Monitoring

All of the actions and goals defined in your data quality strategy have to be actively monitored. Utilising monitoring tools that may construct alerts and communicate through various channels is crucial for early detection.

Also it’s crucial to log your incidents, and categorise them based on their impacted dimensions. This practice means that you can focus your attention on specific areas and discover potential gaps in your strategy. Even higher, for those who maintain an incident report, it lets you reflect on how your work in specific areas contributes to reducing the variety of incidents over time.

Periodical revisions of the framework

Your team must review the incident log periodically and update your data quality framework accordingly to fill the identified gaps. This ensures your actions and goals reflect reality and are up so far.

Service Level Indicators and Transparency

It is crucial to measure the fulfilment of your Service Level Objectives. For each SLO, it’s best to have a Service Level Indicator (SLI) that shows the fulfilment of the SLO. For example, in our example you may have a SLI that shows the share of success within the last X days of not having data that’s older than 6 hours in production (timeliness dimension). This helps users understand how the info behaves and builds trust in its quality.

Transparency in practice is vital to extend user adoption and Service Level Indicators are those in command of providing this transparency.

For sharing our data quality metrics (SLIs), I actually like embracing data product concept inside a data-mesh implementation.

Our data quality strategy has these characteristics:

• It’s domain specific because the objectives comes from a business need
• Transparent as we are able to share and wish to share it with users
• Visible as our data quality framework is straightforward to interpret

This aligns perfectly with the definition data-mesh gives to data products. I totally recommend using a data-mesh approach encapsulating data and its quality metrics into data products to boost transparency.

Why data products for sharing our data quality metrics

Per definition, an information product in data-mesh is a self-contained, domain-specific unit of knowledge capabilities. They encapsulate data, processing logic and data quality checks, promoting decentralised data ownership and seamless integration into the broader data ecosystem. They’re designed to serve specific business needs in a selected domain. They’re easily findable and transparent. As integral components of our data quality framework, data products make sure that our strategy aligns precisely with the unique requirements of every domain, providing visibility and transparency for domain-specific data quality.

Considered one of the important thing benefits of knowledge products within the context of knowledge quality is their ability to carry their very own SLIs. By integrating data quality indicators directly into the info products and making them visible through a user-friendly catalog, we empower users to look, request access, and explore data with full knowledge of its quality. This transparency and visibility enhance user confidence and encourage greater adoption.