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Practical Knowledge Management (or What Comes after the Rows and Columns?)

Published
  • October 10 2002, 1:00am EDT

It is easy to espouse the value of information and knowledge. It is quite difficult to deliver that perceived value efficiently and accurately. Information and knowledge are abstract concepts. Knowledge is perceived to live only between people's ears. Information is data with meaning, but the meaning is difficult to quantify, and few organizations have managed to place the value of their information on a balance sheet. In spite of the abstractions, 21st century organizations that "manage" their intellectual capital will succeed where others will fail. Intellectual capital management will differentiate companies as other supporting technologies become commoditized (ERP, data warehouse, SCM and CRM packages).

While knowledge, like information, is obviously an asset to an organization, the track record of formal knowledge management areas is suspect. Therefore, a need has risen for "practical" knowledge management (KM) - new processes and mind-sets that avoid the shortcomings of the old information management processes but strive to capture context, learning and tacit wisdom that KM gurus have touted.

This article starts a sub-series within the concepts and solutions that must be considered to move "beyond the data warehouse." Several topical areas have leaked out of the illusive realm of knowledge management and into more mainstream thought. Before all the KM types raise your hackles, please acknowledge that four years ago, KM was the "next big thing." However, very few organizations have developed holistic KM approaches. The concepts are too abstract to add pragmatic value in an economy that requires one year ROI on technology. However, KM has provided IT with a wonderful set of ideas and creative approaches that offer solid ROI that can be grasped sooner rather than later.

Taken as a whole, the three topics that will be reviewed in this article are forming a baseline approach to extending the types of structures we associate with data warehouse and Web technology into a kind of "practical" application of knowledge management concepts. We will cover unstructured information, collaboration and extreme information maturity. Subsequent articles will go into detail on the application and relevance of each topic.

Unstructured Data and Information

For many data warehouse shops, the struggle to maintain accurate accounting of the rows and columns within their databases is enough work. However, the amount of intelligence organizations want to extract from unstructured data sources is increasing, proving there is no rest for the weary.

From a business standpoint, blending content is part of the job. To the technologists, it is new territory. Information as an asset is a simple metaphor. Treating information like an asset is not. Frankly, content is content, and the distinctions between areas that handle structured content, e.g., a data warehouse, and unstructured content, e.g., the content management of knowledge management area, is blurring rapidly. Information is information to the business, regardless of format and content. Format and context are attributes that IT needs to understand so they can move toward effective enterprise intelligence management via unstructured content.

We (KI) sniffed around to try and find out how many organizations were blending structured and unstructured data. The answer depends on whom you talk to. The business users will tally at 100 percent. Simply, their job is to take the BI output and merge it with other information; document review, external information and tacit knowledge. The IT folks will report in at around 15 to 35 percent reporting the need to blend the two. Our conclusion is that the problem is not defined and, again, IT and business should stop acting with variable viewpoints.

There are many examples of successful blending of unstructured information into business processes. The next article will address how to recognize these opportunities and present some design pointers.

Collaboration

The Internet/portal boom of the last few years presented the term "collaboration" to the IT industry ad nauseam. Communities of interest were to flourish, with all the cross-functional sharing and benefits thereof. Most organizations have done well with portals. Efforts to use them to alter business processes and increase the value of information and its distribution costs are falling short.

However, the fact remains that there is tremendous potential in combining data warehouse information, unstructured content and some workflow design to create a new set of formal processes to use organizational intelligence and knowledge assets. In addition, the Web topology offers excellent opportunity to measure the use of information.

Given the current issues with DW costs of support, data quality and cultural barriers, a collaborative environment offers companies an opportunity to extend the value of the organization's information infrastructure through the interactions of people and information.

Collaboration means developing a formal vision and supporting processes how an organization would be more efficient and experience a greater return on information via the application of collaborative technology and processes. This means processes must be re-examined, and organizational issues must be confronted and measured. The article on collaboration will focus on developing a formal collaborative business intelligence environment.

Extreme Information Maturity

Previous columns have delved into information management maturity. Practical KM will exert a pull on organizations to move from historical and operational latency to predictive, agent-based information usage. (Is there such a thing as negative latency?) The meta data and management requirements for this type of data is are extreme. Unstructured data, XML, clickstream analysis etc all contribute to this KM-inspired use of information.

Questions that get answered by analyzing information at this point are: What do you want to happen? and based on certain conditions: What is the decision? Another question that is near and dear to KM-types, and becoming more relevant in an aging work force, is: How did we do that? Extreme maturity will mean managing information and metrics that record how work was done. A final example is a question such as: What should we do next? This is an extension of recording how work is done. Mining successful workflows will allow super intelligent agents to make recommendations for decisions.

Extreme maturity means de-emphasizing the collection of event information (most of rows and columns, and storing and managing information about how work is done will increase in importance.

Obviously, this last topic is far reaching, and the final article in the sub series will focus on how to apply some of the ideas from extreme maturity, as well as potions your meta data to support extreme maturity.

Summary

Like it or not, new business problems will continue to push the creativity and limits of information management specialists. Data warehouse, business intelligence, content managers and knowledge managers will have to apply new approaches that leverage practical aspects of the technologies arising from the KM movement.

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