Archive for the ‘Organizations’ Category
The “value add” tax – a riff on corporate communication
A mainstay of team building workshops is the old “what can we do better” exercise. Over the years I’ve noticed that “improving communication” is an item that comes up again and again in these events. This is frustrating for managers. For example, during a team-building debrief some years ago, an exasperated executive remarked, “Oh don’t pay any attention to that [better communication], it keeps coming up no matter what we do.”
The executive had a point. The organisation had invested much effort in establishing new channels of communication – social media, online, face-to-face forums etc. The uptake, however, was disappointing: turnout at the face-to-face meetings was consistently low as was use of other channels.
As far as management was concerned, they had done their job by establishing communication channels and making them available to all. What more could they be expected to do? The matter was dismissed with a collective shrug of suit-clad shoulders…until the next team building event, when the issue was highlighted by employees yet again.
After much hand-wringing, the organisation embarked on another “better communication cycle.” Much effort was expended…again, with the same disappointing results.
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Anecdotal evidence via conversations with friends and collaborators suggests that variants of this story play out in many organisations. This makes the issue well worth exploring. I won’t be so presumptuous as to offer answers; I’m well aware that folks much better qualified than I have spent years attempting to do so. Instead I raise a point which, though often overlooked, might well have something to do with the lack of genuine communication in organisations.
Communication experts have long agreed that face-to-face dialogue is the most effective mode of communication. Backing for this comes from the interactional or pragmatic view, which is based on the premise that communication is more about building relationships than conveying information. Among other things, face-to-face communication enables the communicating parties to observe and interpret non-verbal signals such as facial expression and gestures and, as we all know, these often “say” much more than what’s being said.
A few months ago I started paying closer attention to non-verbal cues. This can be hard to do because people are good at disguising their feelings. Involuntary expressions indicative of people’s real thoughts can be fleeting. A flicker of worry, fear or anger is quickly covered by a mask of indifference.
In meetings, difficult topics tend to be couched in platitudinous language. Platitudes are empty words that sound great but can be interpreted in many different ways. Reconciling those differences often leads to pointless arguments that are emotionally draining. Perhaps this is why people prefer to take refuge in indifference.
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A while ago I was sitting in a meeting where the phrase “value add activity” (sic) cropped up once, then again…and then many times over. Soon it was apparent that everyone in the room had a very different conception of what constituted a “value add activity.” Some argued that project management is a value add activity, others disagreed vehemently arguing that project management is a bureaucratic exercise and that real value lies in creating something. Round and round the arguments went but there was no agreement on what constituted a “value add activity.” The discussion generated a lot of heat but shed no light whatsoever on the term.
A problem with communication in the corporate world is that it is loaded with such platitudes. To make sense of these, people have to pay what I call a “value add” tax – the effort in reaching a consensus on what the platitudinous terms mean. This can be emotionally extortionate because platitudes often touch upon issues that affect people’s sense of well-being.
Indifference is easier because we can then pretend to understand and agree with each other when we would rather not understand, let alone agree, at all.
Setting up an internal data analytics practice – some thoughts from a wayfarer
Introduction
This year has been hugely exciting so far: I’ve been exploring and playing with various techniques that fall under the general categories of data mining and text analytics. What’s been particularly satisfying is that I’ve been fortunate to find meaningful applications for these techniques within my organization.
Although I have a fair way to travel yet, I’ve learnt that common wisdom about data analytics – especially the stuff that comes from software vendors and high-end consultancies – can be misleading, even plain wrong. Hence this post in which I dispatch some myths and share a few pointers on establishing data analytics capabilities within an organization.
Busting a few myths
Let’s get right to it by taking a critical look at a few myths about setting up an internal data analytics practice.
- Requires high-end technology and a big budget: this myth is easy to bust because I can speak from recent experience. No, you do not need cutting-edge technology or an oversized budget. You can get started for with an outlay of 0$ – yes, that’s right, for free! All you need to is the open-source statistical package R (check out this section of my article on text mining for more on installing and using R) and the willingness to roll-up your sleeves and learn (more about this later). No worries if you prefer to stick with familiar tools – you can even begin with Excel.
- Needs specialist skills: another myth floating around is that you need Phd level knowledge in statistics or applied mathematics to do practical work in analytics. Sorry, but that’s plain wrong. You do need a PhD to do research in the analytics and develop your own algorithms, but not if you want to apply algorithms written by others.Yes, you will need to develop an understanding of the algorithms you plan to use, a feel for how they work and the ability to tell whether the results make sense. There are many good resources that can help you develop these skills – see, for example, the outstanding books by James, Witten, Hastie and Tibshirani and Kuhn and Johnson.
- Must have sponsorship from the top: this one is possibly a little more controversial than the previous two. It could be argued that it is impossible to gain buy in for a new capability without sponsorship from top management. However, in my experience, it is OK to start small by finding potential internal “customers” for analytics services through informal conversations with folks in different functions.I started by having informal conversations with managers in two different areas: IT infrastructure and sales / marketing. I picked these two areas because I knew that they had several gigabytes of under-exploited data – a good bit of it unstructured – and a lot of open questions that could potentially be answered (at least partially) via methods of data and text analytics. It turned out I was right. I’m currently doing a number of proofs of concept and small projects in both these areas. So you don’t need sponsorship from the top as long as you can get buy in from people who have problems they believe you can solve. If you deliver, they may even advocate your cause to their managers.
A caveat is in order at this point: my organization is not the same as yours, so you may well need to follow a different path from mine. Nevertheless, I do believe that it is always possible to find a way to start without needing permission or incurring official wrath. In that spirit, I now offer some suggestions to help kick-start your efforts
Getting started
As the truism goes, the hardest part of any new effort is getting started. The first thing to keep in mind is to start small. This is true even if you have official sponsorship and a king-sized budget. It is very tempting to spend a lot of time garnering management support for investing in high-end technology. Don’t do it! Do the following instead:
- Develop an understanding of the problems faced by people you plan to approach: The best way to do this is to talk to analysts or frontline managers. In my case, I was fortunate to have access to some very savvy analysts in IT service management and marketing who gave me a slew of interesting ideas to pursue. A word of advice: it is best not to talk to senior managers until you have a few concrete results that you can quantify in terms of dollar values.
- Invest time and effort in understanding analytics algorithms and gaining practical experience with them: As mentioned earlier, I started with R – and I believe it is the best choice. Not just because it is free but also because there are a host of packages available to tackle just about any analytics problem you might encounter. There are some excellent free resources available to get you started with R (check out this listing on the r-statistics blog, for example).It is important that you start cutting code as you learn. This will help you build a repertoire of techniques and approaches as you progress. If you get stuck when coding, chances are you will find a solution on the wonderful stackoverflow site.
- Evangelise, evangelise, evangelise: You are, in effect, trying to sell an idea to people within your organization. You therefore have to identify people who might be able to help you and then convince them that your idea has merit. The best way to do the latter is to have concrete examples of problems that you have tackled. This is a chicken-and-egg situation in that you can’t have any examples until you gain support. I got support by approaching people I know well. I found that most – no, all – of them were happy to provide me with interesting ideas and access to their data.
- Begin with small (but real) problems: It is important to start with the “low-hanging fruit” – the problems that would take the least effort to solve. However, it is equally important to address real problems, i.e. those that matter to someone.
- Leverage your organisation’s existing data infrastructure: From what I’ve written thus far, I may have given you the impression that the tools of data analytics stand separate from your existing data infrastructure. Nothing could be further from the truth. In reality, I often do the initial work (basic preprocessing and exploratory analysis) using my organisation’s relational database infrastructure. Relational databases have sophisticated analytical extensions to SQL as well as efficient bulk data cleansing and transport facilities. Using these make good sense, particularly if your R installation is on a desktop or laptop computer as it is in my case. Moreover, many enterprise database vendors now offer add-on options that integrate R with their products. This gives you the best of both worlds – relational and analytical capabilities on an enterprise-class platform.
- Build relationships with the data management team: Remember the work you are doing falls under the ambit of the group that is officially responsible for managing data in your organization. It is therefore important that you keep them informed of what you’re doing. Sooner or later your paths will cross, and you want to be sure that there are no nasty surprises (for either side!) at that point. Moreover, if you build connections with them early, you may even find that the data management team supports your efforts.
Having waxed verbose, I should mention that my effort is work in progress and I do not know where it will lead. Nevertheless, I offer these suggestions as a wayfarer who is considerably further down the road from where he started.
Parting thoughts
You may have noticed that I’ve refrained from using the overused and over-hyped term “Big Data” in this piece. This is deliberate. Indeed, the techniques I have been using have nothing to do with the size of the datasets. To be honest, I’ve applied them to datasets ranging from a few thousand to a few hundred thousand records, both of which qualify as Very Small Data in today’s world.
Your vendor will be only too happy to sell you Big Data infrastructure that will set you back a good many dollars. However, the chances are good that you do not need it right now. You’ll be much better off going back to them after you hit the limits of your current data processing infrastructure. Moreover, you’ll also be better informed about your needs then.
You may also be wondering why I haven’t said much about the composition of the analytics team (barring the point about not needing PhD statisticians) and how it should be organized. The reason I haven’t done so is that I believe the right composition and organizational structure will emerge from the initial projects done and feedback received from internal customers. The resulting structure will be better suited to the organization than one that is imposed upfront. Long time readers of this blog might recognize this as a tenet of emergent design.
Finally, I should reiterate that my efforts are still very much in progress and I know not where they will lead. However, even if they go nowhere, I would have learnt something about my organization and picked up a useful, practical skill. And that is good enough for me.
From inactivism to interactivism – managerial attitudes to planning
Introduction
Managers display a range of attitudes towards planning for the future. In an essay entitled Systems, Messes and Interactive Planning, the management guru/philosopher Russell Ackoff classified attitudes to organizational planning into four distinct types which I describe in detail below. I suspect you may recognise examples of each of these in your organisation…indeed, you might even see shades of yourself 🙂
Inactivism
This attitude, as its name suggests, is characterized by a lack of meaningful action. Inactivism is often displayed by managers in organisations that favour the status quo. These organisations are happy with the way things are, and therefore see no need to change. However, lack of meaningful action does not mean lack of action. On the contrary, it often takes a great deal of effort to fend off change and keep things the way they are. As Ackoff states:
Inactive organizations require a great deal of activity to keep changes from being made. They accomplish nothing in a variety of ways. First, they require that all important decisions be made “at the top.” The route to the top is deliberately designed like an obstacle course. This keeps most recommendations for change from ever getting there. Those that do are likely to have been delayed enough to make them irrelevant when they reach their destination. Those proposals that reach the top are likely to be farther delayed, often by being sent back down or out for modification or evaluation. The organization thus behaves like a sponge and is about as active…
The inactive manager spends a lot of time and effort in ensuring that things remain the way they are. Hence they act only when a stituation forces them to. Ackoff puts it in his inimitable way by stating that, “Inactivist managers tend to want what they get rather than get what they want.”
Reactivism
Reactivist managers are a step worse than inactivists because they believe that disaster is already upon them. This is the type of manager who hankers after the “golden days of yore when things were much better than they are today.” As a result of their deep unease of where they are now, they may try to undo the status quo. As Ackoff points out, unlike inactivists, reactivists do not ride the tide but try to swim against it.
Typically reactivist managers are wary of technology and new concepts. Moreover, they tend to give more importance to seniority and experience rather than proven competence. They also tend to be fans of simplistic solutions to complex problems…like “solving” the problem of a behind-schedule software project by throwing more people at it.
Preactivism
Preactivists are the opposite of reactivists in that they believe the future is going to be better than the past. Consequently, their efforts are geared towards understanding what the future will look like and how they can prepare for it. Typically, preactive managers are concerned with facts, figures and forecasts; they are firm believers in scientific planning methods that they have learnt in management schools. As such, one might say that this is the most common species of manager in present day organisations. Those who are not natural preactivists will fly the preactivist flag when they’re asked for their opinions by their managers because it’s the expected answer.
A key characteristic of preactivist managers is that they tend to revel in creating plans rather than implementing them. As Ackoff puts it, “Preactivists see planning as a sequence of discrete steps which terminate with acceptance or rejection of their plans. What happens to their plans is the responsibility of others.”
Interactivism
Interactivists planners are not satisfied with the present, but unlike reactivists or preactivists, they do not hanker for the past, nor do they believe the future is automatically going to be better. They do want to make things better than they were or currently are, but they are continually adjusting their plans for the future by learning from and responding to events. In short, they believe they can shape the future by their actions.
Experimentation is the hallmark of interactivists. They are willing to try different approaches and learn from them. Although they believe in learning by experience, they do not want to wait for experiences to happen; they would rather induce them by (often small-scale) experimentation.
Ackoff labels interactivists as idealisers – people who pursue ideals they know cannot be achieved, but can be approximated or even reformulated in the light of new knowledge. As he puts it:
They treat ideals as relative absolutes: ultimate objectives whose formulation depends on our current knowledge and understanding of ourselves and our environment. Therefore, they require continuous reformulation in light of what we learn from approaching them.
To use a now fashionable term, interactivists are intrapreneurs.
Discussion
Although Ackoff shows a clear bias towards interactivists in his article, he does mention that specific situations may call for other types of planners. As he puts it:
Despite my obvious bias in my characterization of these four postures, there are circumstances in which each is most appropriate. Put simply, if the internal and external dynamics of a system (the tide) are taking one where one wants to go and are doing so quickly enough, inactivism is appropriate. If the direction of change is right but the movement is too slow, preactivism is appropriate. If the change is taking one where one does not want to go and one prefers to stay where one is or was, reactivism is appropriate. However, if one is not willing to settle for the past, the present or the future that appears likely now, interactivism is appropriate.
The key point he makes is that inactivists and preactivists treat planning as a ritual because they see the future as something they cannot change. They can only plan for it (and hope for the best). Interactivists, on the other hand, look for opportunities to influence events and thus potentially change the future. Although both preactivists and interactivists are forward-looking, interactivists tend to be long-term thinkers as compared to preactivists who are more concerned about the short to medium term future.
Conclusion
Ackoff’s classification of planners in organisations is interesting because it highlights the kind of future-focused attitude that managers ought to take. The sad fact, though, is that a significant number of managers are myopic preactivists, focused on this year’s performance targets rather than what their organisations might look like five or even ten years down the line. This is not the fault of individuals, though. The blame for the undue prevalence of myopic preactivism can be laid squarely on the deep-seated management dogma that rewards short-termism.
Eight to Late 