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Data Management and Data Governance

In the increasingly data driven world of today many organizations have come to realize that while computer systems hold the promise of providing competitive advantages they can only deliver on those promises if they are running on high quality data. Many organizations have also learned that data of consistently high quality cannot be realized through occasional data cleanup projects followed by periods of decay. Achieving "consistently high data quality" requires an attitude and mindset that "data" is an important asset and that it therefore should be managed like important assets. Among other things that means:

  1. "Data Governance" should be viewed as an important organizational discipline throughout the organization and members of data governance and data management teams should have assigned roles and responsibilities.

  2. Professionals with assigned roles in the data organization must have suitable productivity tools so that data issues can be identified, viewed and understood and remedies developed. Ideally this means that the productivity tools should allow for defining criteria for when data is "bad" and based on those criteria be able to find, review and resolve the identified issue.

  3. The organization needs to know where data quality issues exist and must be able to track whether progress is being made towards resolving data quality issues.

Many organizations who have gained awareness of the need for data governance struggle on all three fronts. There is now a growing body of work regarding how the organizational aspects should be handled, but from productivity tools and performance tracking perspective most still rely on spreadsheets and other off-line ad-hoc solutions. But with AdapLink and AdapLink Objects it doesn"t have to be that way.

AdapLink with supporting standardized AdapLink Objects delivers a solution to many of the tools-related issues described above.

  1. AdapLink Objects ensure that professionals have easy access to all relevant data in an intuitive object oriented and brows able format.

  2. AdapLink rules engine makes it possible to define rules for what data should look like and then identify all data that doesn"t comply with the rule. The rules engine can be used to not only identify data out of compliance but also to automatically show what the data should be and finally enable the automated resolution of data issues.

  3. Through AdapLink"s browser the user can see and filter data that fall outside ( - or inside) compliance and drill directly into the associated rich AdapLink Object to get the full picture.

  4. Data issues identified through this approach can finally be summarized as KPI's (Key Performance Indicators) to enable tracking of performance by data type, problem category or organizational unit.