Your organization’s data is one of its most valuable assets. Unlike assets such as cash, however, data doesn’t always grow in value as it expands in volume — it must properly obtained, stored, secured, cleaned and surfaced so that it can put to use in meaningful ways.
Accordingly, more and more businesses are looking to transition from information management to knowledge management systems. But what exactly are the differences between knowledge management and information management? Are there any concepts and terms from information management that you can use to help your organization implement knowledge management effectively?
This article answers these and other key questions you might have about data management and knowledge management.
What is Information Management?
Information management is the practice of collecting, managing and distributing information. Data can be acquired from a wide range of sources, and can come in a variety of formats. Then it must be stored securely in an organized and structured way that both meets relevant regulations and policies and ensures it is accessible to authorized employees. Strong information governance is essential to effective information management.
What Is Knowledge Management?
Data — no matter how secure and organizations — proves its worth only when it’s used to deliver value to the business. In other words, the bits and bytes must be turned into information in order to be useful intellectual capital. Enter knowledge management: the process of capturing, distributing and effectively using knowledge.
This definition might make knowledge management sound a lot like information management, but in this context, the word “knowledge” means a processed form of information that goes beyond merely extracting facts from collected data. Knowledge discovery focuses on the application of information: Knowledge creation is what happens when people refine information into something useful.
Most organizations have both explicit and tacit knowledge. Explicit knowledge is what can be codified into a set of facts, cases, guidelines, etc., and published or otherwise distributed through a knowledge-sharing pipeline. Tacit knowledge includes insights and intuitions that are more difficult to be recorded. Explicit knowledge is usually information-centric, while tacit knowledge is not. Together, they comprise a knowledge base that is a key factor in an organization’s competitive advantage.
The Difference Between Knowledge Management and Information Management
The easiest way to think about the difference between knowledge management and information management is to say that knowledge management centers on people while information management centers on processes. You could also say that information management focuses on hard facts while knowledge management incorporates opinions and intuitions as well.
Here are some more key differences between knowledge management and information management:
Information management:
- Emphasizes explicit characteristic of information: facts, figures and other hard data
- Can be measured quantitatively in short time frames
- Is easy to replicate
- Is technology-based
Knowledge management:
- Incorporates all aspects of an enterprise, including communication, management frameworks, organizational culture and organizational structures
- Is measured by the changes in the behavior and work of individuals and teams over time
- Leads to innovations unique to the company
- Is people-based
Key Processes
There are four main knowledge management processes:
- Knowledge discovery — Extracting new information from data
- Knowledge capture — Transforming information from tacit to explicit
- Knowledge sharing — Sharing knowledge with others
- Knowledge application — Using knowledge to perform tasks
Key Technologies
No single piece of software can help you with the entire knowledge management process; you need different tools for different tasks. According to the “Knowledge Management Tools” blog, most tools can be put into nine categories:
- Groupware systems
- The intranet and extranet
- Data warehousing, data mining and OLAP
- Decision support systems
- Content management systems
- Document management systems
- Artificial intelligence tools
- Simulation tools
- Semantic networks
All of these tools depend upon knowledge and information properly classified and tagged, so you also need technology for data discovery and classification. While some knowledge management tools offer some of this functionality, a purpose-built solution will provide more robust and accurate results.
Conclusion
Knowledge management and information management differ in scope and purpose, but both are essential for modern organizations. The key core process is to classify and categorize your data and knowledge to make it discoverable and retrievable while ensuring security and regulatory compliance.
