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The right data, in the right place: understand the value of data marts in your data strategy
Imagine the scene: it's Monday, 9 a.m. The Sales team needs a pipeline , Finance wants to project cash flow, Marketing wants to cross-reference leads with conversions. All at the same time. And the IT team, of course, becomes the company's "data counter."
If you identify with this, you're not alone. 95% of organizations still struggle to organize and deliver data effectively to each department , Gartner report .
Ultimately, what's lacking isn't data, but rather a structure that simplifies access, divides information by area, and delivers speed without losing control. That's where the data mart makes all the difference. It's like an intelligent extension of the data warehouse : leaner, more specific, designed to deliver to each area what it needs—without overloading the IT team.
In this article, we'll explain why data marts are the right shortcut for teams that need to act quickly, how they work in practice, and what to consider before taking the next step. Because, ultimately, data that's delayed is data that's useless.
Enjoy your reading!
For many companies, the difficulty lies not in having data, but in transforming that data into clear answers for each area of the business.
This is where the data mart . The term "mart" comes from the English word " market ," meaning a segmented "data market" where each team finds only what it really needs to consume. In practice, it's a way to "slice" excess information and deliver ready-to-use blocks , without clutter or waste.
Essentially, a data mart functions as a specialized extension of a data warehouse . While the warehouse concentrates everything in one place, the data mart organizes smaller, specific blocks, ready to be used by Sales, Marketing, and Finance teams—all without queues, rework, or overloading IT. We can also draw an analogy to the structure known as the "medallion" of Bronze, Silver, and Gold, where the data mart is the "Gold."
Thus, each area gains more autonomy to generate reports, consult indicators, and make decisions with greater confidence, without having to compete for space with other strategic demands of the company.
But there isn't just one way to create a data mart . Next, we'll understand the main types and when it makes sense to use each one.
Types of data marts
In general, data marts can be structured in three main ways, depending on their level of integration with the rest of the data architecture :
- Dependent data mart : It is built from the data warehouse . All data comes from a single central source, ensuring consistency, governance, and standardization;
- Independent data mart : arises from specific operational sources, without necessarily going through the data warehouse . It is faster to implement, but requires extra attention to quality and integration;
- Hybrid : mixes the two formats. Combines data extracted from the data warehouse with information from external systems, when necessary. It is an interesting option for companies that already have a robust central database, but need flexibility.
Each format addresses a specific need , and understanding this difference is important for defining how a data mart can generate value in a practical way.
With that in mind, the question now is: how does the data mart actually bring order to the mess ? That's what comes next.
Having all the data stored in one place doesn't solve much if, in practice, the team is still stuck with time-consuming searches, incomplete reports, and IT bottlenecks . This is where the data mart comes in: it's not just a "mini database," but a structure that cuts, filters, and delivers only what each area really needs .
The data mart relies on three fundamental pillars that define how it organizes information in a clear and user-friendly way:
- Specialization by business area : the first pillar is division by theme or area. Sales, for example, doesn't want to navigate through accounts payable data; it wants pipelines , goals, and conversions readily available for consultation. Finance needs projections, costs, and real cash flow. And Marketing wants to cross-reference leads , funnels, and campaign results in a simple way, without depending on endless spreadsheets. This separation ensures that each team works focused, without wasting time searching through everything.
- Speed in data retrieval : with the information already organized, queries run more smoothly. The data arrives quickly, without overloading the IT team with repeated operational requests. It's like having several shortcuts, instead of a single congested road every time a new question arises;
- Optimized performance : the final pillar is technical balance. The data mart works with smaller blocks of information, which reduces the volume processed in the data warehouse . Thus, heavy reports don't cause a complete freeze, even during peak hours. For the technical team, this means less bottlenecking and more fluidity in the infrastructure.
With this well-structured foundation, the data mart moves beyond being "just another technical tool" and becomes a real part of everyday life. After all, organization is only the beginning: the real value appears when all of this connects with decision-makers —and that's what we explore next.
A data mart doesn't just stop at organizing tables: it 's what makes the information come out of the drawer and reach decision-makers with confidence.
In many companies, the daily routine is still marked by contradictory reports, dashboards , and spreadsheet versions where no one knows which one is the final version. Not surprisingly, 70% of professionals say they lose up to one day a week waiting for data , according to Forrester . A data mart shortens this path, but the gains go beyond that.
According to McKinsey , companies that segment data by area are up to 42% more likely to generate actionable insights , because separation makes the information reliable at the source, without rework every time a number changes.
This leads to advantages that go beyond the technical aspects:
- Live BI that keeps pace with the business : dashboards cease to be static and start running in real time, fed by clean data, without manual rework. This shortens the path between those who collect the data and those who need to present the results;
- Governance that works without hindrance : the data mart defines who accesses what, avoids information duplication, and provides traceability. This way, each area understands its limits, the IT team focuses on what matters, and the risk of miscommunication decreases.
- A solid foundation for AI and advanced analytics : segmenting data in an organized way is not only a performance gain, but it's what feeds predictive models without discrepancies. With reliable building blocks, the company tests, adjusts, and scales artificial intelligence (AI) sustainably.
- Scalability at a lower cost : According to the Boston Consulting Group (BCG) , a segmented architecture can reduce processing costs by up to 30% , freeing up budget for what really makes a difference — improving products, innovating, scaling data projects;
- Real autonomy, not just talk : each area can answer questions without waiting in line, create reports, test hypotheses, and make necessary adjustments more quickly. In this way, data ceases to be a bottleneck and becomes an input for evolving the business.
When each piece fits together, the data mart makes the data circulate smoothly and reliably , at the pace required for those who need to make quick decisions.
And it's precisely to make this work that every detail counts, from the capture process to the choice of tools. Let's understand where to begin this implementation?
Having an data mart isn't about pressing a button, but it also doesn't have to become an endless project. The secret is to respect some essential steps , in the right order, to avoid rework and ensure that the structure works from the start.
Here's what you can't miss:
- Map your data sources : it all starts with knowing where the information comes from: ERP, CRM, financial systems, spreadsheets, or external APIs? What is critical? Who owns it? How often is each database updated? Skipping this step opens the door to duplicate information, outdated data, and rework when creating reports;
- Organize thematic blocks and define governance : With the sources clear, it's time to structure how the data will be grouped. Which blocks serve each area? What is specific to Sales, Marketing , Finance? This is where governance comes in: who accesses, edits, or validates each set?
This division prevents the data mart from becoming a messy spreadsheet and ensures that each team has what it needs without overloading IT;
- Configure the ETL/ELT workflow : time to get everything moving. This is where ETL ( Extract , Transform , Load ) or ELT ( Extract , Load , Transform ) processes come in, which basically extract data from various sources, transform or standardize everything, and load it into the data mart , ready for use.
Tools like Fivetran , Airbyte , or DBT ( Data Build Tool ) automate this step with low-code and version control, freeing the team from repetitive manual tasks;
- Continuously validate, test, and adjust : no data mart is ever truly perfect. Creating periodic validation processes is essential: reviewing whether the data arrives clean, whether the blocks still answer the real questions of the departments, and whether new sources need to be integrated. This continuous adjustment prevents hidden bottlenecks and keeps everything relevant as the business evolves.
Following each step, the data mart does what it needs to: organizes blocks, ensures governance, automates workflow, and keeps everything aligned with the different areas. And for this structure to truly run, the choice of platform and BI tools completes the cycle . That's what we'll detail now, so keep reading!
Once everything is built and organized, the time comes to put that data into action . And that's where two fundamental layers come in:
- Cloud infrastructure , which ensures storage, processing, and scalability;
- And the BI ( Business Intelligence ) tools, which transform all of this into dashboards , reports, and visualizations, ready for decision-makers.
It is this combination of a robust database and accessible analysis that takes the data mart out of the back office and puts live information on the table for those who need the right data at the right time.
Next, we will discuss these topics in more detail.
Cloud platforms ( Snowflake , BigQuery , Redshift , Synapse )
Today, it's difficult for a data mart to survive outside the cloud . After all, it's like "fertile ground" where the data mart grows without physical limits. It's where data blocks are stored, processed, and ready to run heavy queries, even when demand skyrockets.
Platforms like Snowflake , Google BigQuery , Amazon Redshift , or Azure Synapse Analytics are the most popular choices today because they help businesses scale without investing in internal servers . With them, companies pay for actual usage, adjust processing according to demand, and integrate everything with ETL/ELT pipelines
Each one has its trump card:
- Snowflake : It is flexible in separating processing and storage, useful for those dealing with query spikes;
- BigQuery : works on demand; good for avoiding waste when usage is variable;
- Redshift and Synapse : They make life easier for those who already run services on AWS and/or Microsoft .
More important than the brand is knowing which platform makes sense for the volume of data, the flow of queries, and the level of security that the business needs today and in the future .
BI tools ( Power BI , Tableau , Looker , Metabase )
If the cloud is the terrain, BI is the showcase: it's where structured data becomes insight , reports, and practical answers in the hands of decision-makers.
Below, we list the most commonly used tools that translate blocks of data into dashboards and easy-to-explore analyses:
- Power BI Microsoft ecosystem and ready-to-use interactive reports;
- Tableau : strong in advanced visualizations and rich dashboards for exploring data intersections;
- Looker for BI : highlights integrated analytics in cloud-based data environments, with centralized governance;
- Metabase : an open-source for creating dashboards with a lower entry cost.
More than just displaying pretty numbers, a BI system well-connected to the data mart ensures reliable access and autonomy for each area to focus on what matters, while the IT team takes care of governance, performance, and architecture evolution.
With the right infrastructure, the data mart feeds the BI, and the data becomes practical answers, without hindering decision-makers. This is how each part adds up , from storage to analysis, and prepares the business to grow based on clear information. And to orchestrate all this with security, integration, and scale, Skyone comes in as a definitive partner in the end-to-end process.
Having an data mart integrated with BI ensures that each area has clear answers at the right time. But those who experience this in practice know that the challenge doesn't end with structuring it : it continues every day, with growing data, changing systems, and new sources emerging.
At Skyone , we help companies build, maintain, and evolve this flow without creating dependencies or rigid processes . In day-to-day operations, this means automating extraction, transforming data from different sources, organizing everything in the cloud with real scalability, and keeping governance alive, even when volume skyrockets.
It doesn't matter which cloud platform your team uses, nor which BI tool. What makes a difference for us is ensuring that everything "communicates" correctly , without hindering those who need answers. Because from there, the IT team can focus on what truly drives the strategy : evolving processes, maintaining security, and supporting areas with data ready to turn into action. At Skyone, the infrastructure for Metabase comes ready and delivered.
If you want to understand how to remove bottlenecks and make your operation leaner, contact us! Talk to a Skyone specialist and see, without obligation, how this works in practice, in your scenario, your way!
When each area has access to the right data, answers arrive at the pace the business demands—with greater accuracy, less wasted time, and more confidence to act . That's what a data mart offers: a clear structure, easy to evolve, and that keeps useful information readily available from start to finish.
Everything we've explored here shows that organizing data isn't just a technical step : it's a practical foundation for empowering teams, supporting strategic decisions, and opening up space for advanced analytics, AI, and true innovation .
If you found this content useful, keep discovering more ways to unlock the potential of your data! Explore our Skyone blog and find other articles about cloud computing, integration, architecture, and trends.
Before creating or using a data mart, it's normal to have questions about what it actually is, how it differs from other data structures, and whether it's worth investing in this approach.
Below, we've compiled direct answers to the most common questions to help you better understand if this solution makes sense in your context.
a data mart the same as a data warehouse ?
No. A data warehouse is the central repository where a company stores large volumes of data from different sources in a consolidated way. A data mart, , is like a specialized "slice" of that whole: a subset of data organized to serve a specific area or theme (for example, Sales, Marketing , or Finance).
In practice, the data warehouse stores everything, and the data mart separates, filters, and delivers what each team really needs, without having to consult the entire raw volume.
Who should use a data mart ?
Companies of all sizes can use data marts . However, it makes even more sense in organizations where different areas need to access specific data quickly, without always depending on IT to generate reports.
If the company has a considerable volume of data and wants to give more autonomy to Sales, Marketing, Finance, or Operations to work with clear breakdowns, the data mart is a practical structure to accelerate queries, reduce overload on the data warehouse , and better organize governance.
Is it safe to store sensitive data in a data mart ?
Yes, provided the architecture follows good data security and governance practices. A data mart can store sensitive information (such as financial data or sales metrics) as long as there are well-defined access layers, encryption, authentication controls, and constant updating of who can view each block.
In most cases, the data mart is part of a larger architecture (with the data warehouse
so compliance policies . This ensures that the right data reaches the right area, without risk of leakage or misuse.
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