Models like the Balanced Scorecard, the Performance Prism, Triple Bottom Line, and so on offer various approaches for measuring different aspects of business performance. You may or may not be one of the organisations that have successfully put such frameworks to good use, ending up with a collection of useful and useable performance measures that you regularly and easily use in your decision making.
It you are one of those organisations, this article will likely confirm what you already know to be true about successfully measuring performance. If, on the other hand, you aren’t one of those organisations, this article will give you some clues about what’s really involved in getting a collection of useful and useable performance measures.
Essential activities of performance measurement
If you want a collection of useful and useable performance measures that lead to improvement of performance, then certain things must occur. You must:
- Decide on what you should measure and how you will measure it;
- Identify and collect the data for those measures;
- Make the data available to those people and systems that will analyse it;
- Summarise and analyse that data to turn it into performance information;
- Communicate that information to the people who will use it to make their decisions;
- Interpret that information so implications for the business are understood; and
- Use that information in deciding what actions to take to improve performance.
These seven activities describe the process that brings performance measures to life. How many of these activities have you been aware of in your business? Do you know who the departments and people are that contribute to these activities? Do you have a good idea about the kinds of resources that are needed to properly perform these activities? Do you appreciate how much time and effort is involved in properly performing these activities?
Performance measurement frameworks (such as the Balanced Scorecard) give you a hand with some of the first activity, deciding on what you should measure. But it’s unlikely these frameworks would have even acknowledged any of the other six activities essential to bringing performance measures to life. Unless you already have processes, skills and resources allocated to the other six activities, these frameworks for selecting measures probably didn’t get you any further than having a document that listed, and maybe defined, your ‘Key Performance Indicators’. Hardly enough of a return on all the time and effort and money you invested with the expectation of having quality performance information to help you manage your business. But don’t throw the towel in yet. If you have gotten this far, then whatever you have is a foundation for improvement. Improvement of your organisation’s performance measurement process.
Performance measurement is a process
The seven important phases of the performance measurement process all play a critical role in the value that performance measurement can bring to your organisation. These seven phases flow together in an ongoing cycle of measuring, monitoring and applying performance measures. PuMP® is the name for this system of seven phases of the performance measurement process, and each phase is explained below, along with the typical activities that are needed.
Phase 1 SELECT: choose & define what’s worth measuring
Choosing and defining what’s worth measuring for your organisation involves:
- Decide what specific results should be measured. Usually these things are decided through the strategic and operational planning processes and end up being written as critical success factors, or objectives, or goals or priorities. The language will depend on your organisation.
- Design measures that give the best evidence of those results. Brainstorming, just seeing what you can do with the data you already have, measuring what you’ve always measured, benchmarking to find what others measure and hiring consultants to tell you what to measure are all approaches you want to avoid, at least until you have really thought through the kind of evidence that will let you know the degree to which your results have been achieved.
- Define performance measures to specify the operational details of how to bring them to life. For each measure, before you can bring it to life, you need to formulate how it is calculated, identify the data you need, decide its reporting methods, define its signals and agree how to take action, know who is best to own it.
Phase 2 COLLECT: gather data which has integrity
The process of collecting performance data is critical to its integrity and can be very resource intensive. It’s worth giving serious consideration to how you will go about it, so it you data can be “fit for purpose”. This phase can often involve the following activities:
- Define the data requirements for a collection of performance measures you want to report. Extract from the measures’ definitions the specific items of data, where the data is and how to extract it. It’s like an action plan for gathering the data that will go into the performance report.
- Design, improve and implement data collection systems to optimize data availability and integrity. Not all the data you need for your measures will be available, and even if it is, it might not be accurate or reliable enough. Designing data collection systems is a fairly big task and to do it without great waste and cost, expert knowledge or assistance should be sought.
Phase 3 STORE: manage the data so it’s quick and easy to access
Where and how you store your data directly determines what data you can access, when and how quickly you can access it, how easy or difficult it is to access and how much cross-functional use you can get out it. To avoid the pitfalls of assuming that data is easy to get your hands on, know that the following activities will likely be needed in bringing your measures to life:
- Use a data referencing model to make data management cost effective & enable cross-functional use of data. A data referencing model maps out how individual data items and named and organised in your database systems. Your organisation’s IT department may already have a data referencing model, and if so, it will help you find and extract the data you need for your measures. If they don’t have one, then you’ll need to help them out by explaining your measure definitions to them, so they can get more information about how to design database systems that will better serve your information needs.
- Extract, integrate and prepare data for analysis. There are some business intelligence systems that can automate the calculation of your measure values for you. However, most database systems are so complex that you can’t just pull your performance measure values straight from them. You often need to extract the subset of data you need (e.g. by running queries), and organise this subset in a spreadsheet where you can create your measure values yourself. When you are bringing different sources of data together, a challenge can be no obvious way to link your data together (e.g. trying to link employee training records with their years of service without having a unique employee number to match the two sources).
phase 4 ANALYSE: turn the data into information
Analysis turns raw data into information. Make sure it’s the most appropriate information by adopting the simplest analysis approach that can produce the information in the form required to answer your driving questions. Analysis activities usually include:
- Choose analysis techniques that produce performance information that answers driving business questions. You need to be able to clearly articulate the questions you designed your measures (in phase 1 SELECT) to help you answer, because that’s the key to choosing the right analysis method. Don’t create totals for each department of your organisation if your question is about change over time. Instead you’d need totals by week or month so you can examine the time series.
- Apply analysis procedures to raw performance data. Working again with your spreadsheet, this means summarizing your raw data into totals or averages or ratios for each time period, such as week or month. It might also mean performing some analysis on this summary data, such as a correlation analysis, trend analysis or statistical process control.