Case #1: Business outcomes from People Analytics - Process Gaps

Almost all talks with C-level executives about People Analytics starts with "what business outcomes would we get?". I understand this question very well. It's the simplest way to get to the point, but there is an important reason why it's not working that way. Guess why... Nobody knows your context as well as you and context is crucial.

But don't worry now, there are some facts that should work, and that's why I decided to define a list of business outcomes you can get from People Analytics. Today we start with case #1 - Process Gaps.

How to use People Analytics data to discover process gaps?

People analytics is a data-driven approach to managing people at work (click to read more). You can use solutions like Lab1 to discover lots of patterns in your organization.

I will start to analyze my case with my own data. I collected all passive meta-data from my activity between January 1st and February 28th. All those data are shown on the chart below.

Figure 1. Daily activities analysis

Source: Lab1 solution.

As you can see, Lab1 divides all activities into different categories like collaboration, focus, research and others. Collaboration is the time spent on video calls, mailing and so on. Focus time is the one spent at least 30 minutes on working without being disturbed. Research time means you are looking for information or reading. And the last one is for all other activities.

Looking at a chart can bring you quick ideas when you feel the most productive and a lot more, but it's not the point here. The point is if my distribution of time is good for me as for the CEO?

Well... we have to move back to the context again. As Bill Gates said, read one book per week - if I could prove that my avg. 3 hours of research are spent mainly on reading - then it's OK. But I know it's not. A lot of my research time is spent on serving my employees in different ways. It shouldn't work like that, but I think at this stage, when we try to compete in new market it can be forgiven. I am also very satisfied with less collaboration time. It's mainly because refreshing email all the day does not give you much.

Ok, but how to use it to discover process gaps?

Let's do the same short analysis, but instead of my personal data, let's take any department. Let's say Customer Service. We have 500 people sitting and waiting for customers who needs help. Even executives see that there is lot to do, customer satisfaction is average (let's say 82%). You draw the same chart as above to all your people and you see that collaboration takes only 4:20 per day. Research time take more than 2:30 hours. Should it work like that? With activity at almost 7 hours your employees may be tired (believe me). Is this the best way of spending their time and your budget?

Probably not. At first sight it looks like digging for information take too much time. Probably your knowledge base is poorly adopted or the process is badly designed. Now with activity meta-data you can prioritize which aspects of your business keep in mind first.

Thanks to objective data, you can assess the value of improvements and check is goals are achieved. In our scenario, even simple changes can bring great outcomes.

	Before change	After change
Employees:	500	500
Time on collaboration:	4:20	4:50
Time on research:	2:30	1:50
Custommer satisfaction:	82%	89%
ROI (lower costs per customer, higher incomes from happy customers, lower rotation in customer service):		from 3.5 mln $...

Well done our example company! Stay tuned for next episodes and feel free to start discussion below.