Data warehouses can be choppy waters for your business users to navigate. They often contain an overwhelming amount of data, most of which has no relevance to their day-to-day responsibilities.
That’s where a data mart comes in handy.
A data mart is a smaller, specified data warehouse that focuses on one function or line of business. This makes it much easier for departments to find the data that’s important to them.
These data marts are only accessible to groups within those business functions. For example, a marketing manager can access a marketing-specific data mart, but they wouldn’t be able to view its financial counterpart.
If you’re still confused between a data mart and a warehouse, try to focus on the size of the two.
A data warehouse is a large structure that gathers data from multiple sources into one single repository. You can then use this data for business intelligence or analysis efforts.
A data mart, on the other hand, is a scaled-down version of a data warehouse. It focuses on one granular business function, rather than the bigger picture. The data mart extracts information from either an overarching warehouse, other sources, or a mixture of both. In theory, you could have an unlimited number of data marts in your organization. It depends on how many functions you have and whether there’s enough valuable data to justify them.
Technically speaking, a data mart is a relational database. It stores current and historical data in rows and columns, which makes it easy to track trends and perform data analysis.
There are three different types of data marts you can choose from:
But how exactly can these smaller warehouses benefit your business?
Naturally, you wouldn’t adopt a data mart structure unless it helped your business processes. Fortunately, there’s an abundance of benefits on offer:
Only a quarter of workers feel confident in their data skills. Beyond data literacy difficulties, data access can also be a pain point for many organizations.
This is where data marts are particularly useful. Dividing data into department-specific pools cuts down on the time and skills required to find data in a bigger warehouse. Instead, your teams can access relevant data quickly and, in turn, gain the insights they need to make informed decisions.
When compared to enterprise data warehouses, creating a data mart is a fraction of the cost. This is because you only need to extract, transform and load a specific, smaller subset of data.
That said, if you choose to create a plethora of data marts, this could rack up your costs.
Again, let’s focus on the size of a data mart here. The volume and size of the data in a mart is smaller than in an enterprise warehouse. This means implementation and ongoing maintenance will take you less time.
As a data mart acts as a centralized deposit for your data, you can use it as your one source of truth.
This minimizes the likelihood of:
Data marts establish control at a departmental level. Your sales team is responsible for their sales data mart, much like your finance team is responsible for the financial data mart.
This reduces the likelihood of stepping on toes, or uncertainty around who owns what data.
More than this, it allows you to restrict who can access specific datasets.
Of course, it can’t all be sunshine and rainbows.
If you get too carried away and create data marts for every function or team, you might find the effort of maintaining the data outweighs the benefits. This is particularly the case if the data doesn’t contribute to actionable business insights.
On top of this, you can’t use data marts for business-wide data analysis. (It would be silly to try!) So consider them as an add-on to your enterprise data warehouse.
If you want to provide your departments with focused sets of valuable data, data marts might just be the ticket.
These smaller warehouses are less expensive to build and much easier to navigate. This allows your business users to access the data they need to identify trends and actionable business insights.
If you’d like to learn more about data architecture as a whole, we’d recommend reading through our enterprise data architecture guide.