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5. Reliability: Delivers dependable results across 3. Timeliness
users.
6. Relevance: Applies to the specific needs of the
organisation.
Defining Data Governance
Data governance is the structured approach to
managing data assets, with clear roles, policies, and
responsibilities. A well-implemented governance
framework standardises data handling, ensuring
accuracy and security throughout the organisation.
The Interplay
Data quality focuses on the data’s integrity, while
governance ensures a secure, consistent data
ecosystem. Beyond quality support, governance
upholds compliance, ethical use, and risk
management, establishing data as a sustainable
organisational asset.
The Six Key Dimensions of Data Quality
For data to be a strategic asset, it must meet six critical
dimensions ensuring its utility and trustworthiness:
1. Accuracy
• Data must be correct and reflect reality. Inaccurate
data undermines decisions, leading to costly errors.
Regular validation checks are essential to detect and
rectify inaccuracies early.
2. Completeness
• All required information should be present,
ensuring no gaps in analysis or decision-making.
Organisations must define completeness based on
their specific needs, creating processes to capture
complete datasets.
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• Data should be current when needed. In
today’s fast-paced environment, outdated
data can diminish relevance. Regular updates
ensure data’s timely availability, maintaining its
decision-making value.
4. Consistency
• Consistent data aligns across systems, avoiding
contradictory information. Standardised
formats, definitions, and data harmonisation
are critical for achieving a unified data view.
5. Reliability
• Reliable data produces consistent results across
conditions. Confidence in data integrity is built
through mechanisms that maintain stability
and dependability.
6. Relevance
• Relevant data fulfils its intended purpose,
helping avoid information clutter. Regular
assessments of data relevance ensure alignment
with evolving organisational needs.
Prioritising these dimensions empowers
organisations to develop data they can trust,
making sound decisions that drive efficiency
and competitive advantage.
The Strategic Importance of Data
Quality
Data quality extends beyond operational
efficiency—it’s a strategic asset impacting
decision-making, customer engagement, and
cost management. The benefits of high-quality
data are far-reaching:
1. Accurate Decision-Making
• Quality data is the foundation for reliable
decision-making. Accurate data insights lead
to confident decisions and successful outcomes,
while poor-quality data risks errors and
inefficiencies.
2. Resource Optimisation
• Reliable data prevents waste from misguided
resource allocations, reducing redundancy. High
data quality enables streamlined operations,
focusing resources on value-driving activities.
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