Separating reporting and analytics is (usually) a bad idea

One of the common things that HR does when building an organization to deliver analytics is to separate reporting and analytics. In many instances this is a bad idea.

I worked in one such team way back. The HR analytics team would do the statistical work, build measurement and models then (or so was the idea) reporting would run these as operational activities. Problem was that it doesn’t work. Far too many great solutions would not be moved into production effectively, arguably because they were never designed with production in mind.

What are some of the problems?

Analysts and reporting teams speak different languages

A generalisation I know but if you look at the skill sets and backgrounds of the analytics teams and the reporting teams they’re often different.

HR analytics teams in big firms often comprise a large percentage of I/O psychologists. Different disciplines historically have used different analytics tools. I’m an economist by education and economists often use Stata. Psychologists often feel most comfortable with SPSS because that’s what they used at university.

Reporting teams often have a decent number of people with a more traditional BI / IT background. They’re comfortable with enterprise BI tools, relational databases and SQL.

A problem I’ve seen time and time again is that the analysts don’t give enough attention to the process of how their analysis is going to be moved to a production environment. Many are using their tools in a point-and-click manner which makes reproducibility difficult.

Often this difference in background and ways of working breeds mistrust.

The outcome is too often predictable. The analysts end up doing projects which are one-off pieces of analysis. The benefits fail to be realised because the analysis outcome is a slide deck rather than tools to support ongoing decision-making.

Great reporting needs predictions

The other issue is more fundamental, and that is what information is needed to create good reporting.

There are two good reasons to do reporting. Either you want information to enable decision-making or you want to change behaviours. In both instances reporting which includes predictions will likely be more effective than that that only presents historic data.

We all make decisions based on our assessment of what is likely to happen in the future. With traditional reporting we do this be presenting data on past events and leaving it to the audience to predict what is most likely to happen in the future. Lots of studies show know how poor we all tend to be at predicting the future!

Good reporting takes a different approach. It starts with the decision that needs to be made and then uses past data and a predictive model to explain what we expect to happen.

A good example of such a graphic is the Bank of England’s fan chart, an example of which is shown above. The fan shows a range of probable outcomes based on the model. You will see similar charts in finance or in scientific publications.

Using models like this in reporting can be shown to increase the effectiveness of decision making. In our experience it can also increase the demand for reporting.

Multi-disciplinary teams are the way forward

Over the last 5 years of OrganizationView we’ve learnt how to deliver great reporting for HR clients. Our finding is to do this really well you need a multidisciplinary team:

  • statisticians are needed to correctly interpret the data and build predictive or forecasting models
  • technologists are needed to automate report generation and distribution. They’ll probably be needed to move the statisticians models to a production environment.
  • graphic designers / data visualisation specialists are needed to design visualisations that are effective and highly professional. We target the quality of a good company report or the visualisations created by the likes of the Financial Times, Economist or New York Times.

Experience has shown that all these people need to be engaged at the beginning as recommendations and work of one will impact the work of the others. It’s hard to do that if you separate reporting and analytics.