1. Analysis processes help companies understand the current state of data quality and the root cause of problems. Processes are needed to inspect the content and structure of data and provide detailed information about metadata, field formats, frequency counts, relationships and validity. Data governance is already being done in every organization, whether a formal program or not, so processes are needed to identify what practices are already in place and where there are opportunities to improve.

2. Standardization processes help create accurate and consistent data. Companies need processes to establish and formalize elements such as data and business rules definitions, issue resolution and change management procedures, data owners, compliance and security policies, and quality metrics. Once data standards are established, processes are needed to cleanse, validate, match and link data across systems. Companies also need processes to communicate policies, standards, procedures, and metrics relating to data quality, along with processes to ensure employees understand them.

3. Control processes help ensure long-term data quality across departments and divisions. Companies need processes to monitor data, check quality parameters, determine appropriate actions, and log detected faults and resulting actions. It is critical that control processes are implemented at the input sources because data entry is the most common origin of data quality problems according to the Data Warehousing Institute.