&I/Pay Equity Analysis
8. Employee Coaching
9. HCM/Integrated Talent Management Analysis
10. Text Analysis

PeopleInsight: An Early Market Leader in the Multi-Source People Analytics Platform Category

In case you are wondering where PeopleInsight fits, we are considered, according to RedThread, as part of their Guiding Analytics quadrant, in their Multi-Source Analysis Platforms segment with eight vendors including two other early leaders, Visier and OneModel.

Also Recently Released: A Practitioners Survey on People Analytics Usage in Business

In a recent practitioner survey called “Big Data and Talent Analytics” by HR.com’s Research Institute, the key takeaways are:

HR Must Create Value by Becoming More Data-Driven

• Train your business leaders in people analytics
• Get your head of HR (e.g. CHRO) actively involved in people analytics
• Train your HR business partners to be people analytics savvy
• Learn about research design
• Partner with finance and other departments on people analytics projects
• Track and promote successes where people analytics drives change
• Examine compensation and recruitment analytics
  
  

HR Data Management is Highly Complex but People Analytics Technologies Can Help You Accelerate

• Make sure data is properly cleaned
• Build an integrated data infrastructure
• Be aware of the value and difficulty of data integration
• Leverage the technologies you have while planning to adopt more powerful people analytics technologies in the future

Moving Forward With People Analytics

Every organization with a critical mass of employees - starting at 40 or 50 - can gain value from people analytics. Here’s a story of one such example of a ~100 person niche manufacturing company who have made remarkable progress on a shoestring budget. The Controller of this company was interested in building new value with the HR data which was locked away in their systems. He started with a small analytics project and now he’s the General Manager running the entire operation and continuing to make progress as a company by leveraging data-driven HR. If you're looking to start your people analytics project, here's an 8-step plan for getting started. 

Certainly one size does not fit all, so here are some critical factors you need to take into consideration when determining your approach (these are based on a highly informative article on the LinkedIn Talent Blog which is expanded upon):

1. Identify the impact you want your people analytics to have - or as I say, focus your people analytics on juicy business issues and picking low hanging fruit - don’t go on a fishing expedition.
2. Understand your current capabilities in delivering people analytics - which includes a combination of environment context, leadership, governance, processes, tools, technology and people. Most importantly, identify your gaps.
3. Find the right “partner” or “tool” (internal, external, sophisticated, simple, etc.) to fill those gaps and consider whether this is for the short term, in order to help you build maturity, or forever.
4. Deliver some quick wins. Gain momentum. Partner with the business on delivering value. Rinse. Repeat.
5. Combine quantitative understanding with qualitative insight and context. Always ask “the why” once you understand “the what”. Drive decisions. Implement change. Measure benefits, success and ultimately, the ROI of your decision.

For more information on number 2 above (the capabilities you will need to deliver people analytics) please refer to the Data-Driven HR Playbook and download the entirety of Playbook # 3 (Capabilities - Pg 44-98).

What is your people analytics customer looking for? 10 CXO Expectations from People Analytics

Ultimately, and to bring this full circle, your goal in people analytics is to connect workforce investments, talent programs and people performance to business performance.

This means delivering a value-added capability to business - which will be guided by expectations set by the C-level. Here are 10 key expectations that CEOs, COOs, CFOs and CXOs have when it comes to people analytics.

If HR can deliver on these expectations, you’ll be well on your way to successful and sustainable people analytics.

1. Know The Pulse - be confident and credible with the topline company metrics on organizational and employee health (understaffed, overstaffed, engaged, turning over, etc.).
2. Know Your Stuff - while HR operational metrics (re: payroll, recruitment, comp and bens, etc.) must be quantified, tracked and optimized as part of HR’s day-to-day - don’t expect others to be always interested unless they are impacted at a given time. HR just needs to be credible and trusted in getting these basics right.
3. Be Impartial - start with unbiased facts and uncover both areas of strength and opportunity. Then move into understanding context or underlying conditions in topics that matter.
4. Have Proof - focus on articulating or debunking the people side of business issues (both strategic and operational) that the CEO is facing such as talent pipeline, productivity, turnover, compensation, etc.
5. Be Practical - identify practical, affordable and ROI-driven programs to build great teams, drive productivity and execute the business plan.
6. Mitigate Company Risk - use HR data to understand and mitigate people-based risk (like the impact of not hitting hiring targets, accurately forecasting hiring needs, etc.).
7. Truly Partner - don’t just identify problems, be preemptive, collaborate on working on solutions and measure impact and progress.
8. Be Insightful Where Relevant - don’t provide insight into things which aren’t of strategic or operational relevance - drill down and deliver credible, data-driven storylines in the things which matter at any given point in time.
9. Optimize Employee Lifetime Value - use your data to make smarter hiring decisions and retain key performers. Understand where your best sources of hires are, how to best onboard them to expected productivity, who creates differentiated value and how to best retain them for the long-run.
10. Identify Value Creating Opportunities - use HR data to reduce risk, trim costs, and to accelerate and directly contribute to company P&L.