How do you ensure data quality when resources are limited?

1 Define your data needs

Before you collect, analyze, or report data, you need to define your data needs clearly. What are the objectives and questions of your performance reporting? What are the key performance indicators (KPIs) and metrics that matter? What are the data sources and methods that are relevant and reliable? By defining your data needs, you can focus on the essential data and avoid wasting resources on irrelevant or redundant data.

2 Establish data standards

To ensure data quality, you need to establish data standards that specify how data should be collected, stored, processed, and reported. Data standards include data definitions, formats, codes, rules, validations, and documentation. Data standards help you ensure consistency, accuracy, and completeness of your data across different sources and systems. They also help you avoid errors, duplicates, and conflicts in your data.

3 Implement data checks

Data checks are procedures that verify and monitor the quality of your data throughout the data lifecycle. Data checks include data audits, reviews, tests, and feedback. Data checks help you identify and correct any data issues, such as gaps, outliers, anomalies, or inaccuracies. They also help you measure and improve the quality of your data over time.

4 Automate data processes

Automating data processes can help you save resources and improve data quality. Data processes include data collection, integration, transformation, analysis, and reporting. Automating data processes can help you reduce human errors, increase efficiency, and ensure consistency and timeliness of your data. You can use various tools and techniques to automate data processes, such as scripts, macros, workflows, dashboards, or alerts.

6 Learn from data quality best practices

Data quality is not a one-time activity, but a continuous process that requires learning and improvement. You can learn from data quality best practices by benchmarking your data quality against industry standards, following data quality guidelines and frameworks, and seeking feedback and advice from data quality experts and peers. You can also learn from your own data quality experiences by documenting your data quality issues, actions, and results, and applying your lessons learned to future data quality efforts.

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?