Trust: The Achilles Heel of People Analytics - Analytics in HR

Trust: The Achilles Heel of People Analytics

It might have started off slow, but things in the world of People Analytics are now accelerating. The number of conferences, experts and blogs have exploded....

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It might have started off slow, but things in the world of People Analytics are now accelerating. The number of conferences, experts and blogs have exploded. On top of that, the promise of People Analytics has begun to deliver, and we’ve started seeing real, concrete results.

More and more companies have deployed People Analytics projects that have had an impact on the success of their employees and their businesses. We’re also seeing fast growth in the number of companies that have started building their own People Analytics units, to continuously strengthen the interaction between the talents of their (future) employees and their organization’s objectives.

Examples of the great results I have witnessed myself are improved quality of hire due to powerful algorithms, increased retention due to clear insight into the key drivers of engagement (followed by interventions), more diversity due to less biased selection processes and real-time insight into how to most build the most effective teams. This is all great news!

The importance of winning trust

However, with all these successes we are also confronted with limitations, pitfalls, and risks. From day one, a key challenge we’ve encountered in People Analytics projects is ‘winning trust’. This is why we should explain to people that analytics can be trusted and that People Analytics is about more than just automating people decisions while excluding or replacing human insights, experience, and even gut-feeling.

More recently, I’ve noticed an increase in the number of questions and concerns with respect to topics such as; privacy, data security, data ownership, ethics, and the reliability or integrity of algorithms. There’s a greater awareness that algorithms can and do sometimes inherit the developer’s biases and those of the used data itself. There’s been huge publicity about how this has happened at Google, with biased search algorithms, and at Facebook, with biased news feeds. Of course, it would be naive to assume that People Analytics projects are immune to this sort of problem.

All-in-all, these are sufficient reasons to be careful when applying data and algorithms ‘blindly’. I think it is good to realize that People Analytics is not the holy grail and that it should instead complement existing academic knowledge and our own day-to-day experience and expertise. Brought together, the theory, experts, and data create valuable insights. It is vitally important not to isolate them from one another.

The evolution of predicting behaviour in People Analytics

Trust in people analytics

When discussing the topic of trust, it helps to distinguish between the different aspects of trust.

  • Trusting the value in People Analytics is worth sharing your data for.
  • Less and less people, including your employees, will be willing to share their data by default. General Data Protection Regulation (GDPR), coming into force in May 2018, will enable them to exercise their rights not to share data. Increasingly they will want to know “what’s in it for me?” and, “can I trust you?”

    I expect that in the near future people will take more ownership of their personal data. Privacy by design will become the standard, and it will be a real challenge to convince people to share their data with you unless they trust you 100%.

  • Trusting the quality and the integrity of the data.
  • Research shows that high percentages of decision makers feel the need for more fact-based people decisions, however, many of them also feel uncomfortable trusting the data in their systems. KPMG published some interesting research on how to reinstate trust, using four anchors. These are:

    1. Quality (good input)
    2. Effectiveness (does the output tackle what it was aiming for)
    3. Integrity (ethics, principles)
    4. Resilience (continuous improvement, not a one-off-shot)
  • Trusting the insights (the outcomes) that have been generated.
  • We know that people have a hard time accepting and trusting automated decisions. This becomes even stronger when it concerns people decisions. This distrust is called algorithm aversion. This is the tendency to distrust evidence-based algorithms, even when it is known that these outperform human forecasting and decisions.

    I recently came across some nice research focused on overcoming this aversion. One key takeaway from this was that we need to give people the possibility to modify algorithms, even if the bandwidth of the modification is relatively small.

  • Trusting the motives.
  • It’s vital that people trust that the revealed insights will be used for the right reasons. This comes down to trusting the people that use the insights to support their decisions. In my experience, it is crucial to communicate that we are not aiming to automate the human but to humanize the data.

It is crucial to communicate that we are not aiming to automate the human but to humanize the data.

People first

We should make it clear we are using the data to create insights that are beneficial for the company’s strategic objectives, but above all that this is beneficial for the people involved.

For instance, gathering insights into work behaviors to find out how people collaborate at work, what they share with each other and how they communicate with customers can create valuable knowledge for the employees involved. This kind of exercise can help them become happier and more effective at work.

It could also be used to let people go, or to become stricter about what they can and can’t do in their roles.

The 4 Elements of rust in People Analytics

To summarize, we must be careful that we do not underestimate the challenges we need to overcome to win the trust of all our stakeholders, including, or especially, the subjects of the analyses. If we don’t take this seriously and put it at the core of everything we do, it could very well be that we have already reached the limits of People Analytics.

Good and effective People Analytics teams must have the highest moral standards and must always put people first. People will not accept a ‘black box’ making decisions about them. To win trust, it is essential that we understand and explain the algorithms that power these decisions. Complete transparency and the ability to modify the algorithms (even slightly) is key, otherwise, analytics will soon be out of data.

Further reading and inspiration
Cubiks International People Analytics Survey Report

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