The Data Warehousing Institute (TDWI) estimates that data quality issues cost U.S. businesses more than $600 billion each year. The problem with data is that its quality degenerates over time. Experts say over 2% of customer files become obsolete in one month because of customer profile changes (i.e. change of address, name, etc.). In addition, data entry errors, system migrations, and department fragmentation also result in inaccurate and unusable data.

The good news is that data quality is not difficult to achieve. The key is to treat data as a unified corporate resource and to implement a methodology that lays the foundation for ensuring data remains at a consistently high quality regardless of where it resides.

The following are some suggested steps that businesses can take to achieve data quality:

Step 1: Launch a data quality program
Step 2: Develop a project plan
Step 3: Build a data quality team
Step 4: Review business processes and data architecture
Step 5: Assess data quality within your organization
Step 6: Clean the data (i.e. correct, filter, detect, report, prevent)
Step 7: Improve internal business practices (i.e. educate, reward)

It’s important to keep in mind that achieving data quality takes a group effort; all departments are to be accountable for ensuring corporate and customer data is accurate and updated when needed. With a few processes in place and company-wide buy-in, businesses can quickly achieve and maintain data quality throughout their enterprise.