Today’s knowledge management specialists must manage a rapidly expanding pool of knowledge. Without a well-designed organizational structure, gross inefficiencies will attend retrieving and amending that knowledge base, wasting time, money and opportunities. To meet this challenge, many businesses have taken a page from their old biology textbook. Their knowledge management strategies incorporate the development of a business taxonomy.

Taxonomy is the practice and science of classifying things or concepts, and the principles that underlie such classification. It most often describes biological taxonomy, classifying the many thousands of different extant and extinct organisms in a logical hierarchy according to their natural relationships, as first laid out by Carl Linnaeus in his groundbreaking Systema Naturae in 1735.

Biological Taxonomy

Systema Naturae stands, along with Newton’s Principia Mathematica and Darwin’s On the Origin of Species, as one of the most influential science texts of all time. The ideas expressed have grown and expanded dramatically in the subsequent centuries, and continue to do so today, but the underlying principles remain.

The essential operation in creating a taxonomy is dividing a very large number of items into a manageable number of smaller groups, each of which is defined by rigorously outlined criteria. Each of those groups is divided into subgroups, which are in turn divided into even smaller subgroups, and so forth.

Biological taxonomy starts with large groups, including animals and plants. Animals can be divided into vertebrates and invertebrates, plants into flowering and non-flowering plants. Vertebrate animals can be further divided into mammals, reptiles, birds, fish and amphibians. Mammals include numerous subgroups, including primates, and these, in turn, contain even more extensively defined subgroups, including our own Homo sapiens.

When Linnaeus began his classification system, there were only about 6,000 species of plants and fewer than 5,000 species of animals identified. Today there are close to two million identified species, all classified within the well-defined framework of modern biological taxonomy.

Useful Distinctions

The informational resources of modern-day knowledge managers in any business can benefit from organization in a similar hierarchical structure. The most important element in designing a useful taxonomy is understanding the relevant distinctions between the individual items being organized.

What information is similar enough that it should be grouped together? What distinctions are important enough that they should be used to discriminate between different content items, relegating some to one group and otherwise similar content to a different group?

For a biologist, dividing all animals into vertebrates and invertebrates is a logical and useful way to cleave the set of thousands of animals into two neat subgroups. For a business, the most logical distinction between the thousands of individual knowledge resources might be customer-facing versus internal content, for example.

Rigorous Definition

Another key to designing a successful business taxonomy is ensuring very careful and rigorous definitions of the various groups. If the definitions are clear and unambiguous, everything will fall naturally into its proper place. If the definitions lack rigor, and interpretation becomes subjective, different individuals might classify the same piece of content in a very different way.

If you were creating a subdivision of content that applied specifically to “small businesses,” it’s important to spell out exactly what defines a small business. You might use any of a variety of criteria, including total volume of sales, profits or structure of incorporation. Which criteria you decide upon depends on what distinctions are most useful to make. But wherever you decide that the lines are drawn, it’s important that the distinctions are clearly and objectively defined.

Trial and Error

In Linnaeus’s first edition of Systema Naturae, whales were classified as fishes. Today, of course, they share with us humans the mammalian class. Linnaeus himself corrected this in his 1758 revision. His first edition, in 1735, comprised only 11 pages. By the 12th edition, in 1768, it had grown to 2,400 pages.

Vigilance and attention to detail are essential to developing and maintaining an effective business taxonomy. Your first best guess as to what differentiation criteria and definitions will be most appropriate might not turn out to be as useful in practice as they did in theory. Successful knowledge management specialists start with a flexible framework encompassing the big picture, refining the process and filling in the details based on experience and careful observation.

Knowledge Management Solutions

Developing a business taxonomy for organizing informational content is an increasingly popular knowledge management solution for many of today’s successful companies. If you run a business and find that your knowledge base has become unmanageably extensive and complex, consider partnering with a knowledge management company with experience in taxonomy development and implementation to organize that chaotic content into an ordered hierarchy.

Do it right, and you can be an A student, baby. Even if you don’t know much about your science book.

We can know more than we can tell.

Do you remember learning how to ride a bicycle? It was tough. You may have earned a few bumps and bruises on the way to mastering that skill. Now think about how much easier it would have been if you had had a detailed instruction manual outlining all the necessary procedures to operate the vehicle. Probably not at all. Riding a bike is an example of tacit knowledge, the sort of knowledge that cannot be adequately conveyed in writing.

Explicit Knowledge and Tacit Knowledge

The first step that many knowledge management specialists take is to make a distinction between explicit knowledge and tacit knowledge, a term popularized by the above-quoted Michael Polyani in 1958 in his highly influential book, Personal Knowledge. Explicit knowledge can be written down. And if it can be, it should be.

Clearly written instructions that spell out exactly how to perform a task, company policies and procedures that affect decision-making and useful factual information that employees need in order to fulfill their duties should always be written down as lucidly as possible, and updated regularly so that everyone in the company is on the same page. Otherwise, different cohorts of employees will be working with different facts and conceptions of policies.

But not all knowledge can be so easily contained and conveyed. Managing tacit knowledge requires different approaches than managing explicit knowledge does.

Shadowing

Someone who has been doing the same job for years has probably amassed a significant pool of tacit knowledge of which he or she is not even consciously aware. If asked to create a training manual for a successor, that employee might be as lost as you would be writing instructions to your child on how to ride a bike. The best way for that successor to master the skills possessed by the veteran may be to work directly alongside him and observe firsthand how he does what he does so well.

Medical students read an awful lot of books on their way to becoming cardiac surgeons. But nobody in his right mind is going to be comfortable with a doctor poking around his chest, based solely on her impressive academic credentials. Knowing that your surgeon has read all the books and passed all the tests is simply not enough.

Surgeons master the finer points of their life-saving craft by working directly with experienced surgeons as they perform surgery on real patients. First, simply observing, then assisting, then operating under the close supervision of their more experienced colleagues, until they are finally prepared with both the tacit and explicit knowledge needed to operate on their own.

Joint Problem Solving

Similar to shadowing, joint problem solving puts the learned master and eager apprentice together to work toward a clearly-defined objective. The novice is not merely observing. He’s assisting with the process, providing input and suggestions.

This approach can benefit the more experienced participant as well as the newcomer. That newcomer might have some new ideas and a fresh perspective that the veteran can recognize and effectively harness. And that expert, though she might be able to solve the problem unassisted without much difficulty, might find it impossible to explain the process she uses to approach problems. Working together, each participant gets insight into the process, gleans that tacit knowledge that is so intangible and hard to grasp.

Organizational Knowledge

Knowledge management experts use the term “organizational knowledge” to refer to the entire collection of knowledge within an organization. It’s essential to the long-term prosperity of the organization that this organizational knowledge base is preserved, even if individual members depart. Some of this knowledge is explicit knowledge, easily codified and recorded.

Companies with explicit organizational knowledge that exists nowhere but inside a few select employees’ heads are asking for trouble. When those employees leave, that knowledge is gone, and it would have been a relatively simple matter to preserve it. Many companies offering knowledge management solutions can assist with the process of preserving this explicit organizational knowledge by getting it all down in writing.

Tacit knowledge is not only far more difficult to preserve, it’s often difficult to identify. Ensuring that the tacit knowledge possessed by individual employees is integrated as organizational knowledge is largely determined by the climate within that organization. When the organizational climate is highly competitive, individuals have little incentive to share tacit knowledge. They may hold on to it as much as possible to gain an edge over their colleagues, whom they see as competitors. And even if a spirit of unbridled competition does not reign, individual tacit knowledge may never be fully integrated if the goals and direction of the organization are not clearly communicated. Individuals may know certain things, but without a clear vision of the big picture, they may not recognize the value of their tacit knowledge.

If collaboration and teamwork are encouraged, and lines of communication are open, tacit knowledge can most smoothly be integrated into organizational knowledge. Once the right environment is established, it might just be time to remove the training wheels.

You are slmost certainly connected to bacon by less than six degrees.

Social networks are as old as human society, but our hunter-gatherer ancestors didn’t need social networking platforms or the now well-developed science of social network analysis. Each was a member of a small, closely knit tribe, and was directly acquainted with every other member of the tribe, while remaining unacquainted with anyone outside of it.

If those Paleolithic tribesmen had Cavebook, each would have an essentially identical group of friends. Society is far more complex today, which is why social network analysis has become such an important concept in modern knowledge management.

Nodes

Any individual in a social network is a node. Think of a complex web of lines connecting points on a geometric plane. Each point, out of which a number of lines are emanating, represents one individual within that social network.

In contrast to the isolated networks of yesteryear (or yesterepoch), some of these nodes will have many more connections than others. Some people, social butterflies and active networkers, will have many lines connecting them to many other individuals within the network. Others, the hermits and wallflowers among us, will have far fewer.

Cliques and Cliquishness

Some social groupings today share similar characteristics with those aforementioned ancient tribes. A typical high school student in a small town might have a lot of friends, but it’s likely that most of her friends have the same friends she has.

Social network analysis can quantify the cliquishness within a social group. In contrast to the students at Smallville High, a group of middle-aged professionals would likely have members with many connections to people who share few other connections with them.

Bridges

Consider an American city as a large social group, and opera fans and members of the local bowling league as two subgroups. Many of the opera fans know one another. Most of the bowlers know one another. Only a few of the bowlers are also opera fans. These individuals are bridges, connecting the one subgroup to the other.

Suppose one of the opera fans knows lots of other opera fans, but isn’t directly connected to many bridges within that subgroup. Another opera buff has fewer direct connections, but the ones she does have include several bridges.

While the former has more direct connections, the latter would have more secondary connections. In social network parlance, the first has a higher degree centrality, while the latter has higher betweenness centrality.

If you want to use social networking to your advantage, it’s good to have a lot of friends. The more the better. But it is perhaps even more advantageous to be friends with a diverse array of people who are themselves diverse in their own friend selection.

Types of Connections

Social network analysis studies the characteristics that tend to be associated with connections between individuals. It examines such factors as geographic or occupational propinquity, the tendency for individuals to form attachments to others who physically reside close to that individual, or who have a similar professional background.

One key result of such analysis with direct ramifications for knowledge managers and those seeking to use social networking to their advantage in business and academia is that some of the same technology we use for analyzing social networks is itself changing the way we are connected.

Before the widespread use of social networking platforms, the degree of occupational and geographic propinquity within most people’s social network was comparatively high. Only a rare individual would have an extensive group of friends or associates who lived thousands of miles away. Most people tended to have a lot of friends and associates with similar professional backgrounds because the workplace was one of the primary avenues whereby people met one another.

All that is changing, and changing quickly. Facebook users routinely maintain active relationships with people who live in other countries, whom they may never meet in person. People have many more opportunities to meet a diverse group of friends and associates than ever before. Professional networking using LinkedIn has allowed people to identify the bridges within their own social networks and use those bridges to connect with others whose indirect connections would have otherwise remained a mystery.

Heck, you’re probably connected to Kevin Bacon, and, according to social network analysis, in fewer than six degrees.

Ehrlich had too much knowledge to manage.

Is it possible to have too much of a good thing? The answer is a resounding "yes," whether the good thing is double-chocolate, triple-layer fudge cake, or data. Knowledge management professionals in business, government and academia are all coming to realize in this Information Age that there is such a thing as too much information, and it does not solely apply to your friend's vivid description of his recent blind date.

DNA+

An insightful 2013 article in Wired, "Biology's Big Problem: There's Too Much Data to Handle," discusses this phenomenon and its impact on scientists involved in genetics research. There is some irony in the cause of this informational crisis in biology, which has largely resulted from a dramatic drop in the costs—in money and time—of gene sequencing.

Techniques for determining the sequence of base pairs in the genome of an organism were developed over 30 years ago. But these approaches were very expensive and time-consuming. In 2006, the X Prize Foundation offered a $10 million prize to spur researchers to improve genome sequencing techniques. The prize was never awarded, but scientists innovated nevertheless.

So much so that the costs of gene sequencing have plummeted, falling even faster than the cost of the computer technology needed simply to store and transmit—let alone begin to analyze—the ever-growing mountains of data.

There's a certain degree of academic inertia that hinders efforts to change direction and focus in scientific research. Sequencing is much cheaper than it once was, but it's still not free, and research funding is limited.

When funding agencies like the National Institutes of Health are seeing ever-growing bang for their buck in terms of sheer volume of data generated, individuals making granting decisions have strong incentives to keep throwing money at data generation, ignoring scientists who need funding to analyze that data with techniques that have not dropped in cost so dramatically.

The Spy Who Loved Me Too Much

While the problem of too much information in scientific research has gotten some attention and discussion, far more prominent recently are revelations of the enormous volume of data collected by the National Security Agency (NSA).

While this obviously raises many important questions about civil liberties and government invasiveness, it also brings with it more pragmatic problems. It's not just that what the NSA is doing is objectionable for its violation of personal privacy. It's also preventing it from doing its job.

A Wall Street Journal article quotes William Binney, a retired NSA computer scientist who helped develop some of the Internet snooping techniques used at the Agency.

"What they are doing is making themselves dysfunctional by taking all this data."

Some of the documents released by Edward Snowden included internal memos in which the Agency itself acknowledges these concerns. One of the leaked reports describing the NSA's efforts to track foreign cell phones noted that this massive data collection endeavor was "outpacing our ability to ingest, process and store" the collected information.

Less Is More (Money)

Governments and academia are adjusting to the new paradigm in information technology. Some businesses have been quicker, recognizing that big numbers aren't very impressive if they don't include that bottom line.

Companies that have successfully addressed the problem of too much information include Boeing, Nike, Macy's and Land Rover, as detailed in a 2013 Slashdot article.

The informational overhaul these companies initiated was time-consuming and in most cases involved bringing in outside consultants to objectively evaluate the inefficiencies inherent in their knowledge management approaches. But in the end these efforts paid off handsomely.

One key approach was to centralize their informational processing, reducing both redundancies and discrepancies. Another was to develop and implement software to better organize the information they had.

And of course they were able to streamline their analysis of business data by recognizing that much of it was not relevant and was simply making the appraisal far more complicated. When the pertinent data was separated from this informational dead weight, analysis became much more efficient.

If you must search for needles in haystacks, try to make the haystack as small as possible.

Efficient Knowledge Management

A&E's popular documentary program Hoarders delighted and horrified millions of viewers with its vivid depictions of people with a psychological disorder compelling them to hold on to just about everything they ever owned, whether they needed it or not, until their lives were made unmanageable by the sheer volume of useless garbage they could not bring themselves to part with.

But while we view these hoarders with a blend of contempt and pity, governments, scientific institutions and multi-billion dollar companies often find themselves in the same predicament, just with data instead of a living room piled to the ceiling with cardboard boxes full of junk.

If you are drowning in data, consider partnering with knowledge management experts who have experience successfully trimming down haystacks. You might find a lot of great needles in there.