Migration

During the data migration step, data from legacy systems is aggregated, converted, transferred and integrated into a new or redesigned information architecture. Duplicate and obsolete data is purged and erroneous data is corrected. According to David Barkaway, data integration manager manager at software solutions provider SAS, the data migration process should be robust and resilient, meaning it should manage all aspects of the migration and be able to cope with high data volumes; execute efficiently by leveraging existing facilities; provide progress updates during the migration process; have built-in backup and recovery; and be reusable, meaning components can be used in other data migration projects.

Maintenance

Data maintenance is the alignment of the master data with changes in the business. This means that data elements are updated to reflect additions, deletions or modifications in business processes. The change could be simple, such as adding a new office address, or complex, such as adding new master data elements for a new business unit that has been spun off from an existing unit.

Quality Control

Data quality refers to the level of correctness, consistency, completeness and integrity of company data. According to IBM data solutions specialists Elizabeth Dial and Cameron Crotty, data quality is not the goal but a key prerequisite for the goal of enhancing business processes. It focuses on identifying and rectifying data errors in information systems in a timely manner. Quality control managers should be in charge of continually monitoring data quality, and there should be a process for resolving the problems identified during monitoring.

Governance

Regulatory disclosure, reporting requirements and the need to protect sensitive corporate information have increased the importance of data governance, which is a comprehensive strategy to manage and secure data. "Data has become the raw material of the information economy," wrote IBM's director of data governance solutions Steven Adler in "CIO" magazine, and thus a critical asset. He suggested several ways for companies to govern data effectively, including selecting a leader and giving him the authority to implement data governance policies; assessing corporate gaps between what is available and what is needed in terms of data integrity and security; bridging the gaps through vision and realistic milestones; calculating the probability of risk based on past data use and abuse; and monitoring and adjusting organizational controls continually.