Establishing Accountability and Ownership
For it to be effective, responsibilities and accountabilities must be clearly
defined. Leadership must designate individuals and groups that are responsible
and accountable for governing specific types of data or domains. This includes
defining data owners - the individuals or business units responsible for
individual data domains and assets.
Data
Governance Data owners are responsible for overseeing access controls,
data quality, ensuring regulatory compliance and more. They serve as the point
of contact between business needs and the technical implementation of
governance policies.
Developing Its Policies
Once responsibilities are established, organizations need to develop a set of
mandatory policies around data. Core policies should address data
classification, data quality, data security, privacy, records and information
management, data retention and deletion. The policies provide guidelines for
appropriate data collection, use, quality standards, access controls and
oversight. They formalize rules and roles to establish accountability across
business units and technical teams and enable consistent data practices.
Implementing Controls and Oversight
Effectively enforcing policies requires implementing supporting processes,
standards and controls. This includes classifying all data according to value
and sensitivity. More stringent security, access and change controls are then
applied to high-value and sensitive data. Data quality standards and tests are
developed to regularly monitor and improve data accuracy and consistency.
Compliance with related regulations like GDPR also necessitates implementing
controls for data privacy, rights management and reporting.
Establishing Governance Processes
Governance is not a one-time initiative but an ongoing process. Organizations
must establish regular processes of oversight, assessment and improvement. This
includes conducting impact assessments of projects and systems handling new
types of data. Periodic audits evaluate compliance while monitoring helps
detect issues needing remediation. Change management procedures govern policy
modifications, and communication keeps all teams apprised. Metrics and
reporting provide transparency into data risks, issues, quality and usage to
senior leadership and boards.
Benefits of Effective Data Governance
With the proper implementation, it provides numerous business benefits:
Improved Decision Making - High-quality, consistent data enables better
analytics and decision making across the organization.
Operational Efficiency - Redundancies are reduced and tasks streamlined through
standardized processes and roles.
Regulatory Compliance - Adherence to privacy, security and industry regulations
is ensured through appropriate controls and oversight.
Risk Management - Critical risks around data privacy breaches, inaccuracies or
unregulated use can be mitigated and addressed proactively.
Increased Trust - Customers and partners have greater confidence in an
organization that prioritizes the responsible and ethical use of data.
Data Monetization - Clean, well-managed data becomes an asset that can be
leveraged for new revenue through analytics and insights products.
Overcoming Common Challenges
While data governance clearly provides strategic advantages, several obstacles
can frustrate implementation efforts:
Resistance to Change - Establishing governance requires alterations to existing
practices and resistance to adopt new processes and accountabilities.
Undefined/Overlapping Ownership - Lack of clear data management roles and
ownership leads to gaps or inconsistencies in governance.
Limited Resources - Robust governance demands time, funding and skills that
organizations may struggle to dedicate.
Cultural Silos - Cross-functional cooperation is difficult without a culture
shift breaking down silos between groups.
Lack of Executive Support - Without strong direction and requirements from leadership,
governance efforts lose momentum.
To succeed, governance requires a phased, multi-year effort that thoughtfully
addresses each challenge through communication, policy, training and cultural
development. With persistence and commitment, the payoffs of empowered
data-driven decision making are well worth overcoming initial obstacles.
As digital tools grow ever more sophisticated and data-hungry, governance is no
longer optional for organizations embracing AI, IoT, analytics and cloud computing.
Effective data governance establishes the controls and shared understanding
necessary for teams across departments to work together in leveraging data
responsibly and ethically as a corporate asset. Even basic governance provides
structure that accelerates data-driven innovation while mitigating legal and
reputational risks. For companies committed to digital transformation,
establishing a strategic approach to governing their full data lifecycle is a
key priority.
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