Knowledge Management is everywhere and nowhere. Though virtually every firm is concerned about bolstering, retaining and leveraging its intellectual property and general know-how, Knowledge Management, as a discipline, is not necessarily invoked. Skeptics might ask if Knowledge Management is just another buzzword used to sell tack-on software.
We don’t think so. Knowledge Management is a complex undertaking that investigates human cognition and human behavior. It cannot be reduced to a knowledge base, or a single tool, a single process, a one-time event or even confined to a single organizational department.
We believe that data, if enriched in the right way, can provide the context for knowledge transmission; Data can become an essential part of a knowledge management ecosystem that results in bolstered and expanded competitive advantage.
Perspectives on Knowledge Management
In a sense, knowledge management has been around since at least the time of cave paintings. Humans have tried to impart their own knowledge, thoughts, dreams and experiences to others for self-expression, but also to share ideas that could help the group. For example, knowing when and where to find buffalo or caribou could save the tribe. As a species, we’ve long had the intuition to write things down, or to tell stories, or to, in some way or form, communicate in an attempt to impart wisdom, learn from experience, and, ultimately, to build civilizations. Now, we live in the age of what Peter Drucker called ‘knowledge work’ with its shift away from factory labor to less concrete knowledge work.
With the rise of the knowledge worker and the rise of digital, internet-driven knowledge transfer and the concurrent retirement of the baby boomer generation, there is a renewed attention paid to how organizations develop, distribute and retain knowledge. As with many problems, some organizations will try to slap on a technological solution, and hope that it is the magic bullet that will solve their problems. Others will spend month after month, year after year, without making much tangible progress. We think that an approach that ties the insights of Knowledge Management to the business-oriented management of data can result in powerful knowledge capabilities.
Essential Concepts in Knowledge Management
Knowledge Management makes useful distinctions between different types of knowledge:
Explicit knowledge is the kind of factual knowledge that is ready to write down in a reference manual. The information is easy to share, is often factual, does not change frequently and does not require significant insider knowledge or experience to appreciate. The set of abbreviations for the 50 states is a good example.
Tacit knowledge can be thought of knowledge that is gained through individual experience, and cannot be reduced to words. A good example is riding a bike: Though we can describe, at great length, the physics and the qualities and even some of the feelings of riding a bike, we struggle to communicate how to calibrate one’s weight and balance in order to pull off a successful bike ride. Thus, training wheels were created to mitigate the risk. Once you have learned how to ride a bike, though, it stays with you.
The concept of tacit knowledge was developed by the scientist and philosopher, Michael Polanyi, who argued that the scientific method could not be adequately taught solely through books or other forms of codified knowledge. Instead, scientists had to be trained by a mentor in order to develop intuition and creative problem solving – in other words, the essential tacit knowledge of science had to be passed on via apprenticeship.
Tribal knowledge describes ideas or ways of doing things that are known to insiders in a group, but are often opaque to outsiders. Tribal knowledge is sometimes codified, though it often goes un-codified or unspoken or is communicated only non-verbally. Tribal knowledge could include the best way to communicate with your boss. Does he or she prefer email or text, formal or casual speech, more indirect or more direct forms of communication? While some bosses might codify their preferences, many won’t. In some cases, the proper approach might depend on the weather, or the stock market, or the latest sales report or some set of variables that are complex and contingent. This is the kind of question that is best to ask an insider – someone who has been around the office for a while.
Data and Knowledge Management
Each type of knowledge requires a different way to share that type of knowledge. For example, explicit knowledge can be shared via documentation and job aids, tribal knowledge can be shared via discussion or intra-organization communication, while tacit knowledge can be shared via experimentation and mentor feedback. Note that these suggested ways of sharing different types of information are examples and are not intended to be an exhaustive or comprehensive list of the ways in which these types of information can or should be shared.
Another factor to consider is the diversity of learning styles – the ways that individuals best absorb information. Some people tend to learn best from written procedures and observation (Information Learners), others prefer collaboration and mentoring (People Learners), while still others prefer hands-on practice and experimentation (Action Learners).
Across all types of knowledge and all learning styles, we believe that knowledge anchored to data can disseminate information throughout an organization. In the modern business, virtually everyone touches data or is affected by data. Data flows through the business, providing insight, circulating knowledge, broadcasting problems, and giving feedback. Data connects people, processes and technology. Data is often the medium of interaction and in the data-centric firm, is always an indicator of these interactions – it tells the story of what happened when, and the what the results were.
The data-centric firm comprehensively enriches data with metadata – the data about data can be divided into three primary categories: physical, logical or conceptual forms. Physical and logical metadata describe technical and structural details that are generally extracted from machines either automatically or by a technician. Conceptual metadata is drawn from the minds of data workers. It illustrates how data functions within an organization. It can reveal how a specific data element is tied to a business process, who is in charge of maintaining that data, where it can be found, and which KPIs it is associated to, among other things.
Conceptual metadata contains a lot of tribal knowledge. It is information about what people in the firm know or think about the data within their work context. Acquiring and publishing this information in the form of metadata stored in a central repository and accessible via the Business Glossary allows captured knowledge to circulate throughout the firm. If done right, the Business Glossary enables workers to immediately find reliable information that has direct impact on their work. In other words, the data provides the context for the spread of tribal knowledge. This makes the knowledge more immediate and more easily searchable. If implemented correctly, a Business Glossary can be updated continuously and painlessly with Wikipedia-like controls on accuracy.
So if someone wants to know who in the firm can make an adjustment to ‘Billing Address’, with a click, she can get the contact information for the Data Steward in charge of maintaining that specific Business Data Element. Maybe a data analyst wants to know why the data values for ‘Marital Status’ appears as 1 or 0 in a given dataset. With one click, he can find that ‘0’ stands for unmarried and ‘1’ for married, or he can request more information from the appropriate Data Custodian.
While stored metadata effectively disseminates explicit and vital tribal knowledge, the process of acquiring this metadata can, in itself, become a rich opportunity for the dissemination of tacit knowledge. Our Controlled Chaos process is a socially collaboratively mechanism that is designed to evoke tacit knowledge from and for all of those involved. It hinges upon gathering a group of people who know or care about data, and prodding them to argue, out loud, over the proper definition/scope/functionality etc. of a specific data element until they reach consensus. The public interaction among experts is tacit knowledge in action. It confers, to all of those involved, ways to think about data, contexts for understanding how data functions within the business, and it demonstrates who knows what about what, among other things.
While this exercise puts tacit knowledge on display for all witnesses involved, the expert locator database can add an asynchronous tool with which to search for and identify knowledge within an organization.
Consider the case of airline Captain Chesley Sullenberger who said, of his famous emergency landing, “One way of looking at this might be that for 42 years, I’ve been making small, regular deposits in this bank of experience, education and training. And on January 15 the balance was sufficient so that I could make a very large withdrawal.” Captain Sullenberger’s quote suggests that the wisdom, or tacit knowledge, that has accrued over the course of a career is what saved 155 lives. While few corporate situations are quite as extreme as that of landing an airliner in the Hudson River, surely every organization wants to ‘bottle’ and disperse the kind of knowledge that accrues over a long career in its best people.
The challenge is immense. For his part, Sullenberger has given various talks and has become the public face for improving aviation safety on a national level. His dramatic story made him a public figure, but what about experts in fields that improvise solutions to unique organizational problems on a daily basis? The smart firm develops an expert locator to not only house the resumes of personnel, but to actively seek out their experience and their wisdom so that the organization does not have to reinvent the wheel every time there is an anomaly. This exists to some degree in the health care industry with its layers of care leading from generalists to specialists. You can imagine the impact of leveraging engineering or other expertise within a huge multi-national firm. While not every individual can know everything, they should be able to leverage the knowledge within the firm.
In a large organization, it is difficult to track who knows what. While job titles and resumes are collected by the H.R. department, this information may only be of limited accuracy, currency and accessibility. The smart firm maintains an accessible database to include all of an employees’ professional skills and a detailed account of the projects that they have worked on. This enables a reliable network of knowledge transfer to arise. Without this kind of system in place, you are leaving knowledge management to the vagaries of chance – if you are lucky, your engineers talk to each other and create a reliable professional network, but what if they leave?
The Business Glossary 2.0 integrates the functionality of the expert locator as it relates to data. One of the sets of conceptual metadata is the contact information for data stewards and custodians who can answer questions pertaining to particular data elements. Furthermore, when business elements are mapped to business processes, any staff member can quickly discover how and where data is created, changed and consumed within the business. This provides invaluable insight into the data knowledge of the firm, and since data touches virtually every aspect of the modern business, it sheds invaluable insight into the entire business.
Age of the Data Knowledge Worker?
At Data Clairvoyance, we advocate a business oriented approach to data management that views data as the vessel of knowledge. Data connects people, process and technology. Even without conceptual metadata, the data that flows through an organization inevitably contains the residue from every person, process or tool that touches it. If your organization bothers to capture conceptual metadata in a collaborative way, it will have captured not only explicit, but also rich tribal and tacit knowledge. If your organization curates and publishes that metadata in a smart Business Glossary, data becomes the conduit for effective Knowledge Management. Improve your data, improve your knowledge, improve your organization.
* Special thanks to Adam Vigiano for his conversations with Data Clairvoyance Group that contributed to this piece. You can see his profile here.