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about PuMP® :: the performance measurement process

There are many models these days that are used to design performance measures and KPIs. There's the Balanced Scorecard, the Performance PRISM, Triple Bottom Line (Quadruple Bottom Line), the EFQM Excellence Model (European Foundation for Quality Management), the Baldrige Criteria for Performance Excellence, and more. But all these models are simply great ways to think about what aspects of performance in your organisation you should measure, they don't tell you how to design and bring to life those performance measures and KPIs. Even if you use one of these models, you are at risk of making some common mistakes in implementing performance measurement.

What’s been missing in the world of management is an end-to-end approach for how to bring performance measures and KPIs to life. PuMP® is this end-to-end approach.

Hard work is much simpler when you have a way to turn the huge mountain of work into smaller, bite sized pieces. It's a bit like renovating a house, or planning a holiday or even just doing the housework - it's so much more motivating when you break the job down into digestible chunks. It's the same for performance measurement! The PuMP® methodology will help you easily and consistently design, build and use your performance measurement system!

PuMP® helps you design useful and usable performance measures and KPIs!

PuMP® is one of the only methodologies (maybe the only methodology) that is a process-based approach for implementing performance measurement systems - the "how to" for designing, defining, reporting and using performance measures to improve business performance. PuMP® uniquely breaks the whole job of performance measurement into logical chunks that make the hard slog so much easier. There are seven important phases in building ANY performance measurement system:

	phase 1: SELECT	deciding what is worth measuring
	phase 2: COLLECT	collecting data with integrity
	phase 3: STORE	managing data for timely and easy access
	phase 4: ANALYSE	turning raw data into information
	phase 5: PRESENT	visually communicating performance information to users
	phase 6: INTERPRET	validly interpreting performance measures
	phase 7: APPLY	turning performance measures into performance improvement through effective decision making

Virtually all of the well known measurement methodologies (like Balanced Scorecard, Performance Prism, Triple Bottom Line and others) provide a framework for the first phase of the performance measurement process: deciding what is worth measuring. PuMP® is the methodology that will bring performance measures and KPIs to life - by providing frameworks for implementing ALL activities in the performance measurement process.

PuMP® is a logical and common sense methodology

The seven phases of PuMP® each contribute an essential part of a complete and operational measurement system. So whether you use the Balanced Scorecard, or another model for selecting your performance measures and KPIs, you’ll still need PuMP® to physically bring those performance measures and KPIs to life and make them part of your decision making!

phase 1 SELECT: choose & define what’s worth measuring

Selecting what to measure means being centered on the outcomes that matter most to you and your business. Define your measures by carefully considering what form the evidence of these outcomes takes.

The first phase of the measurement process is where you choose what is worth measuring. This often (and hopefully) involves thinking about the outcomes you want to achieve (as outcomes are usually your motivation for measuring), considering the evidence that will let you know you have achieved these outcomes and then translating this evidence into quantitative or qualitative measures or indicators.

Clues for what outcomes really matter to you can be found in your business plans (such as in your vision and mission statements, goals and objectives and values) and can be identified through defining, mapping and analysing your "end to end" business processes.

phase 2 COLLECT: gather data which has integrity

The process of collecting 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 your data can be "fit for purpose".

When you know what you want to measure, your next step is to check if the appropriate data is available and has sufficient integrity. If the data is not available, you may need to establish a data collection system for it, which will include defining the data you need, where it will come from, how it will be captured (such as by form or questionnaire or electronic sensing) and what the procedure will be for those people that will do the collecting.

Watching out for data integrity means making sure your data are defined and collected in a such a way that ensures they are relevant, representative, reliable, readable and realistic (that is, relevant, representative, reliable and readable within reason).

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.

The best first thing to do with data once you have collected it is to put it somewhere safe. This means storing it in such a way that it maintains its integrity and is easy and quick to access when you need it. Storage systems that are fit for purpose can include electronic databases (such as mainframe or PC), spreadsheets or even paper files.

The key to effectively managing numerous sources of business data is to have a corporate data model (or data referencing model) which documents data item standards, how data items are organised into data sets and how data sets are integrated into data bases.

phase 4 ANALYSE: turn the data into information

Analysis is the process of turning raw data into information. Make sure it is the most appropriate information by adopting the simplest analysis approach that can produce the information in the form required to answer your driving questions.

Turning your data into information is the whole idea of analysis. There are four stages of analysis: summarising, exploring, explaining and predicting. Each requires different levels of skill in statistical analysis and different sophistication in the tools and techniques that perform the analysis.

Whether your analysis is quantitative or qualitative, it is important to choose valid methods of analysis that can be translated into the language of the people who will use the resulting information.

phase 5 PRESENT: effectively communicate the information

In communicating performance information, you are influencing which messages the audience focuses on. Take care to present performance measures in ways that provide simple, relevant, trustworthy and visual answers to their priority questions.

One way to facilitate the process of communicating the information resulting from analysis is to pay particular attention to how it is visually presented. The design of graphs, the choice of language, the use of formatting and the structure and layout of reports can all influence the usefulness and usability of performance information.

Reports are most useful and most usable when they are designed to match the processes of decision making making of the audiences that use them.

phase 6 INTERPRET: translate the information into implication

Interpreting your performance measures means translating messages highlighted by performance information into conclusions about what’s really going on. To turn information into implication, you must discern which messages are real messages.

Especially for quantitative information, statistical thinking is the key to getting the most out of performance measures. Understanding when a trend is a real trend, when a difference is a real difference and when a target has really been met is not as straightforward as it seems. If your eye detects a trend or difference, it doesn't mean it's real! It will probably be due to simple random variation - due to many factors that normally have some kind of impact on performance. Real differences are differences that were caused by something that doesn't normally impact on performance (such as once off events).

Understanding statistical variation is the key to interpreting performance measures validly.

phase 7 APPLY: decide how implication will become action

When you have worked out what is really going on with your organisation’s performance, you are ready to make some decisions about what to improve, how much to improve it by and how to do that improving.

What good is all the effort in selecting and defining performance measures, collecting and storing data, analysing data, presenting and interpreting information if you never put it to use? The last phase of the performance measurement process is to turn your interpretations into implication: to decide what you will do in response to what your performance measures are telling you in order to bring about the outcomes you want.

Integrating performance measures into your decision making is your last chance for performance improvement.

© Stacey Barr
last updated November 05, 2007

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