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Measuring performance takes some time and effort. But is that a good reason to rely only on readily-available data?

Microsoft’s Workplace Analytics uses data that’s easy to capture: email subject lines, time stamps and calendar data from people’s use of their Microsoft applications. From this data, Workplace Analytics creates behavioral metrics such as these:

• time spent in meetings
• time spent collaborating
• size of internal network
• manager and employee one-on-one time
• workload distribution
• travel time to meetings
• time worked after-hours

It’s too tempting to dive into analytics tools with ready-made measures.

Someone else has done the hard work. It’s now as easy as clicking buttons. This is the magnetic appeal of dashboards.

But if you dive in without a clear orientation, the deeper you dive the more likely you’ll lose your sense of where “up” is, and drown. Like a novice big-wave surfer being dumped by a 10m wave.

Microsoft say, on the back of Forrester research finding that employee productivity is the highest priority for C-level executives, that their new tool, Workplace Analytics, “provides unprecedented behavioral insights that can be used to improve productivity, workforce effectiveness and employee engagement”.

The hard work has been done for us, and the pretty screen-shots tempt us to dive right in. But the risk of drowning in all that data is very real.

When we aren’t clear what outcome matters to us, any measure will do.

And what the measure will do is waste our attention and energy on stuff that doesn’t make worthwhile differences. We dive into the data and get carried away in the current of clicking.

It might be interesting to know Network Size and Network Diversity for our workforce, to slice and dice to explore where and who and when. But to what purpose? Just because it’s available doesn’t mean it’s useful.

Even when we are clear what outcome matters to us, any measure put in front of us can blinker us.

Measures like Time Spent in Meetings and Time Spent Collaborating might sound like they’re telling us about employee productivity, but they’re not. Productivity is not just the input of where time is spent, but also the output created by how we spend that time.

We come to dashboards and analytics tools prematurely.

Data automation, dashboards and analytics tools are all great ideas. But we come to them too soon. They are almost always built on data that is readily or easily available. Limiting our performance measures to data that is available will make sure we never get the data we really need.

We need to come to data automation, dashboards and analytics tools after we’ve clearly defined the goals that matter, and designed the measures that give us direct evidence of those goals. We need to design these tools to answer the questions that matter, not give us answers to questions that are easily answered.

Limiting our KPIs to data that is available makes sure we never get the data that we need.
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DISCUSSION:

Do you have data in your dashboards or analytics tools that is there just because it’s available? How do you get the data you really need, when it’s not already available?