Facing data quality issues in a growing database, how can you use data architecture principles to stay ahead?

-Utilize Databricks Delta for data quality assessments, enforcing schemas, validating data, and collecting statistics. -Automate cleansing with Delta's batch and stream processing, ensuring consistency via ACID transactions and time travel. -Implement governance with Unity Catalog and audit logging for GDPR or HIPAA compliance. -Use Databricks Unified Data Analytics Platform for intelligent modeling with Apache Spark's MLlib and scalable storage in Delta Lake. -Integrate data carefully using Databricks Connect or Delta Lake, ensuring robust pipelines. -Monitor continuously with Databricks tools, Grafana, or Azure Monitor to promptly address data quality issues.

Profile the data for completeness, conformity, uniqueness and accuracy. Analyse the results and identify inconsistent patterns. Define strategies for the same and implement on historical data. Apply the rules in the ingestion pipelines for incremental data for existing and new sources as an output of the assessment.

In my view, regular data hygiene is crucial to keeping a growing database healthy. I automate as much of the cleaning process as possible, checking for duplicates, errors, and inconsistencies. This catches problems early before they become major headaches. I also build data cleaning into our standard operating procedures, ensuring new data is scrubbed before it enters the system. By being proactive and consistent, we maintain a high level of data quality, making it a reliable asset for decision-making.

Include the data governance policies and validations on your development lifecycle to guarantee that any change on your data architecture support all policies and processes of the data governance team.

Creating proper data models, entity and relationship diagrams, implementing the proper schema & table designs with necessary constraints before creating databases helps in data quality issues and avoids duplications and redundancy in the database.

Adding to the Bala's thoughts, need to have forecasting plan in place to take are of performance issues in future. Ensure to have minimal normalization as we do not have a challenge now a days in terms of storage, which was main hurdle during early 90's. Also database design to be made by considering the type of application being handled . For example example for heavy OLTP transactions both insert and querying data both are very crucial. Proper configuration setting has to be made for max degree of parallelism.

Create metadata driven pipelines leveraging configuration based approach for implementing self healing data quality at the point of ingestion. Define threshold for errors. Should be parameter driven to ensure that repeatability is built in. This would ensure adaptability to future needs while addressing the data issues.

In my opinion, the biggest hurdle to data quality is empowering staff to input it correctly in the first place. You can do all the checks, error reports and transformations you like but if the people inputting it are not invested in what they do it'll never be right. "I haven't got time", "it doesn't matter" and "I'll do it later" are too common as excuses. We have allowed ourselves to believe that "the system will fix it", and if the data is wrong then "the system is rubbish" or "it's the system's fault". All of the previous points are very valid, but they are relatively high level compared to actually fixing issues at source. They should be an accompaniment to good data entry principles, not a catch to allow bad data input to continue.