Navigating the Organization to Ensure Data Governance - Data Governance: Creating Value from Information Assets - page 27

to identify the reasons for data quality issues, and continuous quality
improvement to identify preventive and corrective actions. Data stewards
ensure that the data definitions, data standards, and the associated busi-
ness rules are defined and documented within their domain for organi-
zational use and that the data is profiled. They also track data deficiencies
and help in formalizing and improving data management processes. An
overall knowledge of data modeling and database design also is required
as they interact with IT to determine the way data is stored and recorded in
physical databases. Apart from technical skills, possessing interpersonal
skills for a data steward is very important. Excellent people and negotiat-
ing skills allow them to be diplomatic, be a team player where necessary,
and mandate changes where appropriate.
Organizations have begun to recognize the need to define terminology
and business rules around data that is increasingly shared across business
processes and organizations. Business leaders are recognizing the costs
incurred in the enormous and often-replicated efforts involved in finding,
gathering, annotating, consolidating, and deploying data to support grow-
ing projects. Clear policies for the definition, access, and use of corporate
information raise the probability of data being leveraged to streamline
business processes, generate new revenue, and drive innovation.
As data governance involves people and processes in addition to data,
business process management or process automation is often the way
to effectively solve some governance problem costs. As data governance
evolves, it encompasses new roles, new processes, and specialized skills to
support it. It cultivates an awareness of specialized skills, processes, and
tools needed to define, maintain, and provision data across the enterprise.
ORGANIZATIONAL RESPONSIBILITIES
FOR DATA GOVERNANCE
Data governance incorporates five major components:
1. Data governance principles: High-level statements about how data
is used
2. Data architecture: Set of data choices to guide the organization
3. Data infrastructure strategies: Technical infrastructure needed to
deliver reliable, secure, and efficient data