Enforce content standards for completeness & validity irrespective of original format
A one-time cleanup of data is a good starting point, but product data changes continually and mechanisms must be put in place to ensure its ongoing quality and usability. For this reason, many companies are now creating data governance (or stewardship) programs that blend technology, business practices and organizational changes.
Technology plays a critical role in data governance as it can detect problem data before it is passed on to enterprise systems. It can also objectively evaluate incoming data sources and generate 'scorecards' based on completeness and validity.
The DataLens System is the first product to fully support product data governance goals, with real-time monitoring and enforcement of product data quality standards across systems and data sources, including:
- Enforce Content Standards
- Problem – New, inconsistent and incomplete data enters company systems every day causing a wide range of downstream problems. This is due to the fact that there are few standards for product content and most companies lack a mechanism to quickly assess and enforce quality and completeness standards.
- DataLens Solution – A (semantic) content standard can be created — or more likely taken from existing systems or documentation — and applied to all information sources to prevent the proliferation of poor quality data.
- Batch Load Validation
- Problem – Different data sources deliver similar information using different formats, standards and conventions, making it very difficult to assess the quality of the incoming data without manual intervention. Given the data volumes, basic checks and validations are often skipped and poor quality data is allowed into the organization resulting in downstream errors and costs.
- DataLens Solution – All incoming product data can be compared to a known standard for completeness and validity and exceptions can be flagged in real-time for rejection or exception handling.
- Manual Entry Validation
- Problem – Manual data entry is a very common source of non-standard or incomplete information. Systems vary widely in terms of the content standards they enforce — if they enforce any standards at all.
- DataLens Solution – Any system accepting new product data (such as PIM, inventory or ERP systems) can call the DataLens System as a real-time SOA service to check product data completeness and validity before loading the new data.
- Data Scorecard
- Problem – Different data sources deliver information in widely different formats with many different data quality levels. A governance program should monitor and report on these metrics in order to perform corrective action.
- DataLens Solution – Variations in format can be ignored and the underlying data sources and individual records can be scored for completeness and validity — creating not just a scorecard, but a comprehensive ‘fix list’.
