Knowledge Management Challenges

Most of the challenges in knowledge management primarily stem from the types of knowledge reuse situations and purposes. Knowledge workers may produce knowledge that they themselves reuse while working. However, each knowledge re-use situation is unique in terms of requirements and context. Whenever these differences between the knowledge re-use situations are ignored, the organization faces various challenges in implementing its knowledge management practices. Some of the common challenges resulting due to this and other factors are listed below.

Data Accuracy: Valuable raw data generated by a particular group within an organization may need to be validated before being transformed into normalized or consistent content.

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Data Interpretation: Information derived by one group may need to be mapped to a standard context in order to be meaningful to someone else in the organization.

Data Relevancy: The quality and value of knowledge depend on relevance. Knowledge that lacks relevance simply adds complexity, cost, and risk to an organization without any compensating benefits. If the data does not support or truly answer the question being asked by the user, it requires the appropriate meta-data (data about data) to be held in the knowledge management solution.

Ability of the data to support/deny hypotheses: Does the information truly support decision-making? Does the knowledge management solution include a statistical or rule-based model for the workflow within which the question is being asked?

Adoption of knowledge management solutions: Do organizational cultures foster and support voluntary usage of knowledge management solutions?

Knowledge bases tend to be very complex and large: When knowledge databases become very large and complex, it puts the organization in a fix. The organization could cleanse the system of very old files, thus diluting its own knowledge management initiative. Alternatively, it could set up another team to cleanse the database of redundant files, thus increasing its costs substantially. Apart from these, the real challenge for an organization could be to monitor various departments and ensure that they take responsibility for keeping their repositories clean of redundant files.

Knowledge Management Challenges

Knowledge Management provides detailed information on Knowledge Management, Knowledge Management Software, Knowledge Management Systems, Knowledge Management Tools and more. Knowledge Management is affiliated with Supply Chain Management Software.

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5 Data Quality Management Challenges

In just about every field of work, there are quality measures in place to ensure customer satisfaction and product/service effectiveness. Manufacturing companies rely on quality control processes to minimize defects and reworks. Consultants measure the quality of their services to ensure repeat business. Journalist rely on quality information and leads to maintain integrity and credibility. But when it comes to corporate data, many organizations fail to understand the significance and drawbacks of unreliable or inconsistent data. This article discusses five quality challenges many organizations face and ways they can be more proactive in managing data.

Among the primary reasons for inconsistent or unusable data are:

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bad data from human data-entry error poorly-structured process lack of data standards across functional units or divisions

Ensuring the quality of data can become extremely difficult when you attempt to integrate data from across multiple sources. Before your organization begins a data-driven initiative it is important that you address issues of data quality within your existing data sources. Aside from the complexity of the actual process of ensuring the quality of your data, below are five challenges you may face when beginning this initiative:

Data ownership Non standard data requirements Choosing the Right Data Management Tools Placing Responsibility for the Quality of Data on the IT Department Reactive vs. Proactive Mentality
Data Ownership

Data ownership, especially on the enterprise level, is a very complicated transition, and can contribute to significant pushback within an organization. Often the business unit managers or technicians entrusted with the implementation of an application assume ownership of the information used within that system. This introduces potential conflicts when these individuals must participate in enterprise-wide data initiatives and expose the internals of their information management to data quality audits and reviews.

Non Standard Data Requirements

Traditionally data management is structured where the business unit's management chain has authority over the information used within the business unit, and each business unit has its own requirements for quality of data. Once data management progresses toward an enterprise-wide set of standards, there is often push back or hesitation by the business unit managers to invest time and resources in addressing issues that were not relevant at the business unit level.

Choosing the Right Data Management Tools

A frequent response by organizations with respect to building a data quality management program is to immediately begin to research the purchase of automated data cleansing or profiling tools. While some data quality tools do provide some benefit right out of the box, without a well-defined understanding of the types and scope of specific quality problems, and without a management plan for addressing discovered problems, buying a tool will not have a significant return on investment in achieving long-term strategic goals.

Placing Responsibility for the Quality of Data on the IT Department

Business units often assume that any issues regarding the quality of data are IT issues, and should be addressed by the technical teams. However, the business rules associated with running the business is best managed by the business client?

Reactive vs. Proactive Mentality

Most data quality programs are designed to react to data quality events instead of determining how to prevent problems from occurring in the first place. A mature data quality program determines where the risks are, what the objective metrics are for determining levels and impact of data quality compliance, and approaches to ensure high levels of quality.

Ways your organization can be more proactive towards data quality management:

Ask for data quality performance measures as part of your business requirements gathering and prioritizing process. Determine, along with the business, how you are going to handle data quality issues both during the development process and when your processes are operational. Monitor data quality at every stage where data is touched Create a data quality management dashboard to monitor the agreed upon data quality performance measures.

5 Data Quality Management Challenges

About Victor Holman

Victor Holman is a performance management expert who helps organizations reach performance goals through best practice analysis and implementation and custom enterprise performance management products and services.

Check out his FREE performance management kit [http://www.lifecycle-performance-pros.com/index.php/free-kit.html], which includes several templates, plans, and guides to help you get started with your next initiative.

Victor's complete Lifecycle Performance Management Kit is a turnkey organizational performance management solution consisting of a web based organizational performance analysis, 7 guides, 39 templates, 600+ metrics, 35 best practices, 48 key processes, a performance roadmap and more.

His Organizational Performance and Best Practice Analysis measures how well organization's utilize the key performance activities that drive organizational success, and identifies cost savings opportunities and the critical path to reaching organizational goals.

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