2015 in People Analytics

At university, my Econometrics Professor used to always say ‘don’t predict the future as you’ll most likely be wrong’. (He also was paid every year by a big investment bank who wanted him to try and do just that.) Hence in this post I decided to end the year taking a look back at 2015 and what has happened in People Analytics, seen through the lens of one of Europe’s prominent People Analytics practices.

People Analytics has taken off

I think 2015 was the year when we started to see real momentum in firms doing People Analytic projects. Let’s be clear, most firms still aren’t doing anything substantial but those who are are now starting to bring it into most of their initiatives creating ongoing and substantial demand. Those who haven’t started are certainly being left behind.

There is certainly more hiring in this space. Our clients have increasingly got numerous people in their HR analytics functions. The skill levels are increasing. We know several firms with strong, analytical Phds in their teams and an increasing number of analytical MBAs. An increasing number of our projects have ‘knowledge transfer’ as an aim.

HR leaders are eager to become more data-fluent

In 2015 we’ve run analytics training in Europe and Asia and the diary already includes sessions throughout 2016. The audiences have always been similar – senior level HR managers who want to understand enough about analytics to be able to identify opportunities, select vendors and generally feel confident using data to inform decision making.

We’re seeing this move down through the organization, and to more junior levels of HR. We focus on providing skills to help people think analytically rather than teaching how to do analytics. Knowing how to manage analysts will become a core skill for HR and managers need to know enough so that these relations can be fruitful.

Predictive analytics is rapidly becoming standard

OK, so I have a big problem with people using a binary classification between ‘doing’ and ‘not doing’ predictive analytics. We’ve always, since founding in 2010, been using machine learning techniques to help make better decision making for HR. It’s pretty straightforward to build a simple model but to increase the accuracy or usefulness requires greater and greater effort.

It also shouldn’t be seen as an activity which can be completely automated. There is a big difference between taking photos of your friends with your smartphone and creating work like Nan Goldin. As any analyst knows, there’s a lot of craft in building models. It takes time to learn, domain knowledge is vital – not just of HR but also of the specific business situation and how the data has been collected.

In 2015 we saw a continuing push by most big HR technology vendors to provide ‘predictive analytics’ into their products. Clients need to understand whether these generic models are good enough for their needs. My suggestion is that if you’re wanting to gain competitive advantage they’re probably not. There’s also a question of how the results are applied but that probably demands a post to itself.

Graph analysis is becoming more regularly used

The next two trends I predicted during a presentation that I made when I chaired HR Tech Europe’s Big Data conference in early 2013. They’ve now really come to fruition.

Last year I wrote a post on graph or network data structures and how they provided a different way at looking at your data, enabling new questions to be answered. For us 2015 was the year when using graph analysis became mainstream.

I can’t think of one project this year which didn’t involve some form of graph analysis. Some of the time it was about the relationships people had within their businesses but often it was simply to look at data, or metadata from a different perspective.

Our dominant tool for graph analysis this year has been igraph used via the R package. I got a very nice, slightly early Christmas present when Gephi 0.9 was launched on 20 December. During my training I use the wonderfully user-friendly yet powerful Polinode to teach networks and network thinking to teams of senior HR managers.

Text analytics is providing a rich goldmine of information

I wrote in the last post about how text analytics is transforming the employee survey, a central part of our Workometry platform. However in 2015 we saw a strong demand in text-analytics-centric work across HR.

Some of my favourite uses of text analytics have been from looking at 360 review data and even with operational data from a large shared service centre. Text projects almost always involve graph analysis and prediction.

In 2015 we’ve learnt that few HR tech vendors have ever expected the data in text fields of their data to be analysed. It is no longer, unfortunately, a surprise to find numerous systems-generated quasi-xml littering a text-field data extraction. 2015 was the year we got very good at regular expressions.

HR Analytics platforms have fallen flat (with some exceptions)

What we haven’t seen in 2015 is a growth in the usage of dedicated HR Analytics platforms. I covered these last year in a post on The 5 types of HR Analytics Vendors (they’re the technologists).

I think that HR Analytics platforms fall into a difficult space. On one side we see the rise of more sophisticated analytics in HR systems as standard functionality. On the other there’s a realisation that to do the job well requires business-specific models / good analysts. The platforms find themselves in the middle – arguably in no-mans land.

There are some vendors which we like. In 2012 I wrote about OrgVue and it’s been good to see how they’ve risen. I like what the Talent Lab team have built. In both these instances the platforms arguably have taken a specific analytic need and built a tool which meets that need. They’re not trying to provide an all-encompassing solution.

It’s this specific-need route we’re taking with our two products. Workometry can be used by analytics teams in their models – the datastore can be queried directly by the clients’ team and the data comes in a very modelling-friendly manner though of course it can be used as a service in it’s own right. We’re currently working with a few clients on another, early Beta product which takes ‘standard’ data from HR systems and automatically generates the type of features / variables that are typically needed in predictive HR models. I suspect this will end up being an API service.

2015 conferences

I suspect that we’re getting close to saturation with People Analytics conferences and that over time People Analytics will just become part of most HR conferences.

In 2015 we did over one conference presentation / chair a month and it was great to see them moving from ‘why do analytics’ to really sharing some successes. It was also flattering to find clients talking about some of our work at conferences, sometimes even without our prior knowledge.

If I had to pick one presentation that I enjoyed doing in 2015 it was the one that I gave at HR Tech Europe in London on ‘Open Source People Analytics’ where I tried to show what People Analysts actually do using tools such as R and RapidMiner.

During the hour demonstration I showed how to visualise data using ggplot2 with labour market data via calls to quandl. Using R I did live analysis of the conference twitter stream and geospatial analysis using UK supermarket locations, including calling APIs to get driving times between locations. I demonstrated building simple prediction models using RapidMiner. Of course being live things didn’t completely go to plan but it was huge fun and I got great feedback from the audience.

2015 thanks

There are far too many people and teams to include here so apologies if I miss you out.

Of course the first and biggest thanks goes to the OrganizationView team for doing such great work, being always open to try new things and for putting up with my demands to discuss algorithm selection. We achieved great things & I couldn’t have achieved it without them.

A very close second goes to our clients. We’ve had some great problems to solve this year and of course they pay the bills. We can all say we’ve learnt a huge amount helping solve some thought-provoking and challenging issues.

We couldn’t do what we do without the great work that the R community has done this year. R really goes from strength to strength and should probably be the default choice for HR analytics teams. I suppose a related thanks must go to all those who pose and answer questions on stackoverflow.

Finally, a big thanks to my peers in other analytics firms and those who are providing great services & tools to this ecosystem. Even though we often compete I think in general the community has made a big contribution to raising and refining the awareness of People Analytics.