Managing Information As A Strategic Asset

A Data Quality Approach & Methodology For Measurable ROI

In order to illustrate its industry viewpoint and business approach, Datanomic has developed a model called 'The four cornerstones of data quality' to articulate clearly the key facets of a problem-driven approach. This model was developed to help customers and partners understand the steps that Datanomic will go through with them on the way to implementing an overall, long-term and sustainable improvement in an organization's data quality.

The four cornerstones

Datanomic's approach to managing data as a strategic resource identifies four key areas; the cornerstones that provide a stable foundation for data quality.

Control

Data quality management is critical to the business; establishing, measuring and publishing clear performance metrics maintains visibility of this critical asset and helps to promote and maintain the long-term benefits of high data quality. This includes ensuring compliance with regulatory standards, supporting business process management and corporate governance.

Protect

Improving data quality should never be regarded as a quick-fix, one-off activity. It is essential that data defects are prevented from infiltrating the enterprise's data ecosystem. Real-time defect prevention is the most effective way to overcome the reactive approach to data quality management coupled with the policing of all offline data feeds.

Understand

The starting point for dealing with data quality is to gain a clear understanding of the data: how it is captured, its intended uses, its structure and content quality. This requires a mixture of IT and business resources, with individuals who understand the business context working with those who are familiar with the technical aspects of the data: how it is stored and accessed. Between these competencies it is possible to measure how fit for purpose the data is.

Improve

Having identified and prioritized the source and types of defects present, the next logical step is to improve the data. Configurable cleansing rules support the removal of errors and the resolution of inconsistencies. Free-format text, such as names and addresses, can be transformed into defined, structured attributes. Duplicated data should be rationalized to create a unified, consolidated view; it could be a single view of customers or any other entity.

Each of the cornerstones can be tackled individually, but only by addressing all four can organizations achieve the maturity in data quality that allows them to properly manage and exploit their data asset.

The Datanomic approach enables any organization to implement a total, integrated data quality management system at a pace that suits the structure and culture of the business. It offers the flexibility to manage data quality as an enterprise-wide strategic resource and also to address tactical, project-based data quality issues.