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Data Quality

Data quality is essential for accurate information, informed decision-making, and positive business outcomes. Poor data arises from various issues, including manual errors, data silos, and lack of governance, while key dimensions of data quality include accuracy, completeness, and timeliness. Effective data quality management requires executive support, investment in governance, and a culture that prioritizes data responsibility, with AI and machine learning enhancing monitoring and issue detection.

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20 views2 pages

Data Quality

Data quality is essential for accurate information, informed decision-making, and positive business outcomes. Poor data arises from various issues, including manual errors, data silos, and lack of governance, while key dimensions of data quality include accuracy, completeness, and timeliness. Effective data quality management requires executive support, investment in governance, and a culture that prioritizes data responsibility, with AI and machine learning enhancing monitoring and issue detection.

Uploaded by

santukrmandal4
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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Why Data Quality?

Data Quality  Information Quality  Decision Quality  Business Outcomes

Why bad data?

 Intuition is more important to management than data which leads to a culture where
less investment in data initiatives.
 Manual data entry errors
 Data silos – Different department don’t share data among each other caused duplicate
data and other integrity issues.
 Data migration and conversion project
 Scaling of business and its datasets
 No data governance rules

Data quality dimensions 

1. Data Accuracy – Accurately represents reality


2. Data Completeness – All the data for a particular use is present
3. Data Timeliness – Data is available when needed
4. Data Validity – Data conforms to the expected format and range
5. Data Uniqueness – Unique in each dataset
6. Data Consistency – Don’t conflict among different datasets

Data profiling  analyzing multiple datasets and collecting metadata and investigating the data
quality issues. Previously done manually, now being done through rules.

Data parsing

Data Standardization

Identity resolution  Create a single data rich profile for a person or business by validating and
appending information across different datasets  create single customer or business view

Data Linkage

Data Enhancement

Data Inspection and Monitoring

Data Governance  The process of managing the availability, usability, integrity and security of
the data in the enterprise system.
Data Quality best practice 

1. Get executive sponsorship


2. Invest in establishing data governance
3. Invest time in data quality training
4. Establish data quality metrics
5. Appoint data quality steward

Data quality is no longer just a technical task—it’s a strategic necessity. In today’s fast-evolving
data environments, traditional cleanup approaches fall short. The rise of complex ecosystems—
spanning cloud, real-time, and unstructured data—demands resilient, adaptive quality systems.

AI and machine learning are transforming data quality management, enabling predictive
monitoring and real-time issue detection. Yet, these technologies depend on sound governance
and quality training data. Human oversight remains critical; quality must be defined in context,
guided by business needs.

Culture plays a major role. Quality initiatives succeed when organizations embed responsibility
across teams, offer intuitive tools, and align practices with how people actually work with data.

Real-time monitoring and integration into workflows ensure that quality is proactive, not
reactive. Strong governance, clear ownership, and business-driven metrics form the foundation.

Future-ready data quality isn’t a project—it’s a continuous journey powered by smart systems,
strong culture, and a focus on business value.

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