You're developing a database. What's the one mistake you can't afford to make?

1 Define your data requirements

Before you start designing your database, you need to have a clear understanding of what data you need, why you need it, and how you will use it. This will help you choose the appropriate data types, formats, constraints, and validations for your data. You should also document your data requirements and specifications, and communicate them to your stakeholders and users. This will ensure that everyone is on the same page and has the same expectations for the data quality.

2 Implement data quality checks

Once you have defined your data requirements, you need to implement data quality checks throughout your database development process. Data quality checks are methods or tools that verify, validate, or correct your data according to your requirements and specifications. You should perform data quality checks at different stages of your data lifecycle, such as data collection, data entry, data transformation, data loading, and data analysis. You should also automate your data quality checks as much as possible, and use feedback loops to identify and resolve any data quality issues.

3 Monitor and measure data quality

Data quality is not a one-time event, but a continuous process. You need to monitor and measure your data quality on a regular basis, and track your data quality metrics and indicators. Data quality metrics are quantitative measures that evaluate your data against your requirements and specifications, such as accuracy, completeness, consistency, and relevance. Data quality indicators are qualitative measures that assess your data's fitness for use, such as usability, reliability, and timeliness. You should use both data quality metrics and indicators to monitor and measure your data quality, and report your results to your stakeholders and users.

4 Improve and maintain data quality

Data quality is not a static state, but a dynamic one. Your data quality can change over time, due to changes in your data sources, data needs, data users, or data environment. You need to improve and maintain your data quality by implementing data quality improvement initiatives and data quality maintenance activities. Data quality improvement initiatives are projects or actions that aim to enhance your data quality by addressing the root causes of data quality issues, such as data cleansing, data standardization, or data integration. Data quality maintenance activities are tasks or routines that aim to preserve your data quality by preventing or minimizing data quality issues, such as data backup, data security, or data governance.

5 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?