Why People Analytics isn’t Delivering on its Promised Value
According to McKinsey, People Analytics is going mainstream. Where we used to talk about what People Analytics is and how we could get started, the conversation is now shifting to how we can speed it up, implement it faster, and deliver a decent return on People Analytics investment. This is an important conversation to have because the value of People Analytics is lagging behind its potential.
Despite the uptick in analytics adoption, its impact is not really increasing. We need to have a difficult conversation about how the value of People Analytics can finally be released. As for the companies that are already there? Well, that stands between a lowly 11 – 18%.
We know that the potential value of analytics is high. We don’t have the numbers for People Analytics yet, but we know that general business analytics can generate $13 for every $1 invested. That’s an ROI of 13X.
Case studies provide anecdotal, confirmatory evidence. Yet the hype and excitement with which companies jump on board with People Analytics quickly fades, once the majority of CHROs realize they’ll not come close to mirroring the results seen in these much-publicized case studies.
The million-dollar question: Why isn’t People Analytics delivering on its value?
Just 2% of organizations have reached a high level of People Analytics maturity.
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There’s a widespread fundamental problem. People Analytics (analytics being the active word) is being viewed as nothing more than a reporting activity. So let’s clear these two definitions up…
First of all, we have reporting. This involves gathering data and displaying it on dashboards and reports. While reports are valuable and can help to steer business, they focus only on the here-and-now rather than on what is likely to happen in the future; that is, they are not predictive. Furthermore, they do not recommend courses of action to correct problems; that is, they are not prescriptive.
Then we have analytics or statistical modelling. This involves proactive activities such as sorting your employees from low to high performers and then identifying the factors that distinguish low from high. This information can then be used to recruit and develop more high performers.
So, why the confusion? For a definitive answer, we must consider three core reasons:
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- Data-driven equals analytics fallacy. There’s a big difference between ‘data-driven’ and ‘analytics’. While analytics is a data-driven approach, there are also many other data-driven approaches that don’t involve analytics. Most strategic workforce planning tools, HR reports, and other scorecards cannot be categorized as analytics.
- Dashboards sell. In the land of the blind, the one-eyed man is king. For a department that’s data-averse, a dashboard feels like the real deal – especially when presented by a slick salesperson. Consultants who show People Analytics maturity models which suggests that dashboards are a requirement for advanced analytics, don’t help.
- Deceiving vendors. In line with the previous, many HRIS vendors are selling basic dashboarding and extrapolation functionalities as if they are advanced analytical suites. This has changed how people see analytics. There are only a handful of solutions in the market today that are truly useful for proper (predictive) analytics. Most of these tend to include programming languages like R or Python integrations and the ability to import data external to the system.
There are only a handful of solutions in the market today that are truly useful for proper (predictive) analytics. Most of these tend to include programming languages like R or Python integrations and the ability to import data external to the system.
(Even more reasons why) reporting is not enough for effective People Analytics
From an HR perspective, insights into people processes and workforce capability is definitely a step in the right direction; but it is not the end of the story. This is because operational stakeholders in the business are not interested in these processes and capability; they want to know how HR activities and processes are driving business performance and outcomes.
“To what extent are our people processes and workforce capabilities delivering our key performance drivers and business outcomes?”
That’s a key question most senior business leaders need to be answered from HR. Yet most HR reporting focuses on levels 3 and 4 (People Processes and Workforce Capabilities).
In this key question, we get to understand what analytics should be about – not simply illustrating “how things are now”. When done well, and done right, analytics helps to analyze how what we’re doing in HR adds to the rest of the business. This is what generates real business impact. This is what is achieving the much-touted 13X return.
Moving on: How to transition from reporting to proper Scientific People Analytics
The chasm between simply reporting and what we will call Scientific People Analytics is a divide that must be bridged – requiring team members who can understand the business, aggregate data, and perform statistical analysis.
Scientific People Analytics requires the identification of process issues that drive key business outcomes. In our example, we’ll focus on increasing sales revenue through better recruitment, learning and development, and retention of salespeople.
The first step is to establish what it is that defines ‘good’. If you don’t know what a high performer looks like, on what basis do you recruit, decide what training to provide, or know what parts of your culture to change in order to retain these people?
This is defining high performance – the best strategy for which is to apply interviewing techniques such as the Repertory Grid, which elicits structured information from managers.
Once you know what it is that defines ‘good’, scientific statistical methods can be used to identify the factors that distinguish between low and high performers, and between employees who leave versus those who stay.
These factors might include competencies people gain through training, the personality they were born with, their education, experience, etc.
While competencies can be trained, personality – in contrast – is fixed from about ages 6 – 60 and so cannot easily be changed by learning and development. If personality is important for a particular role, it would have to be ‘recruited’, since training can’t help to develop it.
Once the personality characteristics of high performers have been identified, and the training that increases their performance most has been established, these factors can be built into job advertising, candidate selection processes, and learning and development plans. This is how you add real value using Scientific People Analytics.
From data-driven to value-added
Getting started with Scientific People Analytics requires HR analysts with often difficult to find diverse skill sets. Yet as we have seen before, when this is correctly done, it tremendously increases the value that HR delivers to the organization.
This recognition is beginning to happen. Slowly, companies that are coming to understand the difference between crude reporting and actual analytics are seeing significant results on their People Analytics investments.
This article was co-written by Max Blumberg and Erik van Vulpen
Erik van Vulpen is founder of Analytics in HR (AIHR). He is writer, speaker and trainer on people analytics. Erik is instructor for the HR Analytics Academy and has extensive experience in the application HR analytics. Contact Erik at firstname.lastname@example.org or connect with him on LinkedIn.