The unspoken life of information in organisations
Introduction
Many activities in organisations are driven by information. Chief among these is decision-making : when faced with a decision, those involved will seek information on the available choices and their (expected) consequences. Or so the theory goes.
In reality, information plays a role that does not quite square up with this view. For instance, decision makers may expend considerable time and effort in gathering information, only to ignore it when making their choices. In this case information plays a symbolic role, signifying competence of the decision-maker (the volume of information being a measure of competence) rather than being a means of facilitating a decision. In this post I discuss such common but unspoken uses of information in organisations, drawing on a paper by James March and Martha Feldman entitled Information in Organizations as Symbol and Signal.
Information perversity
As I have discussed in an earlier post, the standard view of decision-making is that choices are based on an analysis of their consequences and (the decision-maker’s) preferences for those consequences. These consequences and preferences generally refer to events in the future and are therefore uncertain. The main role of information is to reduce this uncertainty. In such a rational paradigm, one would expect that information gathering and utilization are consistent with the process of decision making. Among other things this implies that:
- The required information is gathered prior to the decision being made.
- All relevant information is used in the decision-making process.
- All available information is evaluated prior to requesting further information.
- Information that is not relevant to a decision is not collected.
In reality, the above expectations are often violated. For example:
- Information is gathered selectively after a decision has been made (only information that supports the decision is chosen).
- Relevant information is ignored.
- Requests for further information are made before all the information at hand is used.
- Information that has no bearing on the decision is sought.
On the face of it, such behaviour is perverse – why on earth would someone take the trouble to gather information if they are not going to use it? As we’ll see next, there are good reasons for such “information perversity”, some of which are obvious but others that are less so.
Reasons for information perversity
There are a couple of straightforward reasons why a significant portion of the information gathered by organisations is never used. These are:
- Humans have bounded cognitive capacities, so there is a limit to the amount of information they can process. Anything beyond this leads to information overload.
- Information gathered is often unusable in that it is irrelevant to the decision that is to be made.
Although these reasons are valid in many situations, March and Feldman assert that there are other less obvious but possibly more important reasons why information gathered is not used. I describe these in some detail below.
Misaligned incentives
One of the reasons for the mountains of unused information in organisations is that certain groups of people (who may not even be users of information) have incentives to gather information regardless of its utility. March and Feldman describe a couple of scenarios in which this can happen:
- Mismatched interests: In most organisations the people who use information are not the same as those who gather and distribute it. Typically, information users tend to be from business functions (finance, sales, marketing etc.) whereas gatherers/distributors are from IT. Users are after relevant information whereas IT is generally interested in volume rather than relevance. This can result in the collection of data that nobody is going to use.
- “After the fact” assessment of decisions: Decision makers know that many (most?) of their decisions will later turn out to be suboptimal. In other words, after-the-fact assessments of their decision may lead to the realisation that those decisions ought to have been made differently. In view of this, decision makers have good reason to try to anticipate as many different outcomes as they can, which leads to them gathering more information than can be used.
Information as measurement
Often organisations collect information to measure performance or monitor their environments. For example, sales information is collected to check progress against targets and employees are required to log their working times to ensure that they are putting in the hours they are supposed to. Information collected in such a surveillance mode is not relevant to any decision except when corrective action is required. Most of the information collected for this purpose is never used even though it could well contain interesting insights
Information as a means to support hidden agendas
People often use information to build arguments that support their favoured positions. In such cases it is inevitable that information will be misrepresented. Such strategic misrepresentation (aka lying!) can cause more information to be gathered than necessary. As March and Feldman state in the paper:
Strategic misrepresentation also stimulates the oversupply of information. Competition among contending liars turns persuasion into a contest in (mostly unreliable) information. If most received information is confounded by unknown misrepresentations reflecting a complicated game played under conditions of conflicting interests, a decision maker would be curiously unwise to consider information as though it were innocent. The modest analyses of simplified versions of this problem suggest the difficulty of devising incentive schemes that yield unambiguously usable information…
As a consequence, decision makers end up not believing information, especially if it is used or generated by parties that (in the decision-makers’ view) may have hidden agendas.
The above points are true enough. However, March and Feldman suggest that there is a more subtle reason for information perversity in organisations.
The symbolic significance of information
In my earlier post on decision making in organisations I stated that:
…the official line about decision making being a rational process that is concerned with optimizing choices on the basis of consequences and preferences is not the whole story. Our decisions are influenced by a host of other factors, ranging from the rules that govern our work lives to our desires and fears, or even what happened at home yesterday. In short: the choices we make often depend on things we are only dimly aware of.
One of the central myths of modern organisations is that decision making is essentially a rational process. In reality, decision making is often a ritualised activity consisting of going through the motions of identifying choices, their consequences and our preferences for them. In such cases, information has a symbolic significance; it adds to the credibility of the decision. Moreover, the greater the volume of information, the greater the credibility (providing, of course, that the information is presented in an attractive format!). Such a process reaffirms the competence of those involved and reassures those in positions of authority that the right decision has been made, regardless of the validity or relevance of the information used.
Information is thus a symbol of rational decision making; it signals (or denotes) competence in decision making and that the decision made is valid.
Conclusion
In this article I have discussed the unspoken life of information in organisations – how it is used in ways that do not square up to a rational process of decision making. As March and Feldman put it:
Individuals and organizations invest in information and information systems, but their investments do not seem to make decision-theory sense. Organizational participants seem to find value in information that has no great decision relevance. They gather information and do not use it. They ask for reports and do not read them. They act first and receive requested information later.
Some of the reasons for such “information perversity” are straightforward: they include, limited human cognitive ability, irrelevant information, misaligned incentives and even lying! But above all, organisations gather information because it symbolises proper decision making behaviour and provides assurance of the validity of decisions, regardless of whether or not decisions are actually made on a rational basis. To conclude: the official line about information spins a tale about its role in rational decision-making but the unspoken life of information in organisations tells another story.
On the statistical downsides of blogging
Introduction
The stats on the 200+ posts I’ve written since I started blogging make it pretty clear that:
- Much of what I write does not get much attention – i.e. it is not of interest to most readers.
- An interesting post – a rare occurrence in itself – is invariably followed by a series of uninteresting ones.
In this post, I ignore the very real possibility that my work is inherently uninteresting and discuss how the above observations can be explained via concepts of probability.
Base rate of uninteresting ideas
A couple of years ago I wrote a piece entitled, Trumped by Conditionality, in which I used conditional probability to show that majority of the posts on this blog will be uninteresting despite my best efforts. My argument was based on the following observations:
- There are many more uninteresting ideas than interesting ones. In statistical terminology one would say that the base rate of uninteresting ideas is high. This implies that if I write posts without filtering out bad ideas, I will write uninteresting posts far more frequently than interesting ones.
- The base rate as described above is inapplicable in real life because I do attempt to filter out the bad ideas. However, and this is the key: my ability to distinguish between interesting and uninteresting topics is imperfect. In other words, although I can generally identify an interesting idea correctly , there is a small (but significant) chance that I will incorrectly identify an uninteresting topic as being interesting.
Now, since uninteresting ideas vastly outnumber interesting ones and my ability to filter out uninteresting ideas is imperfect, it follows that the majority of the topics I choose to write about will be uninteresting. This is essentially the first point I made in the introduction.
Regression to the mean
The observation that good (i.e. interesting) posts are generally followed by a series of not so good ones is a consequence of a statistical phenomenon known as regression to the mean. In everyday language this refers to the common observation that an extreme event is generally followed by a less extreme one. This is simply a consequence of the fact that for many commonly encountered phenomena extreme events are much less likely to occur than events that are close to the average.
In the case at hand we are concerned with the quality of writing. Although writers might improve through practice, it is pretty clear that they cannot write brilliant posts every time they put fingers to keyboard. This is particularly true of bloggers and syndicated columnists who have to produce pieces according to a timetable – regardless of practice or talent, it is impossible to produce high quality pieces on a regular basis.
It is worth noting that people often incorrectly ascribe causal explanations to phenomena that can be explained by regression to the mean. Daniel Kahneman and Amos Tversky describe the following example in their classic paper on decision-related cognitive biases:
…In a discussion of flight training, experienced instructors noted that praise for an exceptionally smooth landing is typically followed by a poorer landing on the next try, while harsh criticism after a rough landing is usually followed by an improvement on the next try. The instructors concluded that verbal rewards are detrimental to learning, while verbal punishments are beneficial, contrary to accepted psychological doctrine. This conclusion is unwarranted because of the presence of regression toward the mean. As in other cases of repeated examination, an improvement will usually follow a poor performance and a deterioration will usually follow an outstanding performance, even if the instructor does not respond to the trainee’s achievement on the first attempt…
So, although I cannot avoid the disappointment that follows the high of writing a well-received post, I can take (perhaps, false) comfort in the possibility that I’m a victim of statistics.
In closing
Finally, l would be remiss if I did not consider an explanation which, though unpleasant, may well be true: there is the distinct possibility that everything I write about is uninteresting. Needless to say, I reckon the explanations (rationalisations?) offered above are far more likely to be correct 🙂
Insights, intuitions and epiphanies: some reflections on innovation and creativity
Introduction
The Merriam-Webster dictionary defines the word innovation as:
Innovation (n): a new idea, method or device.
This definition leaves the door wide open as to what the term means: an innovation could be a novel product to blow the competition away to a new way to organise paperwork that makes it easier to find the hardcopy of the contract you’re after.
Organisations hunt high and low for the magic formula that would enable them to foster and manage innovation. So management gurus, consultants and academics oblige by waxing at length on the best way to inspire and direct innovation (there has to be a process for it, right?). And there’s the paradox: the more we chase it, the further it seems to recede. But that does not stop organisations from chasing the mirage. In this post I present a few reflections on creativity and innovation based on a couple of personal experiences.
The first story
In the early 90s I started working towards a research degree in chemical engineering at University of Queensland. Given my theoretical leanings, I naturally gravitated towards the mathematically-oriented field of fluid dynamics. I’d spoken to a couple of folks working in the area, and finally decided to work with Tony Howes, not only because I found his work interesting, but also thought that his quick intelligence and easygoing manner would make for a good work environment.
I spent a few weeks – or was it months – trying to define a decent research problem, but got nowhere. Tony, sensing that it was time to nudge me towards a decision, suggested a couple of problems relating to a phenomenon that is easily demonstrated in a kitchen sink. If you’re game you may want to make your way to the nearest sink and try the following:
Turn the tap on slowly until water starts to flow out as a cylindrical jet. You will notice that the jet breaks up into near spherical droplets a short distance from the mouth of the tap.
This phenomenon is called jet breakup. Instead of describing it further, I’ll follow the advice that a picture is worth several words (see figure 1).
Figure 1: A water jet breaking up
If you are interested in knowing why fluid jets tend to break up into drops, please see the next paragraph; if not, feels free to skip the bracketed section as it is not essential to the story.
[Boring details: The basic cause of break up is surface tension – which is essentially a force that keeps a fluid from becoming a gas. Surface tension arises from the unbalanced “pull” that molecules in the interior of a fluid exert on molecules on the surface. The imbalance occurs because molecules at the surface “feel” a pull only from the interior of the fluid. In contrast, molecules in the interior of the fluid are subjected to the same force on all sides as they are surrounded by fluid. One of the effects of surface tension is that fluid bodies tend to minimise their surface area. The upshot of this for cylindrically shaped jets (such as those emerging from a tap) is that they tend to pinch off into a series of drops because the combined surface area of the drops is less than that of the cylinder.]
To get back to my story: I realised that I’d already burnt up a few months of a research grant so I agreed to work on one of the problems Tony suggested. Once I’d signed up to it, I hit the books and research journals getting up to speed with the problem. I learnt a lot. Among other things, I learnt that the problem of jet breakup was first studied by Lord Rayleigh in 1878! I also learnt that since the late 1960s, the phenomenon of jet break had enjoyed a bit of a renaissance due to applications such as inkjet printing. Tony had proposed a problem of interest to the metals industry – the production of shot from jets of molten metal. However, it seemed to me that this problem was at best a minor variation on a theme that had already been done to death.
Anyway, regardless of how I felt about it, I was being paid to do research, so I plugged away at it. In the process I developed a good sense for the physics behind the phenomenon, its applications and what had been done up until then. Although I wasn’t too fired up about it, I’d also started work on modelling the molten metal shot problem. It was progress of sorts, but of the dull, desultory kind.
Then one evening in October or November 1994, I had one of those magical Aha moments. …
I was washing up after dinner when I noticed a curious wave-like structure on the thin jet that emerged from the kitchen sink tap and fell onto a plate an inch or two below the tap (the dishes had piled up a while). The wave pattern was absolutely stationary and rather striking. Rather than attempt to describe it any further, I’ll just show you a a photograph of the phenomenon taken by my colleague Anh Vu.
Figure 2: Stationary waves on a water jet.
The phenomenon is one that countless folks have noticed, and even I’d seen it before but never paid it much attention. Having been immersed in the theory of fluid jets for so long, I realised at once that the pattern had the same underlying cause as jet breakup. I wondered if any one had published any papers on it. Google Scholar and decent search engines weren’t available so I rushed off to the library find out. A few hours of searching catalogues and references confirmed that I’d stumbled on to something that could see me through my degree and perhaps even give me a couple of papers.
The next day, I told Tony about it. He was just as excited about it as I was and was more than happy for me to switch topics. I worked feverishly on the problem and within a few months had a theory that related the wavelength of the waves to jet velocity and properties of the fluid. The work was not a major innovation, but it was novel enough to get me my degree and a couple of papers.
This episode taught me a few things about innovation and creativity, which I list below:
- Interesting opportunities lurk in unexpected places: A kitchen sink – who would have thought….
- …but it takes work and training to recognise opportunities for what they are: If I hadn’t the background in the physics of fluid jets, I wouldn’t have seen the stationary waves for what they were.
- A sense progress is important, even when things aren’t going well: Tony left me to my own devices initially, but then nudged me towards a productive direction when he saw I was going nowhere. This had the effect of giving me a sense of progress towards a goal (my degree), which kept my spirits up through a hard time.
- It is best to work on things that interest you, not those that interest others: I stuck to my primary interest (mathematical modelling) rather than do something that was not of much interest but may have been a better career choice.
The second story
Here’s another story, from a few years later when I was working as an applied mathematician within a polymer processing laboratory.
Some background first – polymer extrusion is an industrial process that is used to create plastic tubing from raw polymer pellets. It involves melting the raw material and driving the melt through a die with the required cross-sectional profile. A common problem encountered in this process is that at high flow rates, the melt emerging from the die has shark skin-like surface imperfections. This phenomenon is sometimes called the melt flow instability.
I was hired to work on a project to model the melt flow instability described above. I began, as researchers always do, by wading through a stack of research papers on the topic. Again this was a topic that had been over-researched in that many different groups had tried many different approaches. However none of them had answered the question definitively. I learnt a lot about modelling polymer flows (quite different from modelling flows of water-like fluids described in the earlier story) but didn’t make any progress on the problem.
Most of the other members in the research group were doing experimental projects, working in the lab doing stuff with real polymers, whilst I was engaged in modelling imaginary ones using simulations. Oddly enough, the folks engaged in the two strands of research did not meet much; I didn’t have much to do with them, and was happy working on my own little projects.
One day, after I’d been in the lab for a year or so, one of the experimentalists knocked on my door to have a chat regarding a problem he was having with a mathematical model he had developed. The reading and background work I had done up to that point enabled me to solve his problem rather quickly. Progress at last – but not in the way I’d imagined.
Encouraged by this, I started talking to others in the group and soon found that they had modelling problems that I could help with. I published a few papers through such collaborations and kept my academic score ticking along. More importantly, though, I got – for the first time – a taste of collaborative work, and I found that I really enjoyed it. One of the papers that we wrote rated a minor award, which would have helped my academic career had I stayed in the field. However, later that year I decided to switch careers and move to consulting. But that’s another story…
My stint in the polymer lab, very different from my solo research experience, taught me a few more things about creativity and innovation. These are:
- Collaboration between diversely skilled individuals enhances creativity. It is important to interact with others, particularly professionals from other disciplines. I’m grateful to my colleagues from the lab for drawing me out of my “comfort zone” of theoretical work.
- Being part of a larger effort does not preclude creativity and innovation – although I did not do any experiments, I was able to develop models that explained some of the phenomena that my colleagues found.
- Even modest contributions add value to the end product – great insights and epiphanies aren’t necessary – none of the modelling work that I did was particularly profound or new. It was all fairly routine stuff, done using existing methods and algorithms. Yet, my contributions to the research added a piece that was essential for completeness.
Reflections and wrap-up
The events related above occurred in a research environment, but the lessons I took away have, I believe, a much wider applicability. Further, although the two stories are quite different – and hold different lessons – there are a few common themes that run through them. These are:
- When doing creative work, one invariably ends up with results that one didn’t intend or expect to find.
- A shift in perspective may help in generating new ideas. Looking at things from someone else’s point of view might be just the spark you need.
- Things rarely go according to plan, but it is important to keep ones spirits up.
- Background is important; it is critical to learn/read as much as possible about the problem you’re attempting to solve.
The above conclusions hold a warning for those who night over-plan and control innovative or creative activities. In both cases I started out by defining what I intended to solve, but ended up solving something else. By the yardstick of a project plan, I failed. But by a more flexible measure, I did alright. By definition, the process of discovery is unpredictable and somewhat opportunistic – one has to be willing and able to redefine goals as one proceed, and at times even throw everything away and start from scratch.
Afterword
I wrote this piece in 2009, intending to post it on Eight to Late. Around that time Paul Culmsee and I were just starting out on our book, The Heretic’s Guide to Best Practices. I was pretty sure this piece would find a place in the book so I held off from blogging it. As it turned out, a modified version ended up in Chapter 4: Managing Innovation: The Demise of Command and Control.
Eight to Late 