KNOWLEDGE MANAGEMENT: OVERVIEW
In the broad and complex field of Knowledge and Data Lifecycle Management, Morgan Borszcz Consulting offers a holistic focused and disciplined methodology for data stewardship and knowledge management. Analysis and decision making are only part of the value of data integration and knowledge sharing. In order to advance the strategic direction of an organization, knowledge and data lifecycle management must produce actionable information, and at the same time facilitate organizational learning. At Morgan Borszcz Consulting (MBC) we are apply leading edge technology solutions for data lifecycle management, document and information management, decision support systems and collaboration, combined with our expertise in network analysis, architecture, and integration. This allows us to provide our clients with true comprehensive enterprise knowledge architecture in the areas of:
• Data Taxonomy & Ontology
• Performance Measuring
• Data Standardization &
• Data Strategy
• Data Cleansing
• Data Migration
• Data Modeling
• Data Warehousing
• Business Intelligence
Does your organization actually have a comprehensive knowledge management practice (by any name) in operation? Advances in technology and the way we access and share information have changed; MBC leads the way in consciously and comprehensively gathering, organizing, sharing, and analyzing corporate knowledge in terms of resources (people, processes and tools), as well as artifacts.
Knowledge management involves data mining and methods of operation to push information to stakeholders. MBC’s flexible and scalable approach can assist any size organization by providing the ability to organize and locate relevant content and expertise required to address specific business tasks and projects, and analyze the relationships between content, people, topics, and activity, and produce to knowledge map report. Our comprehensive approach to knowledge management integrates data modelling, data warehousing, and business intelligence.
In an Information Week article, Jeff Angus and Jeetu Patel describe a four-process view of knowledge management MBC applies to any KM Project:
This Major Process..
Includes these activities..
• Data Entry
• OCR and ScanningVoice Input
• Pulling information from various sources
• Searching for information to include
A knowledge management plan involves a survey of corporate goals and a close examination of the tools, both traditional and technical, required for addressing the needs of the company. The challenge is to select or build software that fits the context of the overall plan and encourage employees to share information.
DATA LIFECYCLE MANAGEMENT
MBC’s Data Lifecycle Management methodology focuses on proper identification, extraction, transformation, and loading of necessary high-quality data for your organization. Our approach integrates the identification of authoritative data sources defined in the systems analysis activities as well as the defined structure of the logical and physical data models of your information systems. Our processes create the necessary construct to initiate complete proper data cleansing activities during the transformation process to ensure data quality.
Our extract, transform and Load (ETL) architecture design includes processes and activities related to data performance, scalability, migration, recovery, operability, and auditability. Our Data quality approach is the foundation for sound organizational decision-making. This is because, database complexity often leads to entry errors, duplication, unintended aliases and other faults in the definition, use and location of data elements. The degree of data quality is a critical prerequisite to any Business Intelligence initiative that seeks to have high integrity in its reports, database analytic outputs, scorecards and dashboards.
Information Architecture thoroughly addressed as part of our data lifecycle management approach. This is because the information systems function creates and regularly updates a business information model and defines the appropriate systems to optimize the use of this information. Our methodology encompasses utilizing best practices in the development of a corporate data dictionary with the organization’s data syntax rules, data classification scheme and security levels. Our process improves the quality of management decision making by making sure that reliable and secure information is provided, and it enables rationalizing information systems resources to appropriately match business strategies. Our structured IT process is also used to increase accountability for the integrity and security of data and to enhance the effectiveness and control of sharing information across applications and entities.
Integrated within our data lifecycle management methodology is logical and physical data model development. Our logical data modeling techniques document the specific entities, attributes and relationships involved in a business function. They serve as the basis for the creation of the physical data model. Our physical data modeling approach represents an application-oriented and database-specific implementation of a logical data model in a set of de-normalized tables and columns incorporating event triggers and referential integrity.