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How do you share data quality best practices with analysts?

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Data quality is the degree to which data meets the expectations and requirements of its users and stakeholders. It is essential for analysts to ensure that the data they use and produce is accurate, complete, consistent, relevant, and timely. However, data quality is not a one-time task, but a continuous process that requires collaboration and communication among analysts and other data professionals. In this article, you will learn how to share data quality best practices with analysts in your organization or team.

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James Kelly

AI and treasury transformation: treasurer turned coach, helping multinational treasury teams to improve cash flow by…

1 Define data quality standards and metrics

The first step to share data quality best practices is to define what data quality means for your specific context and goals. You should establish clear and measurable standards and metrics for data quality, such as accuracy, completeness, consistency, relevance, and timeliness. These standards and metrics should be aligned with the expectations and requirements of your data users and stakeholders, as well as the data sources and processes. You should document and communicate these standards and metrics to your analysts and other data professionals, and ensure that they understand and follow them.

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James Kelly

AI and treasury transformation: treasurer turned coach, helping multinational treasury teams to improve cash flow by millions and reduce workload by 20%+ | Experienced FTSE100 Treasurer | Speaker
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In my experience it is helpful to conduct a data inventory - analysing existing metrics and datasets to ensure they can be compared across the organisation if relevant and benchmarking them against these standards.

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2 Implement data quality checks and controls

The second step to share data quality best practices is to implement data quality checks and controls throughout the data lifecycle. You should design and execute data quality checks and controls at different stages of data collection, processing, analysis, and reporting. These checks and controls can include data validation, verification, cleansing, transformation, profiling, and auditing. You should use appropriate tools and methods to perform these checks and controls, such as data quality software, scripts, queries, or dashboards. You should also document and communicate these checks and controls to your analysts and other data professionals, and ensure that they apply them consistently and effectively.

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James Kelly

AI and treasury transformation: treasurer turned coach, helping multinational treasury teams to improve cash flow by millions and reduce workload by 20%+ | Experienced FTSE100 Treasurer | Speaker
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To create effective real time dashboards, it’s important that data is right first time. This means that time needs to be invested to ensure that transactions are correctly coded and that continuous improvement processes look to identify errors and omissions and to ensure that they are not repeated.

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3 Monitor and report data quality issues and improvements

The third step to share data quality best practices is to monitor and report data quality issues and improvements on a regular basis. You should track and measure the performance of your data quality standards and metrics, and identify any gaps, errors, or anomalies in your data. You should also document and communicate these issues and improvements to your analysts and other data professionals, and provide feedback and guidance on how to resolve or prevent them. You should also use data quality reports and dashboards to visualize and communicate the status and trends of your data quality to your data users and stakeholders, and demonstrate the value and impact of your data quality efforts.

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James Kelly

AI and treasury transformation: treasurer turned coach, helping multinational treasury teams to improve cash flow by millions and reduce workload by 20%+ | Experienced FTSE100 Treasurer | Speaker
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It’s easy for users to assume that data quality comes easily so tracking and reporting the work done is really helpful and helps ensure that people continue to focus on delivering quality data.

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4 Foster a data quality culture and mindset

The fourth step to share data quality best practices is to foster a data quality culture and mindset among your analysts and other data professionals. You should promote a shared vision and commitment to data quality across your organization or team, and encourage collaboration and communication among your data professionals. You should also provide training and education on data quality concepts, techniques, and tools, and create opportunities for learning and sharing best practices. You should also recognize and reward your data professionals for their data quality achievements and contributions, and motivate them to continuously improve their data quality skills and knowledge.

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5 Review and update data quality practices

The fifth step to share data quality best practices is to review and update your data quality practices periodically. You should evaluate the effectiveness and efficiency of your data quality standards, metrics, checks, controls, reports, and dashboards, and identify any areas for improvement or optimization. You should also monitor the changes and trends in your data sources, processes, users, and stakeholders, and adapt your data quality practices accordingly. You should also solicit feedback and suggestions from your analysts and other data professionals, and involve them in the review and update process.

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6 Share data quality best practices with analysts

The sixth step to share data quality best practices with analysts is to share them with other analysts outside your organization or team. You should leverage platforms and channels such as blogs, podcasts, webinars, conferences, forums, or social media to share your data quality insights, experiences, challenges, and solutions with other analysts in your field or industry. You should also network and connect with other analysts who are interested in or working on data quality, and exchange ideas and best practices. You should also seek out mentors or experts who can provide you with advice or guidance on data quality, and learn from their expertise and perspectives.

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7 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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James Kelly

AI and treasury transformation: treasurer turned coach, helping multinational treasury teams to improve cash flow by millions and reduce workload by 20%+ | Experienced FTSE100 Treasurer | Speaker
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Often, things won’t go right first time and that can be a good learning experience. When a need is identified to track and report data, it can be difficult to get good quality data if the processes and monitoring were not built with that in mind as a deliverable. Consequently data may be patchy, late, inconsistent or just wrong. Sharing best practice and the lessons learned can help ensure that quality improves to resolve any issues.

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How do you share data quality best practices with analysts?

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1 Define data quality standards and metrics

2 Implement data quality checks and controls

3 Monitor and report data quality issues and improvements

4 Foster a data quality culture and mindset

5 Review and update data quality practices

6 Share data quality best practices with analysts

7 Here’s what else to consider

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How do you share data quality best practices with analysts?

1

2

3

4

5

6

7

1 Define data quality standards and metrics

2 Implement data quality checks and controls

3 Monitor and report data quality issues and improvements

4 Foster a data quality culture and mindset

5 Review and update data quality practices

6 Share data quality best practices with analysts

7 Here’s what else to consider

Analytical Skills

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