The power and peril of employee data analytics

This article originally appeared on LinkedIn and was posted by Akumina’s Board Adviser Florin Rotar, Executive Global Digital Lead at Avanade. 

It’s clear that one of the next competitive frontiers in a post-digital world is the way enterprises design and engineer employees’ workplace experiences (WX). Research from MIT Sloan Center of Information Systems Research provides strong evidence that the firms with the best WX, where employees are genuinely enabled by practices and technologies to do their jobs today and to redesign their jobs of tomorrow, are the clear winners. The research finds that top-quartile performers in employee experience have double the customer satisfaction (NPS, industry adjusted), twice the level of innovation (as a percentage of revenue from new products and services), and perhaps most impressively, 26 percent higher profitability.

The best-in-class enterprises are focusing on the employee “moments that truly matter,” whether it’s when a retail associate is helping a disgruntled customer, when a nurse tries to change his shift, when a new employee is celebrated on a Friday afternoon after the first week at work, or when a newly promoted manager is doing her first performance evaluations.

Creating the best “moments” based on analytics-based insights and powered by AI is already fairly common in the customer experience domain. As many organizations have learned from that experience, it’s great when it’s done right, but it can be creepy or even unlawful when done wrong. The stakes are higher still on the workplace experience side.  A recent Accenture study found that more than six in 10 executives are using new technologies to gather data on their employees and their work to help them to collaborate better, or to enhance their safety and well-being, but less than a third feel very comfortable they are using the data responsibly.

Using data responsibly means addressing fundamental data ethics questions, including clarity into how the data was collected and why. Who is responsible for understanding and affirming the provenance of this data? Can we be sure that the data is only used within the context and intention in which it was collected and that it will not be re-purposed? Can we make certain that the data is truly “neutral” and that is does not carry a history of human decision making that may shape the consequence of its use?

When deciding how to use the data and what digital services to develop, we need to move beyond the instance-based ethics of well-intended gut-feeling decisions. For example, an AI system proactively sharing newly published research with a doctor about a promising new treatment in her area of expertise is likely a good thing. It is okay if this gets highlighted as highly relevant to her based on a colleague are reading or sharing it. It is not okay for a section of the public employee handbook covering parental leave policies to be highlighted in the same way, just because one of her closest colleagues is spending a lot of time reading up on it.

As Peter Temes and I argue in our upcoming book “We the People – Human Purpose in the Digital Age”, we need to shift from instance-based ethics to principle-based ethics. Instance-based ethics is what we practice when we see something and say, “that’s wrong.” Instance-based ethics requires a person or a team to decide on the spot whether something is right or wrong and keeps hidden the underlying principles that guide us in making these decisions. Principle-based ethics give us the chance to openly apply stated values in order to consistently reach good decisions, regardless of the particulars of an individual situation.

Values serve as the compass points – fixed positions that we test our actions against. Organizations might use the idea of human value as one of their positions – that the highest priority is to respect the individuals that this data describes and affects. They may also have stated values about privacy – when an employee become a “data donor,” we should think about how their data is adding value to others, and be sure that the donors are prioritized as beneficiaries of that data. Organizations may also have values about equity – the sharing of the gains created by shared knowledge and shared work.

The Accenture study showed that if done right, more than 90 percent of the workers are open to data being collected about them, their work and their performance – but only if the data collected provided meaningful help for their performance and well-being or provided other personal befits. More than 60 percent would be willing to exchange the data for more customized compensations, rewards and benefits!

The bottom line is that responsible use of workforce data requires principle-based digital ethics based on openly shared and strictly enforced values. This helps to ensure that data is used responsibly and ethically, in ways that will provide additional benefits to the employees about whom the data is gathered.

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