What is evidence-based?
A concept that is increasingly being mentioned when it comes to changing and improving organizations is evidence-based. It is also the way of working that the The Better Company stands for. But what exactly does it mean; evidence-based? Literally, it means to rely on evidence. So evidence-based improvement is a way of improving where you rely on evidence provided.
Evidence is the basis
But what evidence? What form of evidence are we talking about here? Here we are talking about companies and organizations, that is, systems of people and teams working together, to which you want to change “something,” to improve, for example, the degree of alignment, engagement, integrity or inclusiveness. Evidence here means that if you want to achieve “something,” in the form of a better outcome, you have to do one thing but not another. Instrumental evidence, then, which seriously increases the likelihood that if you deploy a particular “tool,” it will result in a desired outcome.
To achieve A, you must do B and leave C. There is a relationship between A, B and C, between outcome A and instruments B and C. It has been established time and again that if you bet B, you get A; bet C, you don’t get A. A heavily simplified example, purely for illustration: you want to create strength by joining two boards together with a nail. Take a hammer (B), and you get the nail driven into the wood creating the desired strength (A); take a saw (C), and your nail won’t enter the wood (no A). This is a given, this is how it works, how one thing and another does not lead to the desired outcome.
Evidence-based and the role of science
That this is how it works, that this has been established and demonstrated time and again: there, right there, is the proof. But who or what provides this evidence? That now is exactly the role of science. From basic science, but also from applied science as in management, organizational and behavioral science. To provide independent and objective evidence for the hypothesized relationship between a wide variety of factors (instruments) and variables (outcomes). Science is like a warehouse. A warehouse full of knowledge of proven relationships that keeps getting fuller. Because science never stands still, we know more and more.
How science works
The science enterprise runs on observation; it is its fuel. She considers all that is observable (the empiricism) in a persistent ambition to be able to explain everything she sees. Why does something happen the way it does? Can we explain that? Explanation is the holy grail of science, it is what drives it and it is never finished. In management, organizational and behavioral science, organizations, teams and people are observed. Why do they function as they do? Why is there more alignment in one case than the other? Why is there less commitment in this case than in that case? How about that?
The essence: cause and effect
Again, the explanation is ultimately found in the proven relationship between a desired outcome and the factors responsible for that outcome. “Every time we see an organization in a high state of strategic alignment (SA), it always appears to be accompanied by a lot of informational justice (IJ) and very little power struggles (PS). And conversely, if the SA is missing, we see no IJ and, on the contrary, a lot of PS.“, to cite just one example. The observed correlation between SA, IJ and PS is the basis. But to speak of true knowledge, the most important thing is to be able to show, to prove from all observations, that SA (the outcome) scores higher or lower because IJ and PS (the instruments) score higher or lower. That SA is the consequence of IJ and PS and vice versa, that IJ and PS are the cause of SA. For solid evidence, of course, you must have been able to establish this “working principle,” this causality, sufficiently often and in a variety of situations; in short, you need enough observations, because you cannot make overnight decisions.
From explaining to predicting
Once the evidence is presented, new knowledge has been added to the storehouse of science. From then on, we can also reverse the direction of knowledge: from explanation to prediction. And we do prediction to purposefully create new, desired situations, not to understand existing ones. Overall, if we already know, based on all the organizations we have seen so far, that IJ and PS are the cause of SA, then based on that knowledge we can effectively start “building” more or better SA in each subsequent organization we see.
Success as a logical and predictable consequence
In this reversal of the direction of knowledge, in shifting, therefore, from explanation by research to prediction by application, therein lies precisely the essence of evidence-based improvement. You literally base your improvement intention on delivered evidence. You are guided by proven science. So if you come into an organization with where there is little SA and a desire to grow it, it is literally wise to focus your efforts on increasing IJ and reducing PS. Don’t start using other “tools,” but limit yourself to those that have been proven to “work. Because then you’re going to make a predictable impact. Then success is not a coincidence, but a logical consequence.
Evidence-based improvement always starts with the desired outcome
So evidence-based improvement always begins with the explicit question of what you want to improve in/about the organization. What desired outcome do you want to create? Stronger alignment? Greater commitment? More inclusiveness? Depending on the answer to that question, you can then consult the warehouse of science in a very focused way. Looking for those factors that have already been proven to relate to the desired outcome. In purposefully creating desired outcomes, we prefer to speak of “drivers” rather than factors. To indicate that we are dealing with driving forces “behind” a socially engineered outcome, for which we must take action to turn them “on.
The importance of measuring (properly)
But if a desired outcome like strategic alignment (SA) is always promoted by informational justice (IJ) and undermined by power struggles (PS), then we can get straight to work, right? Because wherever there is a desire to increase SA, we can therefore immediately put in place specific interventions designed to increase IJ and decrease PS. If only it were that simple, it is unfortunately a little more complex than that. In relation to alignment alone, science to date has identified over 40 different drivers. Having to focus your change and improvement efforts on 40 different drivers is a hopeless task. The good news: it’s also not necessary. Just because we know in general terms that there are 40 drivers by which you can develop and strengthen alignment in a targeted way, does not mean that all 40 are relevant in a specific case. In the unique context of a specific case, there are usually only 3 to 6 drivers of alignment boxing. That creates a huge focus, which makes the world a lot clearer. But how do you figure out which 3 to 6 drivers that are in your case? By measuring. But then you have to measure properly.
Measuring well, how do you do it?
First and foremost, you can only measure well if your measurement instrument is validated. A validated instrument measures the quantity you want to measure in reliable units. With a scale, you measure weight in kilograms. With a thermometer, you measure temperature in degrees Celsius. So if you want to know what something weighs you should not use a thermometer and a scale, you should use the right measuring instrument. At the same time, the measuring instrument must be reliable: the unit of measurement must be correct. If something weighs 27 kilograms, your scale should show exactly that number of units of measure, no more and no less. Because otherwise your data will be unreliable and you will draw wrong conclusions.
Validated measurement tools for reliable data
Desired outcomes such as alignment, engagement and inclusiveness and underlying drivers, validated measurement tools exist for those as well. That too is an important benefit of science, behavioral science in our case: developing and making available well-validated measurement tools for behavioral constructs. For weight, the measurement instrument takes the physical form of a scale and for temperature that of a thermometer; for behavioral constructs, the measurement instrument takes the physical form of a multiple-question question (usually 4 to 6 statements) that is the subject of an interview (small-scale measurement) or survey (large-scale measurement). The exact same applies here: only if the multiple-question survey is validated will you collect real data that reliably represent the outcome or driver in question.
Garbage in, garbage out
Measuring well, with validated instruments, it seems trivial. But practice proves entirely different. There are many employee surveys in the market, e.g. for engagement, that respond to pragmatic desires and practical concerns: the measurement should not require too much time from employees and, above all, it should cover many different topics. The solution is then typically found in coming up with short question statements yourself or in shortening validated, multiple question statements. But in doing so, you pay a fatal price: a flawed survey design undermines the validity of the measurement, and if the measurement is not valid, you do not obtain real data. If the data are not real, then everything you base on them is unreliable: your conclusions, your decisions. ‘Garbage in, garbage out,’ it also applies here.
Good math, what does that require?
Good measurement is very important for another reason, and that is for math. You can do math only with real, reliable data. And math is necessary to arrive at that selection of 3 to 6 drivers that are most important in your unique case. Think of it as a control panel with 40 buttons that you can all press to achieve a particular outcome. Basically, all the buttons are relevant, but which few buttons are going to make the difference in your case? Finding that out, what does that require of the measurement tool? That requires you to build an integrated measurement tool that allows you to measure “above water” – i.e., in the visible upper stream of the organization – against the desired outcome you want to create (e.g., more alignment) and at the same time allows you to measure “below water” – in the less visible undercurrent of the organization – against all of the known and proven drivers that affect the outcome to be created.
Statistically significant correlation
By measuring holistically – proven drivers in relation to desired outcomes – you collect data on both cause and effect and we can statistically test which causes are most important in the unique context of a specific case to start “tinkering” with targeted improvement interventions. Namely, those are the few drivers that, according to the data, show the greatest, the strongest influence on the measured outcome. Thus, it involves finding and establishing statistically significant correlations. So that you know exactly which buttons to start pushing in your unique case to make work toward a better outcome with predictable impact. This is still evidence-based improvement, but funneled, with an extreme focus, and thus even more powerful. Achieving more by doing less, an atypical consulting proposition, that’s essentially what it comes down to. Only want to press those 3 to 6 buttons that are going to do something in your case and leave all other buttons nice and alone. ‘Less is more’.
Never look at average scores
Looking at statistical consistency, that too seems trivial. But practice, once again, proves entirely different. Many – if not all – employee surveys we see in the marketplace do not make a conscious connection between outcomes and drivers at all. Most of all, everything is measured (we will continue to speak consistently of drivers below, even though that is often not applicable). The best you can do then is look at average scores on the drivers you measure. Where those average scores are often compared to an external benchmark compiled at the level of, say, an industry or a country. Anywhere you score high on average and/or above the benchmark, that’s where you’re fine and you don’t need to do anything. Where you score low on average and/or below benchmark, that’s where you’re not right and you need to start improving or changing. A very evil and especially dangerous practice in our view. Because you are missing crucial information to arrive at the right decisions. What if you score low on average on a driver that has no correlation with the desired outcome? Which means you don’t know the latter? Then you are going to invest a lot of time and energy in a change or improvement that will have no effect. And what if, on average, you actually score high on a driver that does correlate with the desired outcome? In which, again, you don’t know the latter? Then you will decide not to do anything (“We are already scoring high, right?”), when in fact we need to, because the average high score is apparently not yet high enough.
Science made practical
The warehouse of science is big and getting bigger. A growing arsenal of proven consistency (between outcomes and drivers) and of validated measurement tools (for those same outcomes and drivers). Where should you go to find the right knowledge for your improvement issue? Consulting this warehouse is what The Better Company has already done in numerous management areas: 1) Strategic Alignment; 2) Engagement & Happiness; 3) Diversity & Inclusion; 4) Integrity & Compliance; 5) Sustainability; 6) Vitality & Change readiness. In each of these areas, we have identified and mapped all relevant drivers. And brought together in integral measurement instruments that can rightly be called diagnostic. Each of these measuring instruments has been made suitable for practical application. So that the science behind it becomes available to everyone in practice and can be used in every organization to achieve predictably better outcomes. By working evidence-based to continuously improve the version of yourself, in other words, to be a Better Company. Not simply intervening on the basis of opinion, hope, belief or conviction, but on the basis of knowledge, data and evidence. For being able to make a lasting and predictable impact. That is what Science For Practice makes possible. And that’s what The Better Company facilitates.
