The law of requisite variety and its implications for enterprise IT

Introduction

There are two  facets to the operation of IT systems and processes in organisations:  governance, the standards and regulations associated with a system or process; and execution, which relates to steering the actual work of the system or process in specific situations.

An example might help clarify the difference:

The purpose of project management is to keep projects on track. There are two aspects to this: one pertaining to the project management office (PMO) which is responsible for standards and regulations associated with managing projects in general, and the other relating to the day-to-day work of steering a particular project.  The two sometimes work at cross-purposes. For example, successful project managers know that much of their work is about navigate their projects through the potentially treacherous terrain of their organisations, an activity that sometimes necessitates working around, or even breaking, rules set by the PMO.

Governance and steering share a common etymological root: the word kybernetes, which means steersman in Greek.  It also happens to be the root word of Cybernetics  which is the science of regulation or control.   In this post,  I  apply a key principle of cybernetics to a couple of areas of enterprise IT.

Cybernetic systems

An oft quoted example of a cybernetic system is a thermostat, a device that regulates temperature based on inputs from the environment.  Most cybernetic systems are way more complicated than a thermostat. Indeed, some argue that the Earth is a huge cybernetic system. A smaller scale example is a system consisting of a car + driver wherein a driver responds to changes in the environment thereby controlling the motion of the car.

Cybernetic systems vary widely not just in size, but also in complexity. A thermostat is concerned only the ambient temperature whereas the driver in a car has to worry about a lot more (e.g. the weather, traffic, the condition of the road, kids squabbling in the back-seat etc.).   In general, the more complex the system and its processes, the larger the number of variables that are associated with it. Put another way, complex systems must be able to deal with a greater variety of disturbances than simple systems.

The law of requisite variety

It turns out there is a fundamental principle – the law of requisite variety– that governs the capacity of a system to respond to changes in its environment. The law is a quantitative statement about the different types of responses that a system needs to have in order to deal with the range of  disturbances it might experience.

According to this paper, the law of requisite variety asserts that:

The larger the variety of actions available to a control system, the larger the variety of perturbations it is able to compensate.

Mathematically:

V(E) > V(D) – V(R) – K

Where V represents variety, E represents the essential variable(s) to be controlled, D represents the disturbance, R the regulation and K the passive capacity of the system to absorb shocks. The terms are explained in brief below:

V(E) represents the set of  desired outcomes for the controlled environmental variable:  desired temperature range in the case of the thermostat,  successful outcomes (i.e. projects delivered on time and within budget) in the case of a project management office.

V(D) represents the variety of disturbances the system can be subjected to (the ways in which the temperature can change, the external and internal forces on a project)

V(R) represents the various ways in which a disturbance can be regulated (the regulator in a thermostat, the project tracking and corrective mechanisms prescribed by the PMO)

K represents the buffering capacity of the system – i.e. stored capacity to deal with unexpected disturbances.

I won’t say any more about the law of requisite variety as it would take me to far afield; the interested and technically minded reader is referred to the link above or this paper for more.

Implications for enterprise IT

In plain English, the law of requisite variety states that only “variety can absorb variety.”  As stated by Anthony Hodgson in an essay in this book, the law of requisite variety:

…leads to the somewhat counterintuitive observation that the regulator must have a sufficiently large variety of actions in order to ensure a sufficiently small variety of outcomes in the essential variables E. This principle has important implications for practical situations: since the variety of perturbations a system can potentially be confronted with is unlimited, we should always try maximize its internal variety (or diversity), so as to be optimally prepared for any foreseeable or unforeseeable contingency.

This is entirely consistent with our intuitive expectation that the best way to deal with the unexpected is to have a range of tools and approaches at ones disposal.

In the remainder of this piece, I’ll focus on the implications of the law for an issue that is high on the list of many corporate IT departments: the standardization of  IT systems and/or processes.

The main rationale behind standardizing an IT  process is to handle all possible demands (or use cases) via a small number of predefined responses.   When put this way, the connection to the law of requisite variety is clear: a request made upon a function such as a service desk or project management office (PMO) is a disturbance and the way they regulate or respond to it determines the outcome.

Requisite variety and the service desk

A service desk is a good example of a system that can be standardized. Although users may initially complain about having to log a ticket instead of calling Nathan directly, in time they get used to it, and may even start to see the benefits…particularly when Nathan goes on vacation.

The law of requisite variety tells us successful standardization requires that all possible demands made on the system be known and regulated by the  V(R)  term in the equation above. In case of a service desk this is dealt with by a hierarchy of support levels. 1^st level support deals with routine calls (incidents and service requests in ITIL terminology) such as system access and simple troubleshooting. Calls that cannot be handled by this tier are escalated to the 2^nd and 3^rd levels as needed.  The assumption here is that, between them, the three support tiers should be able to handle majority of calls.

Slack  (the K term) relates to unexploited capacity.  Although needed in order to deal with unexpected surges in demand, slack is expensive to carry when one doesn’t need it.  Given this, it makes sense to incorporate such scenarios into the repertoire of the standard system responses (i.e the V(R) term) whenever possible.  One way to do this is to anticipate surges in demand and hire temporary staff to handle them. Another way  is to deal with infrequent scenarios outside the system- i.e. deem them out of scope for the service desk.

Service desk standardization is thus relatively straightforward to achieve provided:

• The kinds of calls that come in are largely predictable.
• The work can be routinized.
• All non-routine work – such as an application enhancement request or a demand for a new system-  is  dealt with outside the system via (say) a change management process.

All this will be quite unsurprising and obvious to folks working in corporate IT. Now  let’s see what happens when we apply the law to a more complex system.

Requisite variety and the PMO

Many corporate IT leaders see the establishment of a PMO as a way to control costs and increase efficiency of project planning and execution.   PMOs attempt to do this by putting in place governance mechanisms. The underlying cause-effect assumption is that if appropriate rules and regulations are put in place, project execution will necessarily improve.  Although this sounds reasonable, it often does not work in practice: according to this article, a significant fraction of PMOs fail to deliver on the promise of improved project performance. Consider the following points quoted directly from the article:

• “50% of project management offices close within 3 years (Association for Project Mgmt)”
• “Since 2008, the correlated PMO implementation failure rate is over 50% (Gartner Project Manager 2014)”
• “Only a third of all projects were successfully completed on time and on budget over the past year (Standish Group’s CHAOS report)”
• “68% of stakeholders perceive their PMOs to be bureaucratic     (2013 Gartner PPM Summit)”
• “Only 40% of projects met schedule, budget and quality goals (IBM Change Management Survey of 1500 execs)”

The article goes on to point out that the main reason for the statistics above is that there is a gap between what a PMO does and what the business expects it to do. For example, according to the Gartner review quoted in the article over 60% of the stakeholders surveyed believe their PMOs are overly bureaucratic.  I can’t vouch for the veracity of the numbers here as I cannot find the original paper. Nevertheless, anecdotal evidence (via various articles and informal conversations) suggests that a significant number of PMOs fail.

There is a curious contradiction between the case of the service desk and that of the PMO. In the former, process and methodology seem to work whereas in the latter they don’t.

Why?

The answer, as you might suspect, has to do with variety.  Projects and service requests are very different beasts. Among other things, they differ in:

• Duration: A project typically goes over many months whereas a service request has a lifetime of days,
• Technical complexity: A project involves many (initially ill-defined) technical tasks that have to be coordinated and whose outputs have to be integrated.  A service request typically consists one (or a small number) of well-defined tasks.
• Social complexity: A project involves many stakeholder groups, with diverse interests and opinions. A service request typically involves considerably fewer stakeholders, with limited conflicts of opinions/interests.

It is not hard to see that these differences increase variety in projects compared to service requests. The reason that standardization (usually) works for service desks  but (often) fails for PMOs is that the PMOs are subjected a greater variety of disturbances than service desks.

The key point is that the increased variety in the case of the PMO precludes standardisation.  As the law of requisite variety tells us, there are two ways to deal with variety:  regulate it  or adapt to it. Most PMOs take the regulation route, leading to over-regulation and outcomes that are less than satisfactory. This is exactly what is reflected in the complaint about PMOs being overly bureaucratic. The solution simple and obvious solution is for PMOs to be more flexible– specifically, they must be able to adapt to the ever changing demands made upon them by their organisations’ projects.  In terms of the law of requisite variety, PMOs need to have the capacity to change the system response, V(R), on the fly. In practice this means recognising the uniqueness of requests by avoiding reflex, cookie cutter responses that characterise bureaucratic PMOs.

Wrapping up

The law of requisite variety is a general principle that applies to any regulated system.  In this post I applied the law to two areas of enterprise IT – service management and project governance – and  discussed why standardization works well  for the former but less satisfactorily for the latter. Indeed, in view of the considerable differences in the duration and complexity of service requests and projects, it is unreasonable to expect that standardization will work well for both.  The key takeaway from this piece is therefore a simple one: those who design IT functions should pay attention to the variety that the functions will have to cope with, and bear in mind that standardization works well only if variety is known and limited.

Evolution, obsolescence and enterprise architecture

Introduction

Enterprise architects are seldom (never?) given a blank canvas on which they can draw as they please. They invariably have to begin with an installed base of systems over which they have no control.  As I wrote in a piece on the legacy of legacy systems:

An often unstated (but implicit) requirement [on new systems] is that [they] must maintain continuity between the past and present. This is true even for systems that claim to represent a clean break from the past; one never has the luxury of a completely blank slate, there are always arbitrary constraints placed by legacy systems.

Indeed the system landscape of any large organization is a palimpsest, always retaining traces of what came before.  Those who actually maintain systems  – usually not architects – are painfully aware of this simple truth.

The IT landscape of an organization is therefore a snapshot, a picture that begins to age the instant is taken. Practicing enterprise architects will say they know this “of course”, and pay due homage to it in their words…but often not their actions.  The conflicts and contradictions between legacy and their aspirational architectures are hard to deal with and hence easier to ignore. In this post, I draw a parallel between this central conundrum of enterprise architecture and the process of biological evolution.

A Batesonian perspective on evolution

I’ve recently been re-reading Mind and Nature: A Necessary Unity, a book that Gregory Bateson wrote towards the end of his life of eclectic scholarship. Tucked away in the appendix of the book is an essay lamenting the fragmentation of knowledge and the lack of transdisciplinary thinking within universities.  Central to the essay is the notion of obsolescence. Bateson argued that much of what was taught in universities lagged behind the practical skills and mindsets that were needed to tackle the problems of that time.  Most people would agree that this is as true today as it was in Bateson’s time, perhaps even more so.

Bateson had a very specific idea of obsolescence in mind. He suggested that the educational system is lopsided because it invariably lags behind what is needed in the “real world”. Specifically, there is a lag between the typical university curriculum and the attitudes, dispositions, knowledge and skills needed to the problems of an ever-changing world. This lag is what Bateson referred to as obsolescence. Indeed, if the external world did not change there would be no lag and hence no obsolescence. As he noted:

I therefore propose to analyze the lopsided process called “obsolescence” which we might more precisely call “one-sided progress.” Clearly for obsolescence to occur there must be, in other parts of the system, other changes compared with which the obsolete is somehow lagging or left behind. In a static system, there would be no obsolescence…

This notion of obsolescence-as-lag has a direct connection with the contrasting process of developmental and evolutionary biology. The process of development of an embryo is inherently conservative – it develops according predetermined rules and is relatively robust to external stimuli. On the other hand, after birth, individuals are continually subject to a wide range of external factors (e.g. climate, stress etc.) that are unpredictable. If exposed to such factors over an extended period, they may change their characteristics in response to them (e.g. the tanning effect of sunlight, adaptability etc).  However, these characteristics are not inheritable.  They are passed on (if at all) by a much slower process of natural selection.  As a consequence, there is a significant lag between external stimuli and the inheritability of the associated characteristics.

As Bateson puts it:

Survival depends upon two contrasting phenomena or processes, two ways of achieving adaptive action. Evolution must always, Janus-like, face in two directions: inward towards the developmental regularities and physiology of the living creature and outward towards the vagaries and demands of the environment. These two necessary components of life contrast in interesting ways: the inner development-the embryology or “epigenesis”-is conservative and demands that every new thing shall conform or be compatible with the regularities of the status quo ante. If we think of a natural selection of new features of anatomy or physiology-then it is clear that one side of this selection process will favor those new items which do not upset the old apple cart. This is minimal necessary conservatism.

In contrast, the outside world is perpetually changing and becoming ready to receive creatures which have undergone change, almost insisting upon change. No animal or plant can ever be “readymade.” The internal recipe insists upon compatibility but is never sufficient for the development and life of the organism. Always the creature itself must achieve change of its own body. It must acquire certain somatic characteristics by use, by disuse, by habit, by hardship, and by nurture. These “acquired characteristics” must, however, never be passed on to the offspring. They must not be directly incorporated into the DNA. In organisational terms, the injunction – e.g. to make babies with strong shoulders who will work better in coal mines- must be transmitted through channels, and the channel in this case is via natural external selection of those offspring who happen (thanks to the random shuffling of genes and random creation of mutations) to have a greater propensity for developing stronger shoulders under the stress of working in coal mine.

The upshot of the above is that the genetic code of any species is inherently obsolete because it is, in at least a few ways, maladapted to its environment.  This is a good thing. Sustainable and lasting change to the genome of a population should occur only through the trial-and-error process of natural selection over many generations. It is only through such a gradual process that one can be sure that that a) the adaptation is necessary and b) that it occurs with minimal disruption to the existing order.

…and so to enterprise architecture

In essence, the aim of enterprise architecture is to come up with a strategy and plan to move from an old system landscape to a new one. Typically, architectures are proposed based on current technology trends and extrapolations thereof. Frameworks such as The Open Group Architecture Framework (TOGAF) present a range of options for migrating from legacy architecture.

Here’s an excerpt from Chapter 13 of the TOGAF Guide:

[The objective is to] create an overall Implementation and Migration Strategy that will guide the implementation of the Target Architecture, and structure any Transition Architectures. The first activity is to determine an overall strategic approach to implementing the solutions and/or exploiting opportunities. There are three basic approaches as follows:

• Greenfield: A completely new implementation.
• Revolutionary: A radical change (i.e., switches on, switch off).
• Evolutionary: A strategy of convergence, such as parallel running or a phased approach to introduce new capabilities.

What can we say about these options in light of the discussion of the previous sections?

Firstly, from the discussion of the introduction, it is clear that Greenfield situations can be discounted on grounds rarity alone.  So let’s look at the second option – revolutionary change – and ask if it is viable in light of the discussion of the previous section.

In the case of a particular organization, the gap between an old architecture and technology trends/extrapolations is analogous to the lag between inherited characteristics and external forces. The former resist change; the latter insist on it.  The discussion of the previous section tells us that the former cannot be wished away, they are a natural consequence of “technology genes” embedded in the organization. Because this is so, changes are best introduced in a gradual way that permits adaptation through the slow and painful process of trial and error. This is why the revolutionary approach usually fails.

It follows from the above that the only viable approach to enterprise architecture is an evolutionary one. This process is necessarily gradual. Architects may wish for green fields and revolutions, but the reality is that lasting and sustainable change in an organisation’s technology landscape can only be achieved incrementally, akin to the way in which an aspiring marathon runner’s physiology adapts to the extreme demands of the sport.

The other, perhaps more subtle point made by this analogy is that a particular organization is but one member of a “species” which, in the present context, is a population of organisations that have a certain technology landscape. Clearly, a new style of architecture will be deemed a success only if it is adopted successfully by a significant number of organisations within this population. Equally clear is that this eventuality is improbable because new architectural paradigms are akin to random mutations. Most of these are rightly rejected by organizations, but only after exacting a high price. This explains why most technology fads tend to fade away.

Some consequences

The analogy between the evolution of biological systems and organizational technology landscapes has some interesting implications for enterprise architects. Here are a few that are worth highlighting:

1. Enterprise architects are caught between a rock and a hard place: to demonstrate value they have to change things rapidly, but rapid changes are more likely to fail than succeed.
2. The best chance of success lies in an evolutionary approach that accepts trial and error as a natural part of the process. The trick lies in selling that to management…and there are ways to do that.
3. A corollary of (2) is that old and new elements of the landscape will necessarily have to coexist, often for periods much longer than one might expect. One must therefore design for coexistence. Above all, the focus here should be on the interfaces for these are the critical elements that enable the old and the new to “talk” to each other.
4. Enterprise architects should be skeptical of cutting edge technologies. It almost always better to bet on proven technologies because they have the benefit of the experience of others.
5. One of the consequences of an evolutionary process of trial and error is that benefits (or downsides) are often not evident upfront. One must therefore always keep an eye out for these unexpected features.

Finally, it is worth pointing out that an interesting feature of all the above points is that they are consistent with the principles of emergent design.

Wrapping up

In this article I’ve attempted to highlight a connection between the evolution of organizational technology landscapes and the process of biological evolution. At the heart of both lie a fundamental tension between inherent conservatism (the tendency to preserve the status quo change) and the imperative to evolve in order to adapt to changes imposed by the environment. There is no question that maintaining the status quo is never an option. The question is how to evolve in order to ensure the best chance of success. Evolution tells us that the best approach is a gradual one, via a process of trial, error and learning.

The proof of concept – a business fable

The Head of Data Management was a troubled man.  The phrase “Big Data” had cropped up more than a few times in a lunch conversation with Jack, a senior manager from Marketing.

Jack had pointedly asked what the data management team was doing about “Big Data”…or whether they were still stuck in a data time warp. This comment had put the Head of Data Management on the defensive. He’d mumbled something about a proof of concept with vendors specializing in Big Data solutions being on the drawing board.

He spent the afternoon dialing various vendors in his contact list, setting up appointments to talk about what they could offer.  His words were music to the vendors’ ears; he had no trouble getting them to bite.

The meetings went well. He was soon able to get a couple of vendors to agree to doing proofs of concept, which amounted to setting up trial versions of their software on the organisation’s servers, thus giving IT staff an opportunity to test-drive and compare the vendors’ offerings.

The software was duly installed and the concept duly proven.

…but the Head of Data Management was still a troubled man.

He had sought an appointment with Jack to inform him about the successful proof of concept.  Jack had listened to his spiel, but instead of being impressed had asked a simple question that had the Head of Data Management stumped.

“What has your proof of concept proved?” Jack asked.

“What do…do you mean?” stammered the Head of Data Management.

“I don’t think I can put it any clearer than I already have. What have you proved by doing this so-called proof of concept?”

“Umm… we have proved that the technology works,” came the uncertain reply.

“Surely we know that the technology works,” said Jack, a tad exasperated.

“Ah, but we don’t know that it works for us,” shot back the Head of Data Management.

“Look, I’m just a marketing guy, I know very little about IT,” said Jack, “but I do know a thing or two about product development and marketing. I can say with some confidence that the technology– whatever it is – does what it is supposed to do. You don’t need to run a proof of concept to prove that it works. I’m sure the vendors would have done that before they put their product on the market. So, my question remains: what has your proof of concept proved?”

“Well we’ve proved that it does work for us…:

“Proved it works for what??” asked Jack, exasperation mounting. “Let me put it as clearly as I can – what business problem have you solved that you could not address earlier.”

“Well we’ve taken some of the data from our largest databases – the sales database, from your area of work, and loaded it into the Big Data infrastructure.”

“…and then what?”

“Well that’s about it…for now.”

“So, all you have is another database. You haven’t actually done anything that cannot be done on our existing databases. You haven’t tackled a business problem using your new toy,” said Jack.

“Yes, but this is just the start. We’ll now start doing analytics and all that other stuff we were talking about.”

“I’m sorry to say, this is a classic example of putting the cart before the horse,” said Jack, shaking his head in disbelief.

“How so?” challenged the Head of Data Management.

“Should be obvious but may be it isn’t, so let me spell it out. You’ve jumped to a solution – the technology you’ve installed – without taking the time to define the business problems that the technology should address.”

“…but…but you talked about Big Data when we had lunch the other day.”

“Indeed I did. And what I expected was the start of a dialogue between your people and mine about the kinds of problem we would like to address. We know our problems well, you guys know the technology; but the problems should always drive the solution. What you’ve done now is akin to a solution in search of a problem, a cart before a horse”

“I see,” said the Head of Data Management slowly.

And Jack could only hope that he really did see.

Disclaimer:

This is a work of fiction. It could never happen in real life 😉