Are remote teams productive?
Since the mass adoption of working from home [some 35% of Americans now say they have the opportunity to work remotely five days a week], this is a question that’s understandably on many people managers’ minds.
But because of this, it’s should also be an obvious question for analytics teams to tackle too.
Instinctively answering this seems simple enough. Productivity is – after all – just output divided by input. All we need to do is add up the outputs and inputs and we’re done.
But, if you’ve ever tried to measure the productivity of a team or department you will know that in most cases, it is anything but simple.
Many outputs to measure
At the crux of the problem is the fact that there are many different outputs that can theoretically be measured, and almost all of them are somewhere between hard and impossible to actually measure.
And even when attempts to do some sort of analysis is followed, it can often lead to managers lamening “whoever came up with this has no idea what my business is like.” [See W.Bruce Chew’s No Nonsense Guide to Measuring Productivity, in Harvard Business Review).
A more practical solution?
However, there is a different approach to answering the business question that is much more practical. Instead of framing the question as “What is the productivity of the team?” we can frame it as “Are there teams where productivity appears to be poor?”
This latter framing of the question takes us down an easier path, as well as focusing on our area of greatest concern – that is, we are not so interested in productivity in general; rather we are more interested in places where it’s poor and can be improved.
So how do we do analytics to answer the question: “Are there teams where productivity appears to be poor?”
Indicators rather than measures
Well, we don’t so much need “measures” as “indicators”. For example, if emails are not being responded to in a timely manner now that a team is remote, then that is an indicator that something might be wrong.
If deadlines are being missed that’s also another indicator of a possible problem.
Complaints from internal or external customers about the team are yet another easy-to-collect point of evidence.
These indicators don’t tell you what the productivity level is, but they do tell you whether someone needs to look closely at what is going on in that team.
You’ll notice that this approach to analytics leads us to having someone going in to look carefully at a specific situation. There are a great many reasons why one of the indicators may be troubling and a great many possible actions to take.
But the point to remember is this: no matter what you have on your analytics dashboard, you can’t know the causes or solutions of a productivity problem without a close look at the individual team.
Having indicators is all we need to move things forward. Gathering indicators instead of measuring productivity may sound unexciting for a team of data scientists. Nonetheless, in most cases, it’s the best approach to business analytics.
[Special thanks goes to Dr John Boudreau for the insights on analytics he has shared and to the HR pros I’ve taught for their practical wisdom].