How can data modeling tools improve your data governance?

Data Model has potential to highlight potential data quality issues much in advance. Normally Data Architect, before starting a modelling exercise, would perform Data Analysis and Data Profiling. It will bring normal data patterns as well as anomalies to the front. If these are handled gracefully, data quality is taken care of. In some cases, Modelling also enforces, integrity constraints. It assures, orphan records are lying around.

Using data modeling tools like dbt, you can enhance your data quality. These tools has automated features help check and fix data errors, ensuring data remains consistent across various sources. They also help apply rules to measure with metadata.

Data Model gives first impression of highly sensitive data in regards to GDPR or similar frameworks. Early classification of data through Data Model, ensures implementation is handled as soon as data moves to target system. Apart from Access rights, encryption, masking; Data Model highlights data retention requirement. Right to Forget principle requires, data deletion after specific timeframe. So timely deletion of data can be enforced through Data Model.

DBT helps label sensitive data like personal or confidential info too. You can use dbt to apply security measures such as encryption, hiding senstitive data. (Anonymization layer)

Picture speaks thousand words! Data Model is pictorial representation of Data stored in underlying system. In one glance, experienced eye, can find challenges in underlying data asset. Its a first go to place for Technical glossary.

Data Model has recently lost its significance due to onset of Data Lake and Big Data. Industry veterans will agree, teams pay price in the form of, huge maintenance cost, in absence of Data Model. Its essential to have data model at the start of the project and keep it updated at regular frequency. The relevance of Data Model is fully experienced when fresh eyes start looking at Data Asset. In absence of Data Model, there are high chances of loosing sight especially if the data asset size is huge. Build the model keeping eye on best practices around Governance is key.

Data stakeholders have high expectation from Data Model. Its one stop shop for most of the questions about underlying data asset. Its a common communication language. Involving business stakeholders during Conceptual Model enhances accuracy of model.

Data models are dynamic and living systems, not just static documents. They should evolve and adapt to the changing needs of your organisation. Striking the right balance is crucial: a well-designed data model should be flexible enough to accommodate future changes without adding unnecessary complexity down the road. Continuously monitoring and updating data models ensures they remain effective tools for data governance, helping organisations stay agile and responsive to evolving data requirements. It's about maintaining alignment with data sources, systems, and business processes while proactively addressing any issues or gaps that may arise.