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What techniques can you use to ensure data accuracy in a governance framework?

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Data accuracy is a key aspect of data governance, which is the process of managing the quality, availability, usability, and security of data in an organization. Data accuracy refers to how well the data reflects the reality of the intended domain or purpose. Inaccurate data can lead to poor decisions, wasted resources, compliance risks, and customer dissatisfaction. As a business analyst, you can use various techniques to ensure data accuracy in a governance framework. Here are some of them:

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Amer B.

PMI-PBA. Hospitality, Operational Planning, Strategic Partnerships. Tourism Opens Minds SDG' 2030

1 Define data quality criteria

Before you can measure and improve data accuracy, you need to define what it means for your data to be accurate. Data quality criteria are the standards and rules that specify the expected level of accuracy, completeness, consistency, timeliness, and validity of the data. You can use business requirements, data models, data dictionaries, and data policies to define data quality criteria. You should also involve the data owners, stakeholders, and users in this process to ensure alignment and buy-in.

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Amer B.

PMI-PBA. Hospitality, Operational Planning, Strategic Partnerships. Tourism Opens Minds SDG' 2030
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To ensure data accuracy in a governance framework, you can employ techniques such as data validation, data profiling, data cleansing, data quality assessments, and data monitoring. These methods involve systematically checking, cleaning, and verifying data to maintain its accuracy and reliability within the governance framework.

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2 Implement data validation and verification

Data validation and verification are the methods of checking and correcting the data against the data quality criteria. Data validation is the process of ensuring that the data meets the predefined format, type, range, and constraints. Data validation can be done at the point of entry, such as using input masks, drop-down lists, or error messages. Data verification is the process of ensuring that the data matches the source or the intended value. Data verification can be done by using checksums, cross-references, or audits.

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3 Use data cleansing tools and techniques

Data cleansing is the process of identifying and resolving the errors, inconsistencies, and anomalies in the data. Data cleansing can be done manually or automatically, depending on the volume and complexity of the data. Data cleansing tools and techniques include data profiling, data matching, data deduplication, data standardization, and data enrichment. Data cleansing can improve the accuracy of the data by removing duplicates, filling gaps, correcting mistakes, and enhancing details.

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Amer B.

PMI-PBA. Hospitality, Operational Planning, Strategic Partnerships. Tourism Opens Minds SDG' 2030
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Data quality criteria are specific standards and characteristics used to evaluate the quality of data. These criteria are essential for assessing data accuracy, reliability, completeness, and overall fitness for its intended purpose. Common data quality criteria include accuracy, completeness, consistency, timeliness, reliability, relevance, and uniqueness. Organizations use these criteria to measure and improve the quality of their data to ensure it meets their business requirements and objectives.

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4 Establish data stewardship and ownership

Data stewardship and ownership are the roles and responsibilities of managing and maintaining the data quality and accuracy. Data stewards are the individuals or groups who oversee the data governance activities, such as defining data quality criteria, implementing data validation and verification, and using data cleansing tools and techniques. Data owners are the individuals or groups who have the authority and accountability for the data, such as defining data policies, granting data access, and ensuring data compliance. Data stewardship and ownership can ensure data accuracy by creating clear expectations, communication, and collaboration.

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5 Monitor and report data quality metrics

Data quality metrics are the measures and indicators of the data quality and accuracy. Data quality metrics can be based on the data quality criteria, such as accuracy rate, completeness rate, consistency rate, timeliness rate, and validity rate. Data quality metrics can be monitored and reported by using data quality dashboards, scorecards, or reports. Data quality dashboards are the visual displays of the data quality metrics, such as charts, graphs, or gauges. Data quality scorecards are the summaries of the data quality metrics, such as ratings, rankings, or targets. Data quality reports are the detailed documents of the data quality metrics, such as findings, analysis, or recommendations. Monitoring and reporting data quality metrics can ensure data accuracy by providing feedback, insight, and action.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What techniques can you use to ensure data accuracy in a governance framework?

1

2

3

4

5

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1 Define data quality criteria

2 Implement data validation and verification

3 Use data cleansing tools and techniques

4 Establish data stewardship and ownership

5 Monitor and report data quality metrics

6 Here’s what else to consider

Business Analysis

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What techniques can you use to ensure data accuracy in a governance framework?

1

2

3

4

5

6

1 Define data quality criteria

2 Implement data validation and verification

3 Use data cleansing tools and techniques

4 Establish data stewardship and ownership

5 Monitor and report data quality metrics

6 Here’s what else to consider

Business Analysis

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