In his 1597 Meditationes Sacrae, Sir Francis Bacon penned the phrase “ipsa scientia potestas est,” which means “knowledge itself is power.” Throughout history, the truth of that axiom has been proven time and again. As humankind’s knowledge base has grown, keeping up with new knowledge has proved extremely difficult. As a result, people have had to specialize in various fields. Even in specialized fields, however, new knowledge is increasing at such a rapid rate humans continue to lag behind. Fortunately, Digital Age technologies are emerging that can help organize and analyze data to help manage knowledge bases as well as help in the discovery of new knowledge. Among the most important of these technologies is cognitive computing — which I define as the combination of semantic reasoning (i.e., machine learning, natural language processing, and ontologies) and computation reasoning (i.e., advanced mathematical techniques). I predict cognitive computing will become so important for managing knowledge in most economic sectors future scholars may write “ipsa cognoscere computare potestas est” (cognitive computing is power).
Knowledge Management (KM)
If knowledge is power, then knowledge management (sometimes called information management) is power’s gatekeeper. What is knowledge management? Michael E.D. Koenig, a Professor and former and founding dean of the College of Information and Computer Science at Long Island University, likes the definition suggested by Thomas H. Davenport (@tdav), a Distinguished Professor at Babson College: “Knowledge Management is the process of capturing, distributing, and effectively using knowledge.” Koenig writes, “Probably no better or more succinct single-line definition has appeared since.”[1] Cognitive computing can help with every aspect of knowledge management.
Capturing knowledge. Knowledge is formally defined as facts, information, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject. In the Digital Age, knowledge is often trapped in raw data in both structured and unstructured forms. Today, almost every organization gathers data from numerous sources including sensors, the Internet, and smartphones. One reason I include ontologies in my definition of cognitive computing is because ontologies explain relationships between words and ideas (i.e., established knowledge). You might think “skills” are beyond cognitive systems’ ability to capture, but some skills are able to be captured. Skills often involve using rules of thumb or tricks of the trade that have been learned through years of experience. Such knowledge is often referred to as Tribal Knowledge. In a corporate setting, note Leonard F Bertain and George Sibbald, “Tribal Knowledge or Know-How is the collective wisdom of the organization. It is the sum of all the knowledge and capabilities of all the people.”[2] Cognitive computing can capture much of that knowledge, along with data, white papers, research, and other digitized forms of information.
Koenig notes, “A few years after the Davenport definition, the Gartner Group created another definition of KM, which has become the most frequently cited one: Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise’s information assets. These assets may include databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers.’ The one real lacuna of this definition is that it, too, is specifically limited to an organization’s own information and knowledge assets. KM as conceived now, and this expansion arrived early on, includes relevant information assets from wherever relevant.” Cognitive systems are capable of gathering any relevant data from any relevant source (whether it’s internal or external).
Distributing knowledge. Deloitte analysts John Ferraioli (@Ferraioli) and Rick Burke assert one of the challenges companies face is organizing their data so those requiring the data have access to it. “While data exists in every organization,” they explain, “it is often not fully organized or understood. Rather, it may be housed in disparate sources, data marts and warehouses, trapped in systems, and in various formats with limited context. The challenge, then, for some companies is they may not know what data they already have, where it lives, what may be useful, or how to turn it into meaningful insights that they can act upon.”[3] Not all data needs to be aggregated; but, when the need does exist, cognitive technologies can help. Ferraioli and Burke add, “While companies may have all the data they need to become a truly digital enterprise, some may not be able to access such data particularly well — much less understand how to turn the data into actionable insights.” Cognitive systems can help integrate and organize data so people requiring the data have access to a single source of truth as well as providing decision-makers with actionable insights.
Effectively using knowledge. For me, effectively using knowledge means making better decisions. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert that if you can improve a company’s decision making you can dramatically improve its bottom line.[4] They explain:
“The best way to understand any company’s operations is to view them as a series of decisions. People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”
Joe Hanson (@toejamson) observes, “The most challenging but potentially revolutionary result of cognitive services is decision making. Intelligent systems give businesses the ability to have their systems weigh evidence and analyze data, then make a decision based on that data that is unsullied by human emotional input. These services can consider complex sets of information and act on them, whether to do something as simple as making a product recommendation on an e-commerce site, to way more advanced actions, such as optimizing smart devices in an industrial setting.”[5] I agree with Hanson that cognitive services will have a positive impact on decision-making; however, his description implies such services always deliver black or white answers based on data analysis. The world is more nuanced than that. Jenna Hogue explains, “The types of problems [involved in cognitive computing] … tend to be much more complex and human-like than the average non-cognitive system. These problems tend to comprise multiple different variables included, shifting data elements, and an ambiguous nature.”[6] The fact that cognitive systems can deal with ambiguous situations is one of things that makes them so unique.
Summary
Koenig concludes, “Increasingly knowledge management is seen as ideally encompassing the whole bandwidth of information and knowledge likely to be useful to an organization, including knowledge external to the organization — knowledge emanating from vendors, suppliers, customers, etc., and knowledge originating in the scientific and scholarly community, the traditional domain of the library world. Looked at in this light, KM extends into environmental scanning and competitive intelligence.” Obviously, analyzing the scope of knowledge discussed by Koenig requires leveraging cognitive technologies — which means cognitive computing is power in the Digital Age.
Footnotes
[1] Michael E.D. Koenig, “What is KM? Knowledge Management Explained,” KMWorld, 15 January 2018.
[2] Leonard F Bertain, Ph.D. and George Sibbald, The Tribal Knowledge Paradigm (2012).
[3] John Ferraioli and Rick Burke, “Drowning in data, but starving for insights,” Deloitte Insights, 11 April 2018.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Joe Hanson, “Welcome to the Cognitive Era: The New Generation of Computing,” PubNub, 13 August 2018.
[6] Jenna Hogue, “Cognitive Computing: The Hype, the Reality,” Dataversity, 12 January 2017.
