What is a data mart?

Data is everywhere. Data is everything. From your cell phone to your computer by way of your microwave, every single bit of data is being tracked. How to get your bearings? How to analyze your data? 
Don’t panic! It’s all taken charge of by data marts… (well, maybe not the data emerging from your microwave).

A data mart is a data warehouse but not just any. It’s a subset of a data warehouse that can be used for business purposes. How can it help your business? How to use it wisely? Why use it? Let’s dwell on data mart today and discover it through this article. 

You said data mart, right?

Data mart, they said? It’s a sort of data warehouse that focuses on a specific business area. It contains summarized and processed data, ready to be queried easily with a short delay. 

A data mart helps you focus on a single topic or single business area of your organization. It means that if a single business usually has only one data warehouse, it could need several data marts corresponding to various departments. You can’t load more than 100 GB in a data mart, which can be insufficient if you want to analyze large amounts of data.
For instance, marketing teams often favor data marts because it allows them to focus on their analysis on a specific business issue without requiring huge amounts of data.

Nowadays, more than 62% of companies agree that self-service business intelligence is essential. Why not start now with a tool that your whole team can handle? 

Data mart vs data warehouse

If you are not already familiar with data warehouses, you’d better read this article first.

To make a quick summary about data warehouses, here are some specifications: 

• It stores data, formats it and processes it
• It helps your teams take global data-driven decisions 
• It is essential to process huge amounts of data.

It’s slightly different from data marts. So what platform should you use to make data-driven decisions? 

Let’s make things clear: both can help you build data-driven decisions, but depending on the fields of those decisions, you will prefer to work with one or the other. 

• Data marts are prefered to data warehouses whenever you want to make operational decisions or make decisions that only involve a specific department of your business activity (marketing department, sales…)
  
  
• Data warehouses are prefered to data marts whenever you have to make decisions that involve your business as a whole (global development strategy, internationalization plan…)

But the differences between the two systems don’t stop there. As a direct consequence of the specificities of the two platforms, data marts are way easier to use and implement (less complex and a quicker implementation process) because the amounts of data that go through the system are limited.

See below a diagram that summarizes the differences between the two systems.

Source: https://panoply.io/data-warehouse-guide/data-mart-vs-data-warehouse/ 

Data mart: how does it work?

Process of implementation

How to implement a data mart? 
Usually, the process unfolds as follow: 

1. Understanding which issues need to be assessed, the departments involved, the technical solutions that can be implemented
   
   
2. Identifying the data sources that you can load on a data mart to make decisions 
   
   
3. Picking the data subsets that rely on that specific issue
   
   
4. Design of the system. 

We have not gone into much detail to ease the understanding of the process, but for further explanations or any other questions regarding data marts, don’t hesitate to call on our teams. 

3 types of data marts: discover their specificities

There are 3 types of data marts: dependent, independent or hybrid data marts. 

• Dependent data marts: it works on top of a central data warehouse. What does that mean? The data that you’ll load on your data mart will directly come from your data warehouse. It will be a subset of the sets of data you’ve collected on your central data warehouse.
  
  
• Independent data marts: those data marts don’t rely on a data warehouse. The sets of data loaded come from separate entities.
  
  
• Hybrid data marts: they are both dependent and independent at the same time. Why should you make things easy when they can be complicated? Well, hybrid data marts are flexible as they allow you to load independent external sets of data and subsets of your data warehouse.Most of the time, it’s favored when you create new data marts and need to implement a system quickly

Why use data mart? 

Reporting, dashboards, data integration, data warehousing and data preparation are some of the most important technologies to access to BI. But how is it that 74% of employees report overwhelmed when working with data? Can’t we help them access easy-to-use data tools?

3 benefits of using data mart

Smaller than data warehouses but also quicker to implement, data marts are efficient solutions to many issues as they: 

1. Allow quick insights because they are department-centric, don’t load huge amounts of data and are practical in their way of supporting decision making.
   
   
2. Are easier to implement. Large amounts of data rhyme with more significant implementation time, but it’s not the case with data marts! You only need a small set of data to start analyzing your department performance.
   
   
3. Are cost-efficient. With a smaller and more specific scope, you spend less money than implementing a data warehouse and can see the direct impact on your departments. 

3 disadvantages of using data mart

1. Because a company can have as many data marts as its number of departments, they can become quite complex to manage. 
   
   
2. If you use different types of data marts, you may not come to the same conclusions.
   
   
3. They are not repositories for all firm data: depending on your objectives, they can be insufficient. 

A data mart is a subset of a data warehouse that helps you build strong data-driven decisions specific to a department of your company. While some businesses might find them insufficient for central decision-making, they allow your teams to make practical and operational decisions, a true timesaver for those who want to implement it.
If you’re lost between data mart, data lake, data warehouse and just, data. Let’s take stock of the situation together.