Assorted measuring tapes all tangled together.

A Measured Approach to Performance Measurement

By Chally Kacelnik

You’ve probably heard some variation on this saying attributed to Peter Drucker: ‘what gets measured gets managed’. That makes a lot of sense: if you place a spotlight on an aspect of performance, you’re going to manage it the best you can so you can improve or justify what you’re doing. What happens, however, when you’re managing the wrong thing?

I often see organisations coming up with things to measure based on what they can easily extract from their systems, or constructing measures that will look good based on what they have to work with or what’s occurred. I don’t mean to imply that this is because they’re being wily and duplicitous. Rather, it tends to be a result of the organisation setting goals without specific targets and measures attached – or at least not ones that they’ve thought through.

Good performance management starts with setting the right goals and making sure you have the means of measuring performance against them. This means tackling the difficult questions of how the organisation is really doing and how you can know it. Otherwise, rather than managing performance, you’re stuck managing the measures. Time and effort spent struggling to retrospectively figure out what you can do with the information that you have means time and energy not spent on improving performance.

One performance measurement space in which organisations often slip up is customer service.

Shelved, lopsided black binders

Managing Customer Data the Right Way: 10 Basic Principles

By Chally Kacelnik

Let’s consider how to manage customer data the right way: ethically, usefully, and achievably. With scandal after scandal about the misuse of customer data making headlines, it’s no longer possible to think of data as something neutral or passive that gets collected and sits out of sight and mind.

We’re seeing rapid changes to how we think about the collection and use of data. One major theme of the day is technological innovation. Traditional boundaries are being tested, with even governments feeling out how public and distributed innovations like blockchain could work for them. Another theme is dread. There’s a distinctly dystopian resurgence of anxiety about surveillance and poor or actively harmful data management, and unfortunately we’re seeing that suspicion justified. It’s vital for organisations – particularly government ones – to understand that data is fraught and to take a thoughtful approach to the ethics, reach, volume, and scale of the data they collect and use.

Within organisations, there tend to be two opposed approaches. There are those who trust in tech to solve data problems, the more innovation and more data collected the better, even if it’s not fully understood. Then there are those who trust their own workarounds more: the people who have their paper folder or their spreadsheet sitting on the side, whether because the system isn’t set up usefully, there isn’t an established way of working that encourages the right kind and quality of data isn’t being entered, or because of habit and comfort.

Increasingly, the uses and abuses of data are so top of mind and so poorly understood by most of us that organisations are tending to throw everything at the wall. It’s all too common for organisations to take the approach of investing big in new systems without understanding how to drive them effectively, ending up with messy data that causes lots of headaches, rework, haphazard ways of working across the organisation, and reinforcement of those two opposed approaches (including workarounds on top of workarounds!).

In short, if you throw everything at the wall, you’re going to find cracks. Technology isn’t a wilful force in and of itself (at least not yet!) and should be a facilitator rather than a driver. Positive, active human behaviour should drive how we interact with data and how we use technology to facilitate that interaction. Let’s get back to the very basics of what organisations need to do with data: serve their customers.

Here are seven principles for achieving that aim by managing customer data well at the ground level: