How do you design and maintain BI data marts?

1 Define the scope and purpose

The first step is to define the scope and purpose of the BI data mart. What business questions do you want to answer? What data sources do you need to access? What level of detail and granularity do you need? How often do you need to refresh the data? How will you ensure the data quality and consistency? These questions will help you determine the scope and boundaries of the data mart, as well as the key performance indicators (KPIs) and metrics that you will use to measure its effectiveness.

2 Choose the data model

The next step is to choose the data model that best suits your BI data mart. There are two main types of data models: dimensional and relational. Dimensional models use facts and dimensions to organize data into a star or snowflake schema, which enables fast and flexible querying and analysis. Relational models use normalized tables and keys to store data in a third normal form (3NF) or higher, which reduces data redundancy and improves data integrity. The choice of data model depends on factors such as the complexity, volatility, and volume of the data, as well as the analytical needs and preferences of the users.

3 Design the ETL process

The third step is to design the extract, transform, and load (ETL) process that will populate the BI data mart from the data sources. The ETL process involves extracting data from various sources, such as databases, files, or APIs, transforming it to meet the requirements of the data model, such as cleaning, filtering, aggregating, or enriching it, and loading it into the data mart, either in batches or in real time. The ETL process should be robust, scalable, and efficient, as well as documented and tested.

4 Implement the security and governance policies

The fourth step is to implement the security and governance policies that will protect and regulate the access and usage of the BI data mart. Security policies include authentication, authorization, encryption, and auditing mechanisms that ensure only authorized users can access and modify the data. Governance policies include data lineage, metadata, and quality management tools that ensure the data is accurate, consistent, and traceable. Security and governance policies should be aligned with the organizational standards and regulations, as well as the needs and expectations of the users.

5 Deploy and test the BI data mart

The fifth step is to deploy and test the BI data mart in the target environment, such as a cloud or on-premise platform. The deployment process involves moving the data mart from the development to the production stage, ensuring it is compatible and integrated with the existing infrastructure and systems. The testing process involves verifying the functionality, performance, and reliability of the data mart, as well as validating the data quality and accuracy. The deployment and testing process should be done in stages, such as alpha, beta, and final, and involve feedback and approval from the stakeholders.

6 Monitor and maintain the BI data mart

The final step is to monitor and maintain the BI data mart, ensuring it meets the changing needs and expectations of the users and the business. The monitoring process involves tracking and measuring the usage, performance, and satisfaction of the data mart, as well as identifying and resolving any issues or errors. The maintenance process involves updating and enhancing the data mart, such as adding new data sources, features, or reports, or improving the data model, ETL process, or security and governance policies. The monitoring and maintenance process should be done regularly and proactively, as well as involve communication and collaboration with the users and the stakeholders.

7 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?