Guest Column | July 16, 2020

From Data To Knowledge: Making The Transition From Data Mining To Knowledge Discovery

By Bill Pollock, Strategies for Growth

Sales Advice Data Analytics

The most important asset an organization owns is the cumulative knowledge that it has acquired, developed, and utilized to produce or deliver its products and services to the market. Much of this knowledge is documented in corporate manuals, policies, procedures, or other corporate literature, and even more knowledge is implicit within the systems and processes that the organization uses. However, other, nonetheless valuable, knowledge may just float around or is stashed away, rarely documented, or expressed in a manner that can be effectively used within the organization.

Some knowledge may also be in the form of bits of information stored in a database, while other knowledge is captured and contained in new systems, tools, and technologies that have been put into use. However, most of the time, this knowledge is passed by, unnoticed, undocumented, and otherwise overlooked. Some of it may ultimately become part of the expertise of individuals, and some may forever remain unturned. In either case, the organization at large is denied both access and use of this knowledge if no specific actions are taken to document it, distribute it, or bring it to the surface in a useful form.

Both organizations and their personnel are constantly in a learning mode where additional knowledge is being accumulated on a daily – if not real-time – basis. However, knowledge can only be effectively built on a strong foundation of data and information – and these key components of knowledge must be inherently accurate, clean, well-defined, and easily accessible to ultimately be useful.

The war on the competitive edge moving beyond 2020 (i.e., particularly considering the global impact of the COVID-19 pandemic) will ultimately be won on the knowledge front. Knowledge is the key to any business or economic activity. It is the main ingredient in Research and Development (R&D), and the major component for productivity and quality products and services, as well as in sales and marketing applications. As a result, knowledge management (i.e., acquisition, storage and retrieval, and distribution) has already taken its place alongside R&D, marketing, and finance in terms of its relative importance in corporate missions.

To meet the evolving challenges of the future, companies will need to mobilize all their intellectual resources. The traditional approach to knowledge acquisition, primarily using human experts, will no longer suffice on its own. One important source for knowledge is found in databases that store operational, marketing, and related information that represents an organization’s own, or others’, experiences.

However, three (3) major problems reflect the bottleneck in the current knowledge management process: namely, expense, inefficiency, and incompleteness. The joint interests of business and research will be the catalysts required to stimulate increased activity in the development of the tools and techniques necessary for successful database mining and knowledge discovery.

The principal purposes of data mining and knowledge discovery are to increase the awareness within organizations of their existing and potential knowledge assets. The knowledge discovery approach is designed to help organizations to:

  • Identify areas of operations with a high value of knowledge.
  • Identify sources and stores of knowledge.
  • Develop methods and procedures to express and preserve knowledge.
  • Extract knowledge from existing or planned databases.
  • Develop knowledge-based systems where knowledge is explicitly expressed and used in operations and decision making.
  • Develop methods for the distribution of knowledge.
  • Utilize this knowledge to improve business operations and marketing programs.

A rich source for corporate knowledge are databases that companies normally maintain. Sales histories, customer files, quality control records, activity reports, product failures, recall/repair data, and purchase orders are typical expressions of hidden patterns that govern the behavior of individuals or systems.

In many cases, these databases contain the answers to questions such as:

  • What are the customer buying patterns, and what events influence these patterns?
  • How do customers use products and services, in what combinations, and for what purposes?
  • What products and services best meet user requirements, and under what circumstances?
  • What products fail? In what patterns? Over what length of time?
  • How are these failures typically manifested? What levels of support are required to resolve them?

However, many of these databases are never really used to their full potential, if at all, before they are purged, deleted, or otherwise stored away in warehouses or repositories, never again to see the light of day.

Still, these data stores represent key areas where services organizations can routinely gather, update, and manage crucial information about their internal operations or external environments. This information can – and should – be converted into useful knowledge that can ultimately be utilized for the organization’s, and its customers’, overall benefit.

In today’s highly competitive and increasingly demanding economy, businesses of all types and sizes are being forced to search for a cost-effective means for identifying new markets and establishing programs for penetrating and cultivating them. In most cases, the business development manager finds him/herself “scrambling” to implement the most current, state-of-the-art market development tools to support the overall effort. But sometimes, the most valuable tools may already be resident within the organization, and this is particularly true with respect to the services sector.

Overall, the principal purposes of database mining are to benefit from the already collected internal data and information, and using it to make the organization:

  • More focused on its ability to attract and retain customers;
  • More able to satisfy the needs and requirements of its existing and targeted customer bases;
  • More effective in marketing and selling its services to the marketplace (i.e., selling the right services, to the right segments; using the right messages, etc.);
  • More capable of competing on a direct basis against its primary competitors; and
  • Better able to utilize its existing databases as primary tools to improve service operations, increase customer satisfaction and loyalty, refine sales and services forecasting, and develop the most effective marketing and business development programs.