Knowledge Management is one of the most misunderstood term. It is often confused with several other concpets, like Social networking, e-learning, neural networks, etc. In this short three page paper, I try to classify all these terms based on a two-dimensional matrix - process and knowledge to arrive at a classification which helps distinguish knowledge management with other terms.

2. Document Management

3. Content Management

4. Data warehouse

5. Data Mining and Analytics

6. Expert Systems and Decision Support Systems

7. Neural Networks and Genetic Algorithm

8. Blog Engines and even blogs

9. Social Networking

10. Collaboration PlatformsPlease note that - I am not saying that the content in the above platforms does not comprise of knowledge. I am only saying that knowledge management is much more complex than the complexities involved in the above platforms. <br />Explicit Knowledge (Q1)Knowledge you know you have (is well documented & stored)Knowledge Gap (Q2)Knowledge you know you do not have (is well documented & stored)Tacit Knowledge (Q3)Knowledge you do not know you have (is in the minds of your people)Unknown Knowledge (Q4)Knowledge you do not know you do not have <br />Before trying to classify what is and what is not, knowledge management, let us classify knowledge into four dimensions (like is done in case of Johari Window)<br />The focus of knowledge management should be in the management of Tacit knowledge (Q3) in the above diagram and to some extent the knowledge related to Q2. The explicit knowledge of quadrant 1 in the above diagram is what is best captured in document management or content management tools.<br />Tacit knowledge is unstructured and hidden in the minds of the people (employees) and hence required a semi-structured or unstructured process to capture it. Capture of tacit knowledge is like writing a story or peom for which making a process can be difficult if not impossible.<br />In the below model, I suggest a classification which comprises of two axis; knowledge and process and try to map the different tools into the four quadrants resulting from these axis to further bring out the concept of knowledge management and distinguish it from other terms.<br />Data Warehouse and Data Mining/Analytics do not explicit store any knowledge. As their name suggest, they store data. Nevertheless, they are powerful tools in the hands of a knowledge worker. Powerful insights about the business can be derived by the knowledge worker. These insights become an input to the knowledge management system once fed by the knowledge worker. In other words, the data per se is not of any interest for fulfilling the knowledge management objectives, but the inferences drawn from the data using human intelligence which makes the realization of the objectives of KM fructify. The data screen shot from these tools can be inputted by the knowledge worker in the KM system along with his inference.<br />Similarly, if you consider Expert Systems – you cannot create expert systems till you capture the human expert’s knowledge (explicit knowledge) into a rule-engine. The contributed over here is structured and it is also stored in the expert system in a structured manner (A series of nested if-then rules). On the other hand, in Genetic Algorithms and Neural Networks, the implicit knowledge is captured. The problem with these systems is that the store of knowledge is also unstructured. If at a later date one wants to extract the knowledge from these systems, it is not possible as both these systems work on the regression and convergence principles and do not explicitly store the knowledge in a comprehensible form.<br />STRUCTUREDUNSTRUCTUREDSTRUCTUREDUNSTRUCTUREDE-LearningPROCESS KNOWLEDGE WebsiteWikisBlogsSocial NetworkingCollaboration/Co-AuthoringDocument ManagementContent ManagementKnowledge ManagementInsightsData WarehouseData MiningAnalyticsNeural NetworksGenetic AlgorithmsExpert Systems<br />I have felt this to be common-sense approach to distinguishing Knowledge Management from the different terms with which it is often confused with. <br />Do let me know your views on it.<br />