November 21, 2007

The Keys to Data Quality

A PricewaterhouseCoopers Global Data Survey of 452 company Chief Information Officers shows data management strategies are still not a top priority among senior executives. Only 34% of the respondents claimed to be “very confident” in the quality of their data.

 

The quality of your product and price information may cost your business vast amounts of money and lead to disruptions in the supply chain, poor business decisions and inferior customer service. Whether it is a manufacturer’s or distributor’s data base, data quality can be compromised many ways.

  1. Received – how is the data received or managed. If data or data processes are outsourced to a 3rd party, external data quality may be questionable.
  2. Processed – when incorrect data enters your operating system it may be propagated across multiple systems or locations compromising data quality throughout your company. Even simple and straight forward data quality errors can grow into a complex tangle that sabotages company wide data quality efforts. 
  3. Stored – storing data in multiple systems puts data consistency at risk.
  4. Merged, consolidated, integrated – when bringing data together from multiple systems to improve operational effectiveness or create an internal/company data warehouse or improve customer service, you will most likely face disparate and inconsistent data across the enterprise.
  5. Maintained – each act of data maintenance creates a potential opportunity for errors that may have unpredictable consequences.
  6. Entered – data quality suffers when data fields are left blank or incorrectly filled in with the wrong value or industry standards and requirements.

 

Keys to Data Quality

 

  1. Standards – Understand and adopt industry data standards- standardized business information is the foundation for commerce and when companies use the same terms for unit of measure, for example, it eliminates confusion and provides information consistency between trading partners.
  2. Monitoring – Assess your internal data quality and monitor and report (create report card method) on the ongoing quality of that information to your company stakeholders.
  3. Cleansing – implement business processes and tools such as Product Information Management (PIM) applications to enable your business team to manage the process for correcting and enhancing data. Parse, transform and correct existing data in a well thought out plan.
  4. Centralize – use an external centralized information bank such as the Industry data Warehouse (IDW) for storing and disseminating your business information to your trading partners, manufacturer reps and end customers.
  5. Validate – participate in the industry Data Audit Certification program.
  6. Data Match – match similar records or SKUs with your trading partners. Unless trading partner the data is identical mismatched data still requires manual reconciliation processes that costs money. In the PricewaterhouseCoopers Global Data Study 50% of the respondents had incurred extra costs sue to the need for internal reconciliations and 33% failed to bill or collect receivables.
  7. Management – make data quality a senior management executive objective and measure on going effort and effectiveness.

 

Assessing your company’s data and adopting a proactive, business focused data quality strategy is the fastest and most cost effective way to resolve data quality issues.