How do your clients deal with information complexity?

There is a consensus that the world has entered a knowledge era where information is power and rapid learning a necessary condition for success. The concept itself, though, is nothing new: the English philosopher and statesman Francis Bacon is credited with coining the phrase “knowledge is power” in 1597 in his Meditationes Sacrae. And in business, knowledge is now widely regarded as a powerful source of competitive advantage.

But information tends to be complex and, as anybody who has worked in different types of libraries and information services knows, clients from different communities handle information, both simple and complex, in different ways. Someone from the business community, for example, will handle information differently to someone from the academic or medical community.

To be part of a community and to truly belong, you have to be able to understand and process information given to you by other members of that community. Such communities have been described as populations of data-processing agents. The way in which the community’s data-processing agents handle information is one of the community’s key cultural attributes, and different communities have evolved different strategies for handling the complexity of the information they deal with.

Reduction vs absorption

A community’s members have two fundamental choices for dealing with complexity (in other words, for adapting to the complexities of their environment). They can agree either to reduce the complexity or to absorb it (that is, come to terms with it in some way).

But while broad agreement between members on the right approach is vital (it is, after all, a key cultural attribute of the community), perfect agreement is unlikely. And just to complicate matters, most people belong to at least two communities: their workplace and their society, and those communities may well have evolved different strategies for dealing with complexity.

From an informational point of view, to make it easier to disseminate and share data with others, items of information first have to be codified, then compressed into categories, before finally being formalised.

If the community’s strategy is to reduce complexity, an ideal system of categorisation is both complete and mutually exclusive. Ideally, there are no exceptions: every item of information falls into one and only one category.

But if the community’s strategy is to absorb complexity, then it becomes accepted and expected that any item of information can simultaneously fall into two or more categories.

In reality, and irrespective of strategy, there will be a number of exceptions: some items of information simply will not fall into any existing category. And there will always be a need to weed out out-of-date or irrelevant information.

Retaining the purely informational point of view, codification and categorisation is followed by abstraction – in other words, a reduction in the number of categories to which data must be assigned so that items of information (often referred to as “phenomena” or “structures”) can be accessible and comprehended, and more generally, so the community members can share a world model in which both events and information can be understood.

We live and work in a complex world. It is very large, it contains ambiguity and contradictions, and it is always changing. As a consequence, world models have to be complex too, with multiple and possibly contradictory explanations of phenomena. Abstraction involves reducing the number of categories to which data needs to be assigned for a phenomenon to be apprehended.

The strategic choice of whether to reduce or absorb complexity implies handling abstraction in different ways. Reducing complexity requires a highly structured world model. Crucially, alternative explanations are regarded as competing with each other. The community’s members search for the best explanat ion and the best abstraction, normally on a logical basis.

By contrast, absorbing complexity requires the community to accept co-existing contradictory explanations and so simultaneous alternative abstractions. This may be second nature to information professionals, but not necessarily to their clients.

The complexity space

It is possible to plot on a two-dimensional graph how information is organised and to distinguish between different communities’ organisational strategies. The graph would have codification as one axis and abstraction as another. At the origin (the theoretical zero point on each axis), information is uncodified and unabstracted: every item of information is unique, as is every event. The zone nearest to the origin corresponds to complexity absorption (non-exclusive categories and joint membership on one axis; alternative abstractions on the other), further from the origin reflects complexity reduction, few exceptions and an established (single) structure. The ability to feel comfortable with contraction, and indeed a preference for alternative abstractions, is said to be a characteristic of Eastern societies.

Codification and abstraction simplifies the dissemination of information and the sharing of information between members of the community, which, in principle, speeds it up. But another factor must also be taken into account. There are at least two types of data-processing agent in any community. At the very least, some agents will be leaders and others followers. The extent to which members of the community are unequal and the extent to which each is seen as entitled to possess information introduces a humanistic component to complexity: the number and variety (not forgetting ambiguity and rate of change) of relationships between members of the community. This is known as relational complexity.

Relational complexity in a community is largely determined by the nature of its institutions. Perhaps surprisingly (at least, if you belong to a community that practises informational complexity reduction), there is no connection between informational complexity, determined by the agreed reduction vs absorption strategic choice, and the way that a community’s institutions deal with relational complexity.

Four types of institution

A bureaucracy, for example, is high on codification and abstraction; by severely curtailing the number of other data-processing agents who need to be dealt with, it is also low on relational complexity. By contrast, market-type institutions have high relational complexity (little restriction of those with whom members may interact) while adopting the same strategy of complexity reduction.

There are four distinct institutional types – markets, bureaucracies, fiefs and clans – associated with different types of informational complexity, necessitating different informational strategies. The four types distinguish between open information that is available to everyone and secret information that is accessible only by insiders.

Markets refer to institutions where information is highly codified and disseminated. Relationships are impersonal and everyone looks after their own interests. Market types are open. There are no barriers to entry and exit. Examples include the financial and commodities markets. Market types reduce informational complexity.

Bureaucracies refer to the use of secretive, codified information to achieve co-ordination; the approach is sometimes called hierarchical co-ordination. Bureaucracies are impersonal and secretive by nature. Efficient government agencies resemble bureaucracies, as they possess a strong capacity to structure, refine and make sense of information. Other examples include the military and large corporations. Bureaucracies reduce informational complexity.

Fiefs, unlike market types, are about personal power and charisma. Inf ormation is secret and uncodified. Knowledge resides with a few, making relationships hierarchical and personal. Fiefs are personal and secretive. An R &D department where one prominent scientist leads large projects, aided by assistants, could be a fief. Other examples include cartels and top management teams. Fiefs absorb informational complexity.

Clans are produced by open, uncodified and non-disseminated information. Clan types are personal and open. Examples include family businesses, the top tier of some bureaucracies, and some entrepreneurial startups. Clans absorb informational complexity.

While an institutional typology may offer some insights into how communities deal with information complexity, this should be supplemented by discussion within the profession. Information complexity provides several key messages for information professionals.

First, information professionals have to understand and react to the needs of their clients, even if those clients do not fully appreciate the nature of their needs and what action is appropriate.

Second, in their roles as information professionals, librarians and information and knowledge managers need to be able to diagnose the strategy that their clients (and client communities) use to handle complex information: are they reductionists or absorptionists?

Third, information professionals may find they need to modify the way they organise their knowledge and their information services (especially their cataloguing and classification) to suit their clients’ reductionist or absorption strategies, and also the way that they present information to their clients.

The nature of the client community’s institutions give some insights into how they handle complex information.

For example, if fiefs are the most common sort of organisation for your clients, their strategy is probably going to be to absorb complexity. As a result, such clients will want concrete and uncodified answers, maybe three or four specific examples, possibly in the form of narrative, and they will be treated as private or limited circulation.

By contrast, if the most common sort of organisation is market type, the strategy is probably to reduce complexity, and so the clients will want abstract and codified answers, perhaps a model or a policy, probably containing jargon, which will be treated as open information.

What exactly is complexity theory?

Complexity theory and chaos theory are the two main ways to study and analyse the structure and behaviour of complex systems such as information.

A complex system is a self-adapting one where the whole is greater than the sum of the parts. The weather is an example of a purely physical complex system: the rain in a particular location, say, is influenced by the wind some way to the west of it, and will in turn affect the sunshine to the south of it. A complex system may likewise be a biological system or a human system. The key point, though, is that complex systems adapt and evolve, and some can even learn.

Complexity theory is used to understand how organisations such as businesses, not-for-profit organisations and political institutions adapt to their environments. Complexity theory treats organisations as adaptive systems that acknowledge and respond to the complexity of their environments. The ability to learn, self-organise and evolve should help businesses to adapt successfully and so survive.

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