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How do you compare data quality between different sources and systems?

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Data quality is a crucial aspect of data management, especially when you need to compare data from different sources and systems. Data quality refers to the degree to which data meets the expectations and requirements of its users and purposes. Poor data quality can lead to inaccurate, incomplete, inconsistent, or irrelevant results, which can affect decision-making, performance, compliance, and customer satisfaction. Therefore, it is important to assess and improve data quality across different data sources and systems. In this article, you will learn how to compare data quality between different sources and systems using some common methods and tools.

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Ilgar Zarbaliyev

Training Program Manager @ Novum LLC | Microsoft 365, Analytical Skills | Microsoft ?MVP | MCT Regional Lead (CEE…

1 Define data quality criteria

Before you can compare data quality, you need to define what it means in the context of your objectives. Data quality criteria are the standards and measures used to evaluate and compare data quality, and they vary depending on the type, nature, and use of data. Common dimensions of data quality include accuracy, completeness, consistency, timeliness, relevance, and validity. To define data quality criteria, identify the dimensions that are most relevant to your data sources and systems. Then set specific thresholds, indicators, or metrics for each dimension. For example, accuracy can be defined as the percentage of data records matching the reference data; completeness as the percentage of populated data fields; consistency as the number of conflicts or errors; timeliness as the frequency or latency of updates; relevance as alignment with business goals or user needs; and validity as compliance with predefined rules or formats.

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Ilgar Zarbaliyev

Training Program Manager @ Novum LLC | Microsoft 365, Analytical Skills | Microsoft ?MVP | MCT Regional Lead (CEE Region)
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Defining data quality criteria is pivotal for effective data assessment. It involves specifying standards and metrics tailored to your data's type, purpose, and usage. Common dimensions like accuracy, completeness, consistency, timeliness, relevance, and validity serve as reference points. To define criteria: 1-Select Relevant Dimensions: Identify which dimensions matter most for your data. Not all may apply equally to your context. 2-Establish Metrics: Set measurable thresholds or indicators for each dimension. For instance, accuracy could be a percentage match with reference data, and completeness the percentage of filled data fields. By defining data quality criteria, you create a framework to evaluate and enhance data quality.

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2 Collect data quality metadata

After defining data quality criteria, you need to collect data quality metadata from your data sources and systems. This information describes the characteristics and status of data quality, helping you understand the current state of data quality, identify issues, and compare data quality across different sources and systems. Data quality metadata can include data quality scores, which measure the level of data quality based on the criteria; data quality reports that summarize the results of assessment, analysis, and improvement; and data quality logs that track the changes and events that affect data quality. You can collect this metadata by using data quality tools such as software, dashboards, or APIs. These tools help automate the process of assessment, analysis, and improvement, generating metadata in various formats like tables, charts, graphs, or alerts.

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Ilgar Zarbaliyev

Training Program Manager @ Novum LLC | Microsoft 365, Analytical Skills | Microsoft ?MVP | MCT Regional Lead (CEE Region)
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To collect data quality metadata effectively: 1-Define Requirements: Clearly outline the metadata you need based on data quality criteria like accuracy, completeness, and consistency. 2-Choose Tools: Select data quality tools, like software or APIs, capable of assessing, analyzing, and improving data quality while generating metadata. 3-Conduct Assessments: Use chosen tools to assess data against predefined criteria, generating data quality scores. 4-Generate Reports: Create data quality reports summarizing assessment results, highlighting areas needing improvement. 5-Maintain Logs: Implement data quality logs to track changes affecting data quality, providing a historical record for analysis.

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3 Compare data quality metadata

Collecting data quality metadata allows you to compare data quality between different sources and systems. This comparison can help you evaluate your data sources and systems, identify gaps and discrepancies, and determine best practices for improvement. To compare data quality metadata, you can look at data quality scores, reports, and logs. For instance, you can compare accuracy scores or trends of reports, or the frequency and outcome of logs. Data analysis and visualization tools such as software or APIs can also assist in processing, manipulating, and displaying the metadata in various ways.

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Ilgar Zarbaliyev

Training Program Manager @ Novum LLC | Microsoft 365, Analytical Skills | Microsoft ?MVP | MCT Regional Lead (CEE Region)
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To compare data quality metadata effectively: 1-Collect Metadata: Gather data quality information from different sources and systems, including scores, reports, and logs. 2-Define Metrics: Determine the key metrics for comparison, like accuracy scores, report trends, or log data. 3-Analyze Data: Use data analysis tools (software, APIs) to process and visualize metadata, making it easier to compare and identify discrepancies. 4-Identify Discrepancies: Analyze the metadata to spot gaps or trends, highlighting variations in data quality across sources and systems. This process helps you evaluate, improve, and maintain data quality across your organization's data sources and systems.

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4 Interpret data quality comparison results

Interpreting the results of data quality comparison is necessary for understanding the implications of data quality differences and similarities between different sources and systems. This process involves evaluating data quality performance, identifying data quality patterns, and explaining data quality causes and effects. Data interpretation and communication tools can be used to generate, organize, and present data quality insights and recommendations in various formats. For example, you can evaluate if your data sources and systems have high or low data quality scores, identify if they have consistent or inconsistent data quality across different dimensions, or explain why they have higher or lower data quality than others. Additionally, you can use these tools to create narratives, summaries, or feedback.

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Ilgar Zarbaliyev

Training Program Manager @ Novum LLC | Microsoft 365, Analytical Skills | Microsoft ?MVP | MCT Regional Lead (CEE Region)
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Interpreting data quality comparison results involves assessing the differences and similarities in data quality across various sources or systems. Begin by evaluating data quality metrics like accuracy, completeness, consistency, and timeliness. Identify trends or patterns in the data quality scores to pinpoint areas of concern or excellence.

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5 Apply data quality improvement actions

The final step of comparing data quality between different sources and systems is to apply data quality improvement actions. These steps and measures are taken to enhance and maintain data quality across different sources and systems, and can include data quality validation, correction, standardization, integration, and monitoring. Data quality validation checks the accuracy, completeness, consistency, timeliness, relevance, and validity of data. Data quality correction fixes errors or anomalies. Data quality standardization aligns and harmonizes the data formats, rules, and conventions. Data quality integration combines and consolidates data from different sources. Data quality monitoring tracks and measures the performance, trends, issues, and actions across different sources. You can apply these improvement actions by using data quality management tools such as software, dashboards, or APIs to automate the process and manage data quality metadata in a centralized way.

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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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How do you compare data quality between different sources and systems?

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2

3

4

5

6

1 Define data quality criteria

2 Collect data quality metadata

3 Compare data quality metadata

4 Interpret data quality comparison results

5 Apply data quality improvement actions

6 Here’s what else to consider

Data Management

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How do you compare data quality between different sources and systems?

1

2

3

4

5

6

1 Define data quality criteria

2 Collect data quality metadata

3 Compare data quality metadata

4 Interpret data quality comparison results

5 Apply data quality improvement actions

6 Here’s what else to consider

Data Management

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