How do data marts improve BI performance and scalability?

Data mart's superpower is user empowerment. Data marts empower users by providing them with the correct data, in the appropriate format, and at the right level of detail. The reduced complexity translates to a lower barrier in the mastery of skills required to obtain insights from data. This empowers business units to be more self-sufficient, proactive, and agile in their decision-making processes now that they can make informed decisions without relying heavily on IT support.

The BI system can be created from DW or DM, I would say the data platform plays an important role here as well, with better computing nowadays its easier to design a BI solution on DW and make it perform really well, but designing BI solution off DM gives a immense benefit, where you can design DM with all the logic that needs to encompassed, for ex: you can push all your complex logic and formulas to the integration layer and give users a semantic layer which is very easy to navigate as well as easy to analyze as well, I always start my project with DW and eventually shift it to DM.

nowadays its easier to design a BI solution on DW and make it perform really well, but designing BI solution off DM gives a immense benefit, where you can design DM with all the logic that needs to encompassed, for ex: you can push all your complex logic and formulas to the integration layer and give users a semantic layer which is very easy to navigate as well as easy to analyze as well, I always start my project with DW and eventually shift it to DM.

Crafting efficient data marts demands meticulous consideration. The very essence lies in comprehending the granularity and scope of data. Yes, organizations do need to weigh factors like data models type, data type, origin, architecture, and integration when creating a data mart. But data marts isn't just about their technical aspects; it's about how they contribute adding to the overall intelligence ecosystem of an organization and, of course, about the people that will be using them.

Building a Data Mart needs special data modeling skills, the architect or data modeler should understand the business, data, and integration to come up with the model, once the logical model is designed, you will run through the scenarios and make sure that your logical model can answer them all and then convert the logical to the physical model, most DM tools have a feature to generate DDL based on the target DB. Depending on your integration logic and back-end database you would choose either ELT or ETL approach to load data.

well, the article only talks about the communication aspect of best practices, DM design has to go through meticulous work on writing source and target mappings and integration logic and translation of logic to code etc. you'll have to follow standards all through the process to design an effective model, Data Modler should be proficient in all aspects of data and have to work with other teams/developers and maintain the standards to build a good model.

Data Marts can build for any reporting or analytic use cases, for ex: we designed a mart to capture vaccinations data for all employees around the company, the Data Mart loops through more than 5 systems every day and captures the data from one by one and it batches and arranges the data in easy to consumption model,