In recent ActiveOpinions we have spoken of the importance of how you measure the performance of back office operations.
In the era of big data, it’s never been easier to collect data and devise metrics, but just because we can measure something, doesn’t mean that to do so is beneficial to optimising performance or motivating people (which after all, is what really matters!).
Likewise, the warehouses (or increasingly, lakes) of data we now have easy access to may not actually be able to drive the insight needed to make sound operational decisions.
So, measuring what goes on in the back office is not always as easy as it sounds. In this article, we aim to shine a light on what to consider so you get the best from your current approaches.
We also share our views on what to measure and discuss where the data you need comes from.
The first and most obvious consideration is why do you want the data and what are you going to do with it? There can be many valid reasons for measuring performance of the back office, for example:
- Baseline – to document performance levels now, so you can see how they change once some improvement initiative has been implemented.
- Inform – to target where to deploy different types of improvement initiatives. Which activities are consuming enough time to deliver a payback from an RPA project? Where should we focus retraining?
- Sourcing – to change where the work is done, or whether it’s best done internally or with a 3rd party service provider.
- Compare – understand the variation of performance between individuals, teams, units?
- Account – to work out how much it costs to produce different units of output?
- Optimise – to deliver better outcomes by improving the management (through better decision making) of the operation.
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