More fun with the accountants – this time Integrated Reporting Standards

Today I’ve been at another iteration of my gig with the road-show accountants’ professional development conference in another city (see my last blog on what my presentation has been about – Key Performance Indicators (KPIs)). I’ll try to blog later in more detail about KPIs for those who are obsessed with their technical characteristics like I am.

In the meantime, another really engrossing topic (for people like me) – Integrated Reporting Standards. I heard a presentation by Mark Hucklesby on the newly developed Integrated Reporting Standards which are currently out for consultation until July.

A quick aside on standards. One of the great things about accountants is that they’re obsessed with standard setting. They have standards for everything and technical committees meeting all the time figuring out new standards.

Standards are great because they bring about consistency, they also get the best minds in the business focused on the technical trade-offs which come up when reporting and how these are best dealt with.

In the broader area of outcomes systems – the way we identify and measure outcomes of any type in any sector –  I really wish there was a parallel structure to the various official and unofficial standard setting that goes on in accounting. Instead of the order the accountants have in their world, our area of broader outcomes reporting is really like the Wild West. Of course the accountants have had about 500 years to get their area sorted while we’ve only been focusing on outcomes in the modern sense of the term for maybe 30 years or so.

The Integrated Reporting Standards are a new initiative which can be seen as a sort of reinvented Tripple Bottom Line (economic, social and environmental). More information on the initiative at http://www.theiirc.org/.

They have come up with a set of six ‘capitals’

• Financial
• Manufactured
• Intellectual
• Human
• Social and relationship
• Natural

I think that calling them ‘capitals’ is maybe a bit obscure for the average person. I would see them as ‘outcome areas’ or something. However, I can see how they ended up using the term capital. They wanted to have the concept that companies take aspects of these six capitals and add value to them. The concept is set out in the diagram below from their draft standards document.

I raised two issues with Mark in the discussion time. The first was whether their had been any consideration of distinguishing between controllable and not-necessarily controllable indicators in the integrated reporting framework. This is a crucial distinction I draw in my outcomes theory work – http://outcomescentral.org/outcomestheory.html#6.

The purpose of integrated reporting is to give investors and others a crystal clear picture of the risks and opportunities a company is involved in. Confusing controllable with not-necessarily controllable indicators lies at the heart of many of the problems arising from misunderstanding of the true underlying risk profile one is exposed to in both the private and public sector. Mark agreed with the importance of the controllability issue. My second point was whether the standards would allow for a range of reporting approaches. He said that the standards did not stipulate any one way of actually presenting an integrated report. This is good news for someone like myself who thinks that the only way of reporting these days is to use a visual approach because of its clear advantages.

Anyway, sometime when I’m wanting a little light reading I’ll delve into the standards and report back in this blog what’s interesting from the point of view of those of us interested in outcomes theory, measurement and strategy.

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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.

Throwing people in jail because they won’t give us the information we want? – The price of indicator collection

As I’m writing this, we have an interviewer from our Statistics Department sitting in the other room asking detailed questions about  our income and expenditure. It’s part of a nation-wide Household Economic Survey collecting information on household expenditure and income. It’s her second visit to us and she’s been here this time for just on two hours – the first visit took about the same time. Over the last two weeks I’ve been filling in an expenditure diary where I’ve had to record all my daily expenditure. Fortunately our interviewer knows what she’s doing and she has stepped us efficiently through the complex questionnaire – but it’s still a lot of work.

We actually don’t have any choice but to put several hours of our time to one side and fill in the questionnaire with her – participation is not voluntary. It’s mandatory, required by the same legal provisions that demand we fill in our Census forms (something that we, coincidentally, also had no choice but to do just a few weeks ago!). Presumably you would be talking about a fine rather than being thrown in jail (but if you refused to pay your fine, I presume that in the end you could end up in jail in the fullness of time).

This is a little personal example of the cost and infrastructure needed to collect indicator information. Being an outcomes wonk I don’t begrudge putting the time aside because I understand how crucial it is to collect information which can be used for indicator and other types of outcomes work. But the cost is something which is often lost on people who blithely demand that programs and organizations – ‘collect comprehensive outcomes indicator information’ – without any thought to how much it’s going to cost to do so.

It also illustrates the point that collecting accurate information can require more than just spending money – it can involve having to use the power of the State to make sure that such indicator information is collected from the people it needs to be collected from. One of the most dramatic examples we have of this is in the road safety area where drivers can be forced to give a sample measuring their blood alcohol level. Again as an outcomes wonk, I love this sort of data. But there are serious limits to any exercise of State power. A mandatory requirement to collect information needs to be used very carefully to avoid serious push-back from those who have to give their time to fill in the information (for example  discussions like the one here about someone complaining about having to fill in a mandatory survey).

Of course the types of examples that people hold up as providing best practice indicator and outcomes data collection tend to be ones where there’s a large data collection infrastructure and often mandatory data collection  (e.g. road safety, recidivism data in the corrections area). They then expect us to come up with similar information about trends in outcomes and causality in areas where we have much less ability to collect information and can’t turn to the backing of the law to force people to provide information.

So the next time someone demands that you collect more indicator information on your program, it’s reasonable to ask the question: 1) how much are they willing to spend (or do they want you to spend) on collecting this information; and, 2) if required, are they prepared to support making the collection of information on the indicators which are relevant to your program a mandatory legal requirement?

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Does avoiding regulatory enforcement represent a success or failure? A Chameleon Indicator.

There’s no doubt, some indicators are a lot more fun than others (although, I must note that people in my trade have a fairly low threshold for ‘fun’). I particularly enjoy ones which can be interpreted in any way you like. They can be called Chameleon indicators.

When developing outcomes DoViews (visual outcomes models) and performance management frameworks for organizations, I often run into a particularly ambiguous type of indicator – the number of regulatory interventions being undertaken by an organization. At first sight, what better indicator for an organization which includes a regulatory outcome as part of its mandate? But there are problems in interpreting these sorts of indicators reflected in a media exchange I heard this morning.

Our national department of conservation is currently embroiled in media controversy over reductions in staff and budgets. As part of the media spotlight focused on it, I heard a discussion this morning about a reduction in the number of times it involves itself in a regulatory process – the number of times it makes legal representations to Conservation Resource Management Consent Hearings.

A media interviewer, interpreting the reduction in the number of regulatory interventions as a failure of the department to achieve one of its outcomes due to it not pursuing it aggressively due to staff shortages,  asked the department’s head: ‘…on the face of it, is it a lower priority? [the regulatory intervention – the department getting involved in Conservation Resource Management Consent Hearings]. The department’s chief (interpreting the drop in the measure in the opposite way) replied:

‘What you are falling into is the trap of judging and measuring our success by the number of cases we take regardless of the outcome. We see [Conservation Resource Management Hearings] [the regulatory intervention] as a last resort. We would rather sit down without spending money on lawyers and work out issues if we can and confine the [Conservation Resource Management Hearings] issues to ones that we really can’t reach agreement on’.*

This is a classic example of the outcomes theory principle: Ambiguity in interpreting outcomes or performance measures/indicators of regulatory intervention when also seeking prevention.

Not having looked into this particular issue, I don’t want to come down on one side or another. I think that both sides are making reasonable ‘face value’ interpretations of the change in the indicator.

How can people setting up performance management systems deal with these regulatory intervention Chameleon Indicators? While people will continue to take different positions in interpreting them in the cut and thrust of media debate, there is a technical approach to the problem which is suggested by outcomes theory.  In order to actually interpret what’s going on with this indicator, we would need to have further information about other indicators. For instance whether there has been an increase in departmental activity focused on getting the parties together prior to potential Resource Management Consent Hearings. I’ve DoViewed it (built an outcomes model) below so that we can get a clearer picture of what’s going on. We would need to get information about the indicator in red in order to be able to interpret the regulatory intervention indicator in black. Even then we could not be certain, just from the indicator in red that the department had been successful in reducing the number of contentious issues going to  hearings (which is what this DoView aspires to). So we would really need to answer the evaluation question which also appears within the DoView.

So the technical answer to dealing with Chameleon Regulatory Intervention Indicators is to always interpret them against the underlying outcomes model (e.g. DoView) of the logic of what the organization is trying to do.  For the theory on showing whether an organization is achieving its outcomes see Duignan’s Types of Evidence That a Program ‘Works’ Diagram and for a practical visual approach see here.

So, I the lesson from all this is that we should never just look at a Chameleon Indicator like the number of regulatory interventions on its own. We should always visualize it in the context of the logic of what it is that the intervention consists of and see what surrounding indicators we need to measure and what impact evaluation questions we need to answer in order to really clearly understand whether or not an organization is achieving its outcomes.

*Reference to the interview can be found on in the outcomes theory article linked above.

Mapping indicators onto a logic model is obvious – but why haven't we always done it?

I was running a workshop today teaching policy analysts the basics of my approach to program evaluation (Easy Outcomes). One of the participants, when I talked about the importance of always mapping indicators back onto a visual model, commented that when you do it, it’s so obviously the right approach that you can’t understand why we’ve not been doing it for years.

The idea behind this approach is that the way we almost always approach indicator work is to eye-ball a list or table of indicators and ask the question of a group of busy people sitting around a table – ‘does this list of indicators look any good?’

Tracking jobs created under the U.S. Recovery Act – when should the attempt at measurement be abandoned?

The default expectation in at least some sections of the U.S. public sector seems to be that it should always be feasible and affordable to both measure and attribute the results of interventions. This is using the term attribution to mean being able to actually demonstrate that a change in an outcome has been caused by a particular intervention rather than being the result of other factors (see here for more on attribution). The recent U.S. Recovery Act is a case in point.  While it’s reasonable to start from the position that you should routinely assess the possibility of measuring and attributing changes in outcomes of particular interventions, you can’t start by just assuming that it will always be feasible or affordable to do this. Clinging to such an assumption, where it is untrue, can result in you either measuring an outcome when the data you are collecting is not accurate, or acting as though what you are measuring (even if it is an accurate measurement of a change in an outcome) is demonstrably attributable to a particular program, when in fact it may not be. 

The error of limiting focus to only the attributable

I am continuing to develop a set of articles which outline various problems which are often built into the outcomes systems which I see. The one I have just put up is on the Error of Limiting Focus to Only the Attributable. This is where the whole emphasis of a performance management or other outcomes system is just on holding a provider to account for a list of demonstrably attributable indicators (often called outputs, deliverables, or key performance indicators). This often creates problems.

Unalterable deliverables and program inflexibility

Back blogging now after having been on holiday. Recently I ran into the problem of unalterable deliverables in a project I am involved in. This problem was also mentioned in the UN report on its results-based management system that I blogged about a couple of postings ago. The problem arises where a project is set up and deliverables are set, but where ideally there needs to be some flexibility regarding deliverables as the program develops over time. Sometimes the problem is just a result of the difficulty of changing deliverables.

Intense analysis of the U.N. Results-Based Management System

I have just put up an Outcomes Theory Knowledge Base article which is an intense analysis of the United Nation Results-Based Management System. (Its obscure work, but someone has to do it!). The exciting part is that it has let me road-test my new Outcomes Systems Checklist. This now provides a common framework for analyzing any outcomes system – outcomes systems being any system which attempts to identify, measure, attribute or hold parties to account for outcomes or the steps which it is thought lead to them. A 2008 report from the U.N. itself on its Results-Based Management System said that the system was: ‘an administrative chore of little value to accountability and decision-making”.

The single list of indicators problem

Many results management, performance management and monitoring systems suffer from what is called the ‘single list of indicators’ problem. I have just put up an article on the Outcomes Theory Knowledge Base regarding this problem (the URL of the article is at the bottom of this blog posting). It arises in situations where there is a demand that an indicator list be high-level (i.e. not at the output level) but at the same time that the list be able to be used to hold a program, organization or other intervention to account. Often one list cannot be used to do both of these jobs. There are four things that can happen in regard to single list approaches, all four create problems and can lead to undermining the credibility of the outcomes system in which they occur.