Every year, companies around the world invest millions of dollars implementing data warehouses. All too often, however, the knowledge gained during these implementations is lost, ignored or not leveraged to its fullest potential. Without access to historical knowledge discovered during the different stages of a warehouse implementation, an organization is unable to maximize the use of its business intelligence and may not fully realize the anticipated return on its technology investments.
By establishing a knowledge management framework, organizations can ensure data warehousing knowledge is effectively shared throughout the project team and effectively transferred to end users.
A Knowledge Management and Knowledge Transfer Framework
Knowledge management is a discipline that promotes an integrated approach to the creation, capture, organization, access and use of an organization's information assets. These assets include databases, documents, and the tacit expertise and experience of the individuals developing and implementing the data warehouse.
A data warehouse contains a great deal of diversified knowledge in its software and data structures, and in the processes and business rules developed by an enterprise. This knowledge, crucial for realizing increased returns on investment, cannot be accessed by end users or derived from the operational data warehouse system alone.
To gain the significant benefits from this knowledge, organizations must deploy a knowledge support system and knowledge management processes that align with the organization's needs.
The first step for deploying a knowledge support system is to develop a knowledge transfer framework which defines the organization's broad information goals in regard to the data warehouse. It identifies:
- What information should be retained over the project life cycle.
- Which communities within the organization will use the information.
- How that information will be captured at each stage of the project.
- How that information will be refreshed during the operational stage of the implementation.
In addition, the knowledge transfer framework provides a model of the knowledge environment during the development life cycle of a system. It illustrates the relationships and interdependencies of people, business processes, events and knowledge domains. With this framework in place, warehouse implementation managers are able to anticipate and plan for the knowledge requirements of each phase of this implementation and, more importantly, future data warehousing projects.
The project team can also develop mechanisms for transferring or transforming the knowledge from one phase to another, successfully negotiating the "phase boundaries" where staff changes and new task orientations can easily result in lost knowledge.
Ensuring knowledge transfer at phase boundaries is very important. Knowledge captured during the build and early deployment stages will be needed later to leverage the warehouse data for new analytic and reporting applications, and to accommodate changing business requirements.
This knowledge transfer is especially important when your company is using outside consultants to assist with your data warehouse. In these cases, the goal should be to fully leverage the experience of these consultants to "get it right the first time" and then transfer the knowledge and experience to your staff.
DW Life Cycle and KM Tools
Data warehousing projects can be characterized by three major life cycle stages: analysis/design/build, go live and operate. Each of these stages involves different communities of interest information technology (IT) specialists, business/financial analysts, management and others who are not specialists in analytic disciplines.
The following knowledge management technologies and processes are useful to support the analysis/de-sign/build stage of the warehouse:
- A meta data capture method (business rules, assumptions, data extract and translation algorithms, etc.) and repository
- A knowledge management workgroup environment which provides project management, document management, collaboration/messaging, expertise/team skills management and a lessons-learned process repository
- An information portal that provides access to external knowledge sources and integrates the information received in the context of the data warehousing project and the knowledge management environment
Many build-phase knowledge repositories can be difficult to use. They are designed to store models and business rules rather than to easily retrieve information. These repositories typically offer limited search, link and view functionality.
An organization should develop a plan to integrate its build-phase repository with its IT-knowledge environment. Documentation reflecting the data warehouse system decision-making processes, as well as the impact of expected changes, should be identified and included in the knowledge framework model.
Some of the knowledge management technologies that can support the go-live stage are:
- A controlled, updateable repository for testing and training documents as well as "lessons learned;"
- An accessible, updateable repository for IT-knowledge components, such as software code, models and rules;
- Analytical reporting and self-service tools to access and use report-based knowledge.
During the operational phase, a sound knowledge management system is particularly valuable in organizations that employ external networks, e- business systems, contingent workforces and extranet knowledge value chains. Functional knowledge management systems should integrate warehouse-derived knowledge with business processes across the enterprise. The key elements of this knowledge management system are:
- A single point of access, or portal, to a wide range of relevant knowledge bases (external and internal) that integrate warehouse analytics and tools with documents, databases and management reports from both internal and external sources.
- The ability to notify or alert knowledge-based workers when warehouse data or reports are updated.
- Integrated, desktop access to specialized analytical tools and knowledge-based applications.
The development of a data warehouse is based on a complex set of business rules, algorithms, assumptions and unique data sources. The data in the warehouse can provide its users easy access to valuable information, empowering them to make high-quality business decisions. But this is possible only when the organization can easily obtain and understand the knowledge on which the warehouse is founded.
Schroeck would like to thank Linda Eiland Clark, a principal consultant and knowledge management leader at PricewaterhouseCoopers, who contributed this month's column.
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