I have been thinking for a few weeks now about something that I think is important for those of us involved in this amazing world of evidence-based People Management:

What is our future, where are we heading?

And most importantly, what really worries me ...

How do we move forward so that what we do is really useful to people?

To make them work better, happier, more versatile, more productive and efficient, more committed ... the usual, but in a more complicated context than ever.

A few years ago, when we considered doing a People Analytics project in a company, the first thing we wanted to do was to create a descriptive model of the reality, similar to a demographic photo. Men and women, areas, levels of training, rotation, profiles entering the company and even, going a little further, we were looking for the type of leadership in unplanned departures, etc. ...

We focused and still focus on the individual as if he or she were isolated, as if interaction with others did not matter.

Later, when we already had the People Dashboard developed with a consistent history (18/24M), we considered building the corresponding predictive model by looking for the variables that were close or directly correlated with the one we wanted to know. Our objective was to anticipate what would happen. To move forward thinking that the essentials of the context would not change ...

But I have known for some time now that this is not our future, although I am also aware that there are still many organisations that have not yet reached this point.

In what is to come there will be prediction, of course, but not obviating the now. For sure.

The concept of liquid society, influence, sudden changes, adaptation, vulnerability, collective intelligence, global agreements, shared leadership ... are not in the equation, something very important we are leaving out to be the field of knowledge that will lead the future of People Management.

Prediction or Advanced Analytics

The easy answer to the question we pose is to say that the future of People Analytics is Advanced Analytics with a focus on prediction, but no, it is not. Because in reality it is more of the same, and let me explain.

Those of us in this field have so far considered that the normal evolution in the analytical journey is descriptive, strategic and finally predictive. And I believe we were not wrong, but we are not able to do it simultaneously in all areas of the People Department and not for all the decisions that have to be taken: it is simply not possible.

The result is that while building a descriptive overview of what is being done in the training area with its database and extracting insights for the suitability of certain profiles, we will be developing a predictive output in a certain department with high turnover.

Interventions to segment employees in order to offer them training more suited to their needs and profile (improving the employee experience ) will then coexist in the People Department with others aimed at correcting negative predictions of medium-term departures.

The descriptive and the predictive in a certain analytical moment in the not too distant future will be two sides of the same coin because they are, in short, two ways of working with the employee, but with an individualistic and somewhat static approach.

The hypotheses we will arrive at will always relate current characteristics of people to current patterns of behaviour or to actions that will occur in the future with a certain probability, but ... what if current characteristics are not the right starting point?

The conclusion is, therefore, that predicting is not the future because it is the present, although in some cases it is not yet fully developed, mainly due to lack of data and time.

Ethical Data Management

Nor is it. While we certainly have a long way to go, the future of People Analytics is not an improvement in data ethics.

Beyond the well-known potential privacy problems associated with open data, which have been discussed at length and for which concrete solutions have already been offered, a more general debate on the application of ethical standards in the collection, management and use of data has also been gaining momentum recently.

But in reality all the legislation on personal data is less and less affecting our projects because we are managing to transfer the pillars of the systemic approach where it is made explicit that it is not necessary to know the personal information of each individual for the analyses.

We are working more and more with data associated with individuals in an anonymous form, because the information we are interested in is that relating to groups and not to the details of individual employees.

The only real problem is the one-person positions or responsibilities that need to be dealt with in a special way, but in any case it is very often not necessary to make an ad-hoc analysis of this group.

Ultimately, the real difficulty in data ethics lies in how to deal with data and above all how to explain the conclusions reached and why, without biases, prejudices or a prioris that limit our good work.

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Combination Psychology and Data Driven

Although many of us thought so until recently, the combination of psychology + data driven is not the future either. What can we do?

In a first analysis, one might think that, coming from where we come from in our country, the key was to unite what has historically been done in the field of people, management based on behavioural psychology, and combine it with a new data-driven conception in order to better understand reality, but the truth is that they are incompatible. The two approaches cannot coexist indefinitely, even if you want them to.

One of the two will always win the game.

In any field, the definition of its workflow is the way in which it translates into action what is considered the 'challenge to which we must respond' or in other words the much-quoted strategy. The methodology behind the tasks.

In other words, if the company's overall strategy for this year is to improve efficiency and the use of available resources, for example in the area of people, there are two options: either we consider that we do not know where we are and we must know where we stand, or we do not.

Objectivity in improvement, in the evaluation of the road travelled (which in turn means impact control) is the main insurmountable barrier between the two options.

Can anyone imagine the possibility of doing actions in the marketing department without evaluating the impact on sales or positioning in networks? Or doing it sometimes yes and sometimes no?

The effect of Interaction

People Analytics is a new way of managing people based on evidence for organisational improvement.

But improvement is something that depends on different factors at different times, it is not static. Improving as a company in pandemic times means something very different than doing it months before all this started and it will be different when it is over.

Improving is something that will depend on the people who make up the companies, on what they do, what they feel, what they think at any given moment and not so much, in my opinion, on what they are or have studied up to that moment, their own attributes.

I am in no way saying that we should not predict, nor that we should not have a scrupulous respect for the privacy of each person's data, taking into account each and every one of the laws in force in this respect, nor, even less, that a certain psychological analysis cannot unravel a certain problem.

What I dare to say is that we are at a transcendental moment at all levels, also in the field of People Management, and we must look further ahead and with a deeper, more agile and open vision than we have had so far in order to be able to bring the desired value to the company.

It is not just a matter of going beyond dogmas, of beliefs repeated ad nauseam, which fortunately we have already begun to counteract with the contribution of data, we must take a leap.

First of all, by taking speed and proximity to the decision-making centres because we have already seen that we will only be able to put our knowledge at the service of the company if we manage to identify it at least with the speed it requires and, secondly, by opening our search for hypotheses to explain phenomena to a non-individualistic approach.

Classical causality models have long since become obsolete.

There are timid inclusions in People Analytics about behaviour, Behavioural Analytics, very interesting indeed, but the INTERACTION I am referring to is not only about behaviour but about the result of one's own behaviour on others.

INTERACTION defined as the study of constant change due to internal and external actions as opposed to variables that are static over time.

It is Systemic Analysis at its best and with a practically life-like knowledge that provides us with a fully completed digital transformation and also with graph theory analysis tools and ad hoc mathematical algorithms. Almost nothing.

The sole objective is to understand the organisation for the first time in a global and non-segmented way. To create knowledge that evolves like the companies, like the people that make them up.

Otherwise, the danger is to reach a feeling of digital fatigue that can lead to a return to traditional, more authoritarian management because it is in a way warmer for people, for everyone in general, and certainly much more affordable in global terms.

There is a real danger and it is not as far away as we think.

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ABOUT THE AUTHOR

Marta has a degree in Mathematics (UPC), a postgraduate degree in Mathematical Modelling (UDG) and a postgraduate degree in Training and Development of People (BSM-UPF). Her passion is the alignment of business, culture and people through evidence and Data Analytics in order to achieve shared goals and move forward in an objective way.

With more than 15 years leading teams of Data Scientists in multinationals, in 2015 Marta founded PersonKPI to dedicate herself exclusively to People Analytics and act as a partner in HR metrics for companies. Marta also teaches at different universities and business centres, as well as co-leading, with other colleagues in the field, meetups and specific meetings on People Analytics at a national level in her home country of Spain.