Accountants, KPIs and dry topics

I’ve just got back from doing a presentation to an accountants’ professional development conference. I’m on a gig where I do several of the same presentation in different cities. The conference organizers gave the presentation the rather mind-numbing title of Using KPI* Reports to Enhance Organizational Performance.

Someone once told me that the way I get on in life is that I’m prepared to spend my time thinking about things (he was actually referring to analyzing KPI lists at the time) which most normal human-beings would find painfully boring.

Now, the great thing about accountants is that they’re a bit like that too –  you can’t scare them with a dry little title like the one above, so I had plenty of people turn up to my session.

The fact is that KPI lists (in various forms) are the central mechanism by which we translate our ideas about what should happen in the world into what actually happens on the ground. They’re a major determinant of the way the world turns out in the end. The accountants are right on the money with this one, sparing 50 minutes or so to talk about how to get KPI lists right is time well spent.

I started off my presentation by critiquing two of the most popular sayings in the KPI world – ‘what gets measured is what gets done’ and ‘organizational objectives should always be SMART – Specific, Measurable, Achievable, Relevant and Timebound’.

The problem with the first is that it results in: ‘what doesn’t get measured, ends up being absent from strategic discussions’. And the second (SMART ) can lead to a nasty organization problem – PM – Premature Measurement. Moving to measurement too fast before you’ve defined your strategy.

The take away points from my presentation were: 1) we need to identify our strategy before we focus just on measurement; 2) the best way to talk about strategy is to do it visually; and, 3) once we’ve developed a visual version of our strategy, we can then simply map our indicators (KPIs) directly back onto this map. This ensures that we have alignment between what we’re measuring and the priorities we’re trying to achieve.

One of the participants asked a key question, which is, ‘what it the best way of working out which indicators, out of a mass of indicators we might have, we should track?’

The simple answer is that the indicators we select should focus on our priorities. Working the way I suggested in my presentation is an ideal way of doing ensuring this. However there are some very interesting complexities around the question of indicator selection which I’ll try to get time to blog about in a few days time.

I’ll post the KPI presentation after I tweak it and do the next presentation.

*Key Performance Indicators, if any of the uninitiated are reading this blog.

Are expert and key informant judgment evaluation designs types of ‘impact evaluation’

Up on the American Evaluation Association Linkedin group, I’ve started a discussion about what are the range of evaluation designs which can be regarded as impact evaluation designs.

I have a typology of seven major impact evaluation design types used in Duignan’s Impact Evaluation Feasibility Check.

At least two of those design types – expert judgment and key informant judgment design types – are not seen by some as being appropriate to be called ‘impact evaluation’ designs. Some want to restrict the definition of impact evaluation designs to types such as Randomized Controlled Trials. Key informant designs are where groups of people ‘in the know’ about a program are asked questions about the program.

My definition of an impact evaluation design is one where someone is making a claim that they believe a program has changed high-level outcomes. In my Types of Evidence That a Program ‘Works’ Diagram (, impact evaluation is conceptually distinguished from implementation evaluation on the basis of it making such a claim.

In contrast, non-impact, implementation evaluation (where you do evaluation for program improvement even in situations where you cannot measure impact) is not trying to make such a claim. I am not saying here that every type of key informant or expert design is impact evaluation, just ones where a question is asked along the lines of: ‘In your opinion did the program improve high-level outcomes’.

I think that if this question is asked, then the evaluation is trying to ‘make a claim about whether a program changed high-level outcomes’. The question of whether particular stakeholders believe this to be a credible claim in a particular situation is a conceptually different questions. And there are many stakeholders who would not regard it as such. However, this does not detract from the conceptual point that, if you can find stakeholders who in some situations would regard key informant or expert judgement designs as sufficiently credible for their purposes, then these designs can be regarded as a type of impact evaluation.

My broader purpose with this thinking within outcomes theory is to get the full list of possible impact evaluation designs considered in the case of any program so that we don’t just get obsessed with a limited range of impact evaluation designs, useful though things like Randomized Controlled Trials (RCTs) may be in some circumstances.

Putting the Planning back into M&E – PME or PM&E what’s the acronym going to be?

In a posting on Linkedin, Leslie Ayre-Jaschke talked about the growth of PME – or maybe it will end up being called PM&E, or something else. Regardless of the acronym, it’s the movement to put planning back into monitoring and evaluation. ‘Putting the P back into M&E’ was the subtitle of a workshop I ran in South Africa for UNFPA several years ago. I think that it’s a concept that’s going to get a lot more traction over the next few years.

It’s consistent with what evaluators like Michael Patton, and many of us in the evaluation community, have been talking about for years. We’ve been talking up the key role of formative evaluation – evaluation aimed at making sure that programs are optimized. And formative evaluation is all about making sure that programs are well planned.

The point of this approach within evaluation is that it’s often pointless to evaluate a badly planned program. Evaluation resources would be better spent on making sure that the program is better planned than on measuring the fact that it often will not achieve its outcomes due to the fact that planning has been poor.

The new PM&E movement is not just about evaluators and evaluation, it is much broader than that taking in people from a range of disciplines. This new integrated approach which is emerging needs an underlying theory which will appeal to all of the different disciplines involved – strategic planners, performance managers, evaluators, contract managers, policy analysts etc. The work I’ve been doing in outcomes theory has been designed to meet this need.

The purpose of outcomes theory is to provide an integrated conceptual basis for PM&E-type approaches. A common conceptual basis is needed if people across the different disciplines and sectors are going to be able to share conceptual insights about how they identify, measure, attribute and hold parties to account for outcomes when doing planning, monitoring and evaluation. Good theory is needed to help them quickly sort out the type of conceptual confusion that current characterizes much of the discussion of outcomes related issues. As the famous social scientist Kurt Lewin said – ‘there’s nothing so practical as a good theory’.

This aspiration of outcomes theory is summarized in the diagram below showing how it’s a meso-level theory reaching across strategic planning, monitoring, evaluation etc.

(see for more on this)

For people just working out in the field, who don’t  need to know much theory, outcomes theory principles have been hard-wired into the DoView Visual Planning, Monitoring and Evaluation approach Using the approach means that they will avoid many of the technical problems which are highlighted by outcomes theory.

Large-scale visual models of a program (drawn in the correct way, for instance as ‘DoViews’) provide the ideal foundation for the new fully integrated approach to planning, monitoring and evaluation which many are now seeking.

Stop the terminological madness now! ‘Outcomes’, ‘impact’, ‘results’, ‘goals’ and the Buffalo Dung Problem

All I can ask is ‘when will it stop’? As we speak I’m burning up bandwidth on an EVALTALK (the evaluators list) discussion about the distinction between a ‘goal’ and a ‘mission’. I’m on Linkedin where people are arguing about the distinction between a ‘result’ and an ‘outcome’ and I’ve someone emailing me from Europe preoccupied about why I don’t draw a distinction between an ‘outcome’ and an ‘impact’ in my outcomes theory work.

I think that Karyn Hicks on EVALTALK has come up with the best term for these debates, calling them the Buffalo Dung Problem! This stems from her being in a meeting involving one of these endless debates and her Director hollering ‘Well #!@ we can just call it buffalo dung for all I care’! From then on she’s called it the Buffalo Dung Problem.

Most of these Buffalo Dung discussions are a total waste of time and we can think about this in terms of there being two underlying issues:

1. These terms are all used in a common sense way by stakeholders to mean roughly the same thing: ‘the stuff we’re trying to achieve’. It’s ultimately futile to try and force the rest of the world to use them in very specific ways that suit us for our technical work. If we were physicists and no one had any common sense uses for our terms – like Boson Particles and Quarks – we could define them how we liked and insist that the people using them use them in a very precise technical way. We simply do not have the power to insist that people use terms in the way we want because we work amongst a wide variety of lay stakeholders who will use terms in whatever way they want to.

2. When we insist on using terms in a particular way we are usually trying to pack into the one term a number of technical distinctions which it is better to tease apart. These distinctions include things such as: 1) where something fits within a causal pathway; 2) whether it’s measurable or not; 3) whether it’s controllable or not; 4) whether it’s going to be used for accountability or not.

For instance in one of the discussions I’m involved in at the moment, it’s being suggested that maybe the term goal should be restricted to: 1) something below a thing called a ‘mission’ within a causal pathway; 2) something that is measurable; and, 3) something that is controllable. The problem is that when we ask an unsuspecting lay person to give us their ‘goals’, they have no way of knowing from just this word that we want a very specific thing from a technical point of view. We want something which has three specific technical characteristics. It’s far clearer to forget the word goal and tell them that we want something that is measurable and controllable by them (distinctions 2 and 3 above). We can achieve our first distinction – the position in the causal pathway – much more elegantly by just doing the whole thing in the form of a visual outcomes model.

A fully visual approach gets rid of a lot of the terminological madness which stems from trying to specify a particular location within a causal pathway, e.g. having to insist that a process is before an immediate outcome and that is before an intermediate outcome and that is before an impact.  When you try to do it in this way you inevitably get people then asking you where a result, goal, mission and vision fit into the schema.

You can eliminate this entire debate by simply working in a totally visual way. You can do the whole work of building an outcomes model visually just by talking about boxes within the model and the end-box(s).  Being a little less extreme, I normally talk about steps and at the end of the steps there are final outcomes.  But I couldn’t care less what people want to call the boxes at the end of the visual model. The visual approach eliminates the need to use words to describe particular positions within the causal pathway – you can just point at them (or if you are not physically present color them up, e.g. the green boxes).

Having eliminated this major cause of terminological stress by working visually you can then next deal with distinction 2, measurement. This is best though of in terms of a measurement being an object you put onto a visual model next to a box. It is something that measures that box. I happen to call these indicators but again couldn’t really care less what you call them as long as you maintain the idea of measuring things.

Then you need to deal with the 3rd distinction – controllability. This is best done by simply marking up the indicators that are controllable in some way. Make them red, put a letter next to them, whatever you like. But just think of it in terms of a particular type of indicator being controllable.

Lastly you need to deal with distinction 4 – whether a party is going to be held accountable for something. This is best dealt with by simply marking up the indicators which a party will be held accountable for. In the public and non-profit sector, these usually are exactly the same as the controllable indicators you’ve just marked up.

It’s as easy as that, you simply do not need the terminological madness so many people are currently involved in. I would love someone to work out the sum total of human time, effort and bandwidth (and hence dollars) which is currently going into these endless terminological debates.

William of Occam was a medieval philosopher who came up with Occam’s Razor – ‘do not multiply entities beyond necessity’. He was trying to stop the the type of madness where people in his time used to make dozens of distinctions between different types of angels. We have the same problem on our hands at the moment with the Buffalo Dung problem. I’m an Occam’s Razor fan myself – let’s just stop the madness!

To see how to work practically in this way as I do and those who use DoView Visual Planning and Management do all the time, see: that link shows you the 13 rules for building elegant but accessible visual models that you can use in the way described above. This url: shows you how you can used the whole process for accountability, evaluation, reporting etc.

Want more detail and references to this thinking? The following is a technical article about this issue (read the summary referenced at the start of it if you do not have time to read the whole article): Duignan, P. (2009). Simplifying the use of terms when working with outcomes. Outcomes Theory Knowledge Base Article No. 236. ( ). The substance of this article formed the basis for Duignan, P. (2009) Rejecting the traditional outputs, intermediate and final outcomes logic modeling approach and building more stakeholder-friendly visual outcomes models. American Evaluation Association Conference, Orlando, Florida, 11-14 November 2009.)

And the following article talks about the different dimensions we get mixed up in our outcomes and evaluation work:

Duignan, P. (2009). Features of steps and outcomes appearing in outcomes models. Outcomes Theory Knowledge Base Article No. 208. ( ).

Paul Duignan, PhD. Follow me on this;; or via my E-newsletter and resources at

Is it the role of an evaluator to always 'value' what they are evaluating?

I’ve had occasion recently to need to think about whether or not the notion of ‘valuing’ something is always an essential part of evaluation. To question this may seem a heresy to some evaluators who see this as the defining aspect of evaluation (for instance as opposed to ‘research’ where they don’t see such valuing as needing to take place). I’m not definite in my thoughts on this issue and below just want to float the argument which has been rattling around in my head for a while and which I have not had a chance to get down in writing to see if it can be shot down – in which case I will change my mind. Continue reading

Untangling evaluation terms – discussing evaluation 'types' with clients often more useful than evaluation 'approaches'

I have just put up a outcomes theory article based on a book chapter I wrote some time ago dividing the terminology used in evaluation into five groups of terms about five different ‘aspects’ of evaluation. These aspects are: evaluation approaches; evaluation types (based on the purpose of the evaluation); evaluation methods; evaluation information analysis techniques; and evaluation designs. Approaches tend to combine a range of different elements including general approaches to evaluation, philosophy of science views and for instance, quasi-political perspectives on the relationship between empowered and disempowered groups. Evaluation approaches are often not mutually exclusive from each other from a conceptual point of view. Evaluation approaches include such things as Scriven’s Goal Free Evaluation, Patton’s Utilization Focused Evaluation and Fetterman’s Empowerment Evaluation. While I find these very interesting from the point of view of stimulating my thinking about evaluation, I often (but not always) do not find them very useful when talking to a client about a specific evaluation.
Continue reading

Formative evaluation versus impact/outcome evaluation

In response to a posting on one of my outcomes theory articles by Marcus Pilgrim who ran the recent YEN Evaluation Clinic in Damascus, I have worked up an article on the difference between formative, process and impact/outcome evaluation. As Marcus points out in his posting, the term formative (or developmental) evaluation is not one which is widely known in all sectors. Formative evaluation is directed at optimizing program implementation. Process evaluation attempts to describe the course and context of a program. Impact/outcome evaluation looks at the intended and unintended, positive and negative outcomes of a program and whether they can be attributed to the program. Continue reading

What we are all on about – representing causal models

Whether we know it or not, a lot of us in evaluation, monitoring, social programs, philanthropy etc. spend a lot of time working with ‘causal models’. We call them all sorts of things – program justifications, rationales, program activities and objectives, logic models, logframes, intervention logics, strategy maps etc. – and most people who work with them don’t think of them as causal models. But that’ s what they are if we see causal models as just being an attempt to set out ‘what it is believed causes what in the world’. In the case of a program, the model is going to be a model of the steps which you think a program needs to take in order to cause high-level outcomes to occur. We really should get our heads around the best way to represent such models because at the moment I think that there is a great deal of wasted talk and effort about all of this. And it distracts us from getting on with the job of implementing good programs as fast as possible. Every dollar or every hour spent on struggling with an inefficient way of representing our program is a dollar or hour wasted. Continue reading

What exactly is 'best practice'?

Identifying and communicating best practice is widely recommended in many sectors and disciplines. But I’ve sometimes wondered as I’ve sagely recommended in a serious voice, ‘I think that we should use an approach based on identifying and implementing best practice here’ exactly what best practice is? I think that doing it is often a good idea and I can work out how to identify it and share it, and I will blog about that tomorrow, but what I’m not clear on is exactly how we define ‘best’ in the term ‘best practice’. It’s not clear whether best practice consists of: 1) claims that practitioners, from their own experience, believe the practices concerned to be feasible and ‘useful’ to implement; or 2) practices which have been proven to improve high-level outcomes (through making a strong outcome/impact evaluation claim of some sort such as is made using some of the types of designs listed here). Continue reading