Data
governance (DG) is the overall management of the availability, usability,
integrity and security of data used in an enterprise. A sound data governance
program includes a governing body or council, a defined set of procedures and a
plan to execute those procedures.
The
initial step in implementing a data governance framework involves defining the
owners or custodians of the data assets in the enterprise. This role is called
data stewardship.
Processes
must then be defined to effectively cover how the data will be stored,
archived, backed up and protected from mishaps, theft or attacks. A set of
standards and procedures must be developed that defines how the data is to be
used by authorized personnel. Moreover, a set of controls and audit procedures
must be put into place that ensures ongoing compliance with internal data
policies and external government regulations, and that guarantees data is used
in a consistent manner across multiple enterprise applications.
Once
an overarching strategy is defined and data owners and custodians are
identified, data governance teams are often formed to implement policies and
procedures for handling data. These teams can comprise business managers, data
managers and staff, as well as end users familiar with relevant data domains
within the organization. Associations dedicated to promoting best practices in
such data governance processes include the Data Governance Institute, the Data
Management Association (DAMA) and the Data Governance Professionals
Organization
Often,
the early steps in data governance efforts can be the most difficult, as it is
characteristic that different parts of an organization have diverging views of
key enterprise data entities -- such as customer or product; these differences
must effectively be resolved as part of the data governance process. To the
extent that data governance may impose strictures on how data is handled, it
can become controversial in organizations.
Data
governance is a particularly important component of mergers and acquisitions,
business process management, legacy modernization, financial and regulatory
compliance, credit risk management, analytics, business intelligence
applications, data warehouses, and data lakes.
User questions & answers