LinkedIn Pulse - 5 challenges for the future of people analytics

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PAFOW London 2019 take-aways

"Take a learner mindset, not a judger mindset."

With this call Al Adamsen and David Green kicked off the PAFOW London conference on April 24th 2019. And that’s indeed the spirit necessary to succeed in the fast-moving space of people and workforce analytics. Even though the field has been around for a couple of decades, it is now evolving at warp speed thanks to various revolutionary technologies such as AI and machine learning. But accelerated evolution often involves not knowing where the road will lead. In such cases, a learner mindset is indeed required.

So, here’s my non-exhaustive and idiosyncratic list of insights and takeaways from the two-day conference.

1 – Insights should inform decision making on business issues

“Don’t forget why you were given this analytics project. You were asked to help answer a business question.”

Tomeka Hill-Thomas and Blair Hopkins (EY) stressed once more what should be considered a truism but is regularly ignored. Too often, workforce analytics is still driven by the love of analytics or the obsession with pure HR topics. If the community is really serious about having an impact on the business, the starting point for each workforce analytics project should be a business issue.

The focus should not be on offering generalized insights as an afterthought, but rather offering deep insights – descriptive or predictive – that allow intelligent and data-driven decision making.

I'm not saying that people issues can’t be business issues by definition. On the contrary, but it's a matter of identifying those people issues that will impact the company’s success in the short or long term. Strategic workforce planning is one of those topics that will become essential in the era of contingent workforces.

2 – Insights should be actionable

To be useful for practitioners and vendors, insights need to lead to real-world results. In daily operations, what business leaders need are actionable insights. Understanding why something has happened and what is likely to happen in the near future is one thing. Receiving valuable, straightforward advice on the optimal course of action is another. I think the future of workforce analytics will be about offering prescriptive insights on where, how and when to intervene, as well as with whom.

And let’s not forget that these interventions and their outcomes generate new data. This data in turn should be captured and integrated in the data model. Such feedback loops allow the accuracy of prescriptions to evolve exponentially.

As Jonathan Ferrar said in his closing keynote:

“It’s not just about getting your point across but really about implementing and evaluating.”

3 – Insights should be productized and ‘solutionized’

Insights have zero value if they are not offered to the right people in the right form at the right time.

"We need to operationalize insights, not just manage the data and provide on demand analytics"

Commonly, internal people analytics teams don’t go all the way with their value chain. Many vendors in the space are now offering dashboards and user interfaces that bring the data to the user. The challenge, however, is to compete for attention with BI tools and a variety of communication channels. I feel that, in the future, a key strategy for vendors will be finding a way to integrate rather than compete with these tools.

4 – Insights are useless if HR doesn’t develop new skills 

I was amazed by how many of the delegates at the conference have strong backgrounds in data science and computing. It seems like there is a whole new breed entering the field of people analytics. In the past, people analytics teams were often composed of people with an interest in data, but not necessarily carrying a diploma or true expertise in data science.

I firmly believe the credibility of HR will benefit from this evolution. However, the suppliers of information and insights are not always sufficiently skilled to actually interact with the consumers of the insights, business leaders and executives.

“There is a need for a partner between the supplier and the consumer.” Al Adamsen

 HR business partners should therefore start developing new skills as a priority. On top of their business acumen (assuming that they have this), they should acquire at least a basic understanding of data science. They don't necessarily need to crunch the data themselves, just act as knowledgeable intermediaries between data scientists and the business. This concept was particularly well-illustrated by Oliver Kasper and Roberto Amatucci from Swarovski.

5 – Insights should be used in an ethical way 

This sounds like a no-brainer and with ‘people data for good’ as the baseline for the conference one might think that no more debate is needed. There seems indeed to be a broad consensus that people analytics should be guided by ethical principles. But what these principles should be (beyond legal guidelines like GDPR) and how they can be safeguarded is less clear.

As a former doctoral researcher in philosophy and applied ethics, I feel there is still a lot of work to be done in setting the ethical agenda of people and workforce analytics. And the domain is vast.

  • What are the ethical boundaries, beyond GDPR?

  • Are topics like inclusiveness and diversity mandatory, even when the business is not interested?

  • Should there always be a benefit for the individual employee or is impact on the bottom line a legitimate and sufficient reason?

  • Is full transparency an absolute must, taking into account that many HR decisions today are made behind closed doors?

  • Where’s the cut-off – if there is any – between the moral obligations of vendors and those of employers?

  • Etc.

The good thing is that many leaders in the space (such as Nicky Clement from Unilever) are investing a lot of their time and energy into exploring these questions and developing new practices. As a community, we should endeavor to not only bring the newest technology to the table, but also to tackle the tough ethical questions. People data for good!