People Analytics – What’s the Big Deal?

Someone asked me recently why they are seeing so much more hype and publishing around the subject of People Analytics. I thought it was a fair question. It’s not like we only recently discovered we could analyse data about people. Psychologists and Sociologists have been doing that already for a long time. I’ve personally been analysing data about people for at least the past 15 years.
So I understood their question to mean ‘What’s new?’ What has changed that has suddenly propelled this topic onto the radar screens of CEOs, CHROs, academics, journalists and others who just want to learn more about it? Why are organisations suddenly setting up and growing dedicated People Analytics groups out of their limited scatterings of erstwhile workforce analysts?
Some people have suggested that organisations have reached a tipping point in volume where they can now regard their people data as ‘big data’, and that this now explains the fuss. I disagree. As Peter Capelli points out in a recent HBR article, it’s not really about ‘big data’. It’s erroneous to think that insight automatically follows from volume. Richness and variety of data is equally important, and structure is the key to almost all effective analytics. When analysing any form of data, there is always a tension between, quantity, variability and dimensionality. Quantity in and of itself never wins out.
[bctt tweet=”People Analytics is not really about big data? Or is it? Keith McNulty explains.” username=”ATCevent”]
So what is it about? In my opinion the answer lies in technology – in the massive levels of digital growth and innovation that we have experienced in the past five years. Technology is giving us new ways of looking at the world around us and challenging accepted wisdom like never before. As a few people have recently pointed out, 20 years ago we were told never to get in a car with a stranger and never to meet people on the internet. Now we are literally summoning strangers from the internet to get in their cars.
In the same way, technology is giving us ways to understand and analyse people that I would never have dreamed possible when I started out as a young psychometrician in the early noughties.
As is my wont, let me put some structure to this. In the past five years, technological development has offered up exciting opportunities to enrich the digital exhaust, obtain new people data to analyse and to analyse people data in new ways. Here are some examples of what I mean.

1. A richer digital exhaust

Many organisations are experiencing a better quality and richer digital exhaust in recent years. The quality and richness of the data imprint of members and employees is vastly superior to the situation even five years ago. This is due to:

  • Improved data systems with greater levels of flexibility to capture information in both structured and unstructured form.
  • A greater variety of database structures, ranging from traditional relational databases like Oracle and SQL through to graph databases allowing highly flexible data capture and query, like PoolParty and Neo4j.
  • Improved document digitisation and parsing, allowing the text within documents which were previously out of scope for the everyday analyst to now become a part of their dataset.
  • More integrated data across fewer systems, making it easier to connect data related to individuals and groups and analyse a broader set of indicators.
  • More pulse data, with more organisations ‘sampling’ the performance and attitudes of their members on a more frequent basis. For example, Bridgewater ask their employees to complete a feedback rating on all attendees of meetings above a certain size after every meeting.

2. New technologies to capture people data

The past five years has seen the realisation by organisations and entrepreneurs that people data is a valuable asset that can be harvested and harnessed by new technology. This has led to exciting innovations both in how to capture this data and in how to offer it to potential consumers. Here are some current examples of this.

  • Talent aggregation is the concept that an organisation-independent repository of biographical data about people can be built and utilised for a number of purposes. Primarily and most obviously this can be used to identify potential recruits, but it also offers the possibility of broader insights around organisational trends, retention and attrition, culture and many other possibilities. LinkedIn was the first mover in this space, but many others and now battling it out in this arena, including Entelo, Hired and Piazza. This raises some fascinating and crucial questions about access to and ownership of people data – a debate which has already made its way into courtrooms.

  • Digital technology allows new ways to gather data on people’s knowledge, abilities and characteristics. Innovative companies like Knack and Arctic Shores design game-like tasks which collect data on the decisions people make and the ways they approach problems as they play them. HireVue, among others, is digitising the spoken word in video recorded interviews and using machine learning techniques to try to identify the ‘language of success’. Although there are varying standards of scientific rigor among the many players in this space currently, the common motivator behind all this innovation is the richness of new data that can potentially be harvested in this way.

[bctt tweet=”Why are we seeing so much more hype around People Analytics? Keith McNulty shares his thoughts. ” username=”ATCevent”]

3. Analysing people data in new ways

All this rich exhaust and new data wouldn’t have been much use to us five years ago. The word ‘data scientist’ was just coined, and the majority of analysts were limited by the size of their processor or hard drive and the UI limitations of Microsoft Excel or SPSS. Now a whole host of technology has entered the fray which offer up a wealth of capability.

  • Open source statistical programming software has made he jump from academia to the world of enterprise. R and Python, for example, are being adopted more and more to analyse people data. This kind of software opens up the possibilities for what can be done. It automatically understands different data types, from numbers to text strings. It can process data quickly and more efficiently, reducing time and screen freezes. It offers up multitudes of pre-programmed packages for working on people data. And, as open source software, its easy to gain access to learning or advice through resources like Datacamp and Stackexchange.
  • Cloud based computing is very accessible, allowing tasks that have a high processing demand, such as text analytics, to be done effectively and quickly. So much people data is formed of unstructured text, and the ability to process this text allows possibilities that simply were not there previously, such as sentiment and emotion analysis, topic modelling, predictive analytics and artificial intelligence.
  • Visualisation possibilities are vastly greater today. Facilitated by the adoption of open source technology, we can view people data in multiple dimensions, in time series, using network graphing and other diagrams that were not so easily accessible previously. We can create dynamic charts that change and update on the fly. All of this is critical in facilitating a stronger understanding of complex people phenomena.

We are in the midst of a genuine paradigm shift in how we gather and analyse people data, and it’s the technology that has taken us here. Even though many are still trying to wrap their heads around such a rapidly moving space, there is no doubt that it’s a big deal, and it will continue to be for some time.
Cover image: Shutterstock

This article first appeared on LinkedIn on August 16, 2017.

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