The Role of Data Governance in an Organization - Data Governance: Creating Value from Information Assets - page 11

business intelligence and data warehousing initiatives, to support master
data management initiatives, to facilitate the migration of legacy data, to
meet compliance and legislative requirements, and to improve corporate
flexibility and business agility.
Data governance includes establishing who in the organization holds
decision rights and is accountable for an organization's decision making
about its data assets. Khatri and Brown's (2010) framework for data
governance includes five interrelated decision domains: data principles,
data quality, metadata, data access, and data life cycle. An organization's
data principles set the boundary requirements for the intended uses of
data and establishes the extent to which data is an enterprise-wide asset.
It specifies appropriate data policies, standards and guidelines, and prin-
ciples for sharing and reusing data. Data principles also consider the regu-
latory environment that influences the business uses of data.
Business users play an important role in managing data quality as
well as its life cycle, interpretability, and access. As poor data quality can
impact an enterprise at both operational and strategic levels, data gover-
nance helps set the organization's standards for data quality. The quality
of data refers to its ability to satisfy its user requirements. Data quality
dimensions, such as accuracy, timeliness, completeness, and trustworthi-
ness, are defined in the context of the end use of data. This, in turn, is the
basis of how data is interpreted through metadata and accessed by users.
Metadata describes the data and provides a mechanism for a concise and
consistent description of the representation of data. The definition of the
production, retention, and retirement of data, which constitutes the data
life cycle, plays a key role in operationalizing the data principles into IT
(information technology) infrastructure.
After enterprise resource planning and data warehousing, the focus of
organizations is now toward data governance, especially to aid in improv-
ing reporting and business intelligence. There is a growing awareness
of the costs of inconsistent, inaccurate, and unreliable data. A growing
number of organizations have either already implemented data gover-
nance in a limited way or enterprise-wide or are planning to implement
it in the coming years. Estimating the cost to the business of poor data is
key to building a business case for implementation of data governance and
ensuring that the program receives ongoing support and funding.
Data governance is the process of establishing and maintaining com-
mon standards between functions/departments within an organization
to establish how common business data and metrics are created, defined,