As your business grows and evolves, so too must your analytical data models. Maintaining these models is not just about keeping them operational; it involves continuously refining, extending, and optimizing them to ensure they remain aligned with the changing needs of the business. This article explores key strategies for maintaining your analytical data models, with a particular focus on model extension and enhancement.
Business needs change over time, whether due to market expansion, new product lines, changes in customer behavior, or evolving business strategies. Your data models need to be flexible enough to adapt to these changes without requiring a complete overhaul. Gradually extend your model by adding new tables, columns, or dimensions as needed; this allows for seamless integration of new data sources or business metrics. As mentioned in the previous article, we recommend implementing version control practices to manage changes in your data models; this ensures that you can track modifications, roll back to previous versions if necessary, and manage updates systematically.
When adding new columns, or modifying the data type of a column, the schema must be updated in Luzmo for the changes to be reflected within datasets previously created in Luzmo, you can find more information about this in our Dataset schema updates in Luzmo article.
Regular audits help identify areas where the data model may no longer align with current business processes or where performance optimizations could be made. During these audits, assess whether the data model accurately represents current business processes, supports all necessary analytics and reporting requirements, and last but not least performs efficiently with growing data volumes.
Recommended steps to audit a data model:
As your data grows, your models must be optimized to handle larger volumes without sacrificing performance. This might involve reindexing tables, denormalizing certain parts of the model, or creating summary tables to speed up queries.
Performance optimization techniques:
Data quality is critical for the accuracy and reliability of your analytical data models. As your business evolves, new data sources or changes in data entry processes can introduce errors or inconsistencies.
Strategies to manage data quality:
As you extend or modify your data models, it's crucial to update your documentation accordingly. This ensures that all stakeholders, from data engineers to business analysts, understand the current structure, logic, and purpose of the model. Make sure to document changes in e.g. data structure (e.g. new columns, tables, relationships, etc.), business logic, usage instructions, etc.
As your data models evolve, it's important to maintain compliance with data privacy regulations and ensure that sensitive data is adequately protected. This may involve updating access controls, anonymizing data, or auditing data usage. Regularly review and update access permissions to ensure that only authorized users can access sensitive data, make sure to anonymize data fields where necessary to comply with privacy regulations, and maintain logs of who accessed or modified data to ensure transparency and accountability.
By extending and optimizing your models to meet growing business needs, ensuring data quality, and keeping documentation current, you can ensure that your models continue to provide valuable insights that drive business success. Remember, a well-maintained analytical data model is a dynamic asset that evolves alongside your business, enabling you to adapt to new challenges and opportunities with confidence.