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Data Quality Services

Data - The most valuable asset

Consultants emphasize that data has become one of the most valuable corporate assets. It is used and reused in various business intelligence applications to support sophisticated analysis and decisionmaking processes that make the company more competitive. But the value of data is clearly dependent upon its quality. Decisions based on flawed data are suspect and can dearly cost the company.

Gartner Group stated that that based on its analysis, non-quality data can cause business losses in excess of 20 percent of revenue and can cause business failure.
With this in mind, it makes definite corporate sense to thoroughly cleanse any data prior to storing it in a secondary site, such as a data warehouse, and utilizing it in the decision-making process.

Clean, useful and accurate data translate directly to the bottom line for most companies. It represents the added revenues that are realized when businesses correctly model and track their customer relationships, product or service preferences. Analyses performed using data warehouses containing flawed information will lead to flawed strategic decisions.

KnowledgeBase Professional Data Quality Services

KBase Data Quality Service helps to radically enhance the quality and integrity of the data. It combines cutting-edge technologies that turn legacy account information into problem-free standardized files. Our Data Quality Service ensures that data is cleansed of errors, anomalies, duplication and misspellings. We provide a framework for increasing data quality levels and ensuring that data quality is maintained at levels that are satisfactory for all information stake-holders in an enterprise. This framework consists of a combination of knowledge management, intelligent S/W, an analysis of embedded business rules, process improvement techniques and ongoing educational and training programs. We provide an integrated suite of data quality measuring, filtering and certification tools to provide customers immediately measurable improvements in levels of data quality in the short term, and to allow data consumers to separate business-oriented data quality rules from their explicit coded implementation.

By applying advanced data quality management techniques to the information manufacturing chain, organizations can identify and eliminate process inefficiencies, reducing upward-spiraling operational maintenance costs. As a direct result, these organizations can effectively manage the quality of information while isolating data quality rules as a valuable enterprise resource.

Advance Data Quality Management Methods

KBase work with data consumers to understand, identify, and abstract data quality requirements, to determine the data quality rules, and to integrate a rule-based system with a functional library that will test and validate data items at the insertion point. The tools that we offer allow the user to integrate inprocess qualification into the information manufacture and processing chain to both measure and validate data quality. At the same time this provides feedback for the identification of root causes of data quality problems.

A streamlined data merging, migration, and quality assurance methodology will uncover synergistic opportunities when combining data sets. Leverage in affecting the organizational bottom line can be obtained through the qualified merging of data resources by decreasing operational costs (e.g., error detection & correction) and by increasing customer response and customer satisfaction.

KBase will provide guidelines for both the acquisition of valid data over the Internet, as well as guidelines for accurate, customer-focused data presentation.

Data Quality Methodology

K-Base uses the CDQM (Complete Data Quality Methodology) methodology. Essentially this framework consists of 3 core phases - state reconstruction, assessment and improvement. One of the most compelling aspects of this framework is the focus on business process and organisation services, this helps focus the efforts on the pain areas where costs are highest.

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