What is a Data Mart?
Many businesses nowadays use a data mart to organize their data warehouse and make relevant data easily available to specific departments. Data marts are dedicated to specific functions and allow management to quickly get an in-depth understanding of what is happening in a focused area of their business.
What is a data mart?
A data mart is a segment of a data warehouse focused on a particular topic, such as a product or a department. For example, if you’re in charge of inventory for a big-box megastore, you might have a data mart focused on each of your important verticals so you can manage them independently. If you run a university, your data mart may be focused on archeology or art history.
Data marts make data available to a group of users who aren’t necessarily data professionals themselves but need to make decisions based on the data that's covered by the mart. For example, with a data mart that's focused on a particular product line at an ecommerce store, the users could be inventory managers who want to keep tabs on how different products are moving without pestering their data team.
You could also have a data mart that's focused on specific departments in your business. For example, your data mart could be related to sales or marketing. It's important however, to not create a situation where you have data silos and your data becomes so segmented that you miss the ways in which things that happen in one department may be affecting the others.
For example, suppose you have a data mart related to advertising and a data mart related to sales. You may analyze data from the advertising data mart in order to answer questions such as when do consumers view your ads, what ad formats do they like the most and so on. On the other hand, your analysis of sales data may show a spike in sales in a particular month.
If the data from both marts isn't merged to answer specific queries, you might not realize that a particular ad format has had a positive impact on sales to a particular group. In fact, this ad format may be most effective after a special event. You'll need to design your data marts carefully so that they allow you to gain insights without missing out on important connections.
Differences between data marts and data warehouses
The primary difference between a data mart and a data warehouse is that of size. A data mart is a subset of a data warehouse, so it is usually a lot smaller. In addition, a data mart is more focused than a data warehouse, since a data warehouse takes in information on all aspects of the enterprise, while a data mart is focused on just one area.
Data marts are typically limited in terms of the number of sources that they have. A data warehouse takes in data from all over the place and gives you a complete picture of what is happening with all aspects of your business, whether online or offline.
Since data marts focus on just one aspect of the enterprise, they're limited in scope. You can't run a query on the data in a mart and use that to gain a full understanding of what's happening in the whole business. For example, if a data mart only has data on a store's Texas branch, you can't use it to draw reliable conclusions about the entire business.
Benefits of data marts
Data marts help to improve team efficiency by giving team members a concentrated source of data on specific areas. For example, if a company sells all types of paints all across the country, a data mart could concentrate all the data related to sales particular kinds of paint to make .
When team members want to answer questions that will help them to market that type of paint better, such as the season in which most sales take place, the size of the households that purchase that type of paint and so on, they can easily find it with a data mart.
With a data warehouse, the same process takes more time. It makes team members more productive when they can find what they need, without having to do queries that utilize computing resources searching though unnecessary data.
Since they use less computing resources, data marts reduce costs as well. They can also lead to faster responses to competitive threats in any sector since you have faster access to important data. They concentrate information so well that an organization can immediately see what it needs adjust about a particular product or service in order to make it a better fit for your market.
Types of data marts
Data marts can be categorized as independent, dependent or hybrid. Dependent data marts have a more direct relationship with their data warehouse and are created directly from the enterprise's data warehouse. The data in the data mart is extracted from all the general data in the data warehouse when analysts need it.
Independent data marts are standalone systems and they are not usually crafted from a data warehouse. Data comes from internal or external sources and is processed, then sent to the data mart's repository where it is stored until it is needed for analytics. While independent data marts aren't hard to develop, as the business expands, it can be hard to manage a lot of them.
Hybrids contain elements of both independent and dependent data marts, so they combine data from existing data warehouses and external sources. This offers benefits such as speed and is helpful in many situations. For example, if a new product is added to the business, the ad hoc integration that's facilitated by hybrid data marts is ideal.
How to structure a data mart
The structure of a data mart will depend on the requirements associated with it. For example, if you run a large organization with lots of clients, your client information may be used by several departments. This means that client details would be delivered to each one of your departments from the same source.
Data mart structures are called star joins and they are multi-dimensional. Star joins are formed by using fact and dimension tables. For example, facts include end of month tables. So typically, star joins support large amounts of data.
On a star join, the fact table would be in the center. This would be surrounded by dimension tables, such as store dimensions, which give a store's name, address, and so on. Granular data from the data warehouse would be likely to be used as the source for the data mart.
Who needs a data mart?
All large organizations that need to monitor what's happening in every aspect of the structure need data marts. If an organization wants to be able to respond quickly to opportunities and threats in its environment, it will need a data mart.
If a business is fairly small, it won't need a data mart. For example, a very small shop that only operates from one location and may not have a presence online, may not need a data mart. However, a store that also has a relatively small location but does a lot of sales online, may need a data mart.
Similarly, charitable organizations, community groups, and schools that serve a lot of people and are constantly gathering data, may also need data marts. Outreach groups that are national in scope but work with a large number of people in different communities could have data marts that are focused on identifying their clients that are most in need.
Data marts allow all of these organizations to make the best decisions about issues that affect them. If an organization seems relatively small now but has a large influx of data, it will need data marts to help improve its response time.
Without data marts, such organizations may miss out on opportunities as they struggle to deal with all the data that's coming at them from different directions in a warehouse. For some organizations, it may be better to have a data mart for analysis than a data warehouse because data marts are cheaper, require less resources, and are easier to implement.
Businesses of all sizes are hampered by a lack of business intelligence. Some review what's happening in the business on a monthly basis and that may not be as agile a response as they need in order to excel in their sector. A data mart introduces more flexibility with their processes than other methods of storing data and allows them to act more quickly.
Data marts in the cloud
While all data marts are not currently in the cloud, many are making that move. Having a data mart in the cloud allows it to be more agile and improves accessibility. Analysts can access the data from any location and also allow their organization to operate at a lower price point.
Cost effectiveness comes from being able to scale data storage by using reliable providers such as AWS. Organizations will only pay for what they use, while acquiring elastic computational resources.
Mozart Data makes it incredibly easy to set up and manage data marts for your company. The advantage of having all your data in one place just builds upon the power of using data marts to manage your business. They’re a critical part of a modern data stack and an invaluable tool for making data accessible to everyone in your organization, not just data engineers.
Bridging the gap between data and non-technical decision makers is the critical next step in building and running a modern business. And managing data marts through Mozart is the streamlined, user-friendly way to make that happen. Schedule a demo today to see how.