In the digital age, data isn’t just a byproduct of business activities. It’s a core asset that drives decision-making, innovation, and growth. Companies of all sizes use data to gain insights, optimize operations, and stay competitive.
However, no one wants to believe in words in the modern world, so let’s review the statistics.
- According to a study by McKinsey, companies that embrace data-driven decision-making are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable.
- The amount of data generated worldwide is staggering. IDC predicts that by 2025, the global datasphere will grow to 175 zettabytes, up from 33 zettabytes in 2018. This explosion of data means that businesses need effective ways to manage, store, and analyze their data.
- Businesses that effectively use big data analytics can see significant financial benefits. Forrester says that data-driven companies are growing at an average of over 30% annually and are expected to take $1.8 trillion annually from their less data-driven peers by 2021.
Looking impressive, isn’t it? Now, let’s explore two essential tools for managing and using volumes of this data: Data Marts and Data Warehouses.
- A data warehouse is a large, centralized repository that stores data from various sources across an organization. It handles vast amounts of data and supports complex queries and analysis. Data warehouses are typically used for enterprise-wide data consolidation and reporting.
- A data mart is a smaller, more focused version of a data warehouse that serves the specific needs of a particular department or business unit, such as marketing, sales, or finance. Data marts typically contain a subset of data from the organization’s data warehouse, tailored to meet the needs of that specific group.
This article will help users catch the difference between these two terms, consider each repository’s core features and main benefits, and describe common user scenarios in both cases.
Table of contents
- What is a Data Warehouse
- What is a Data Mart
- Key Differences
- When to Use DWH or Data Mart
- Role of Data Integration Tools
- Conclusion
What is a Data Warehouse
Now we know what a data warehouse is, so let’s see how it works. Imagine it as the brain of a company’s data operations, where all the different pieces of data from various parts of the business come together to provide a unified view.
For instance, Mrs. Potter owns a growing chain of retail stores called “Potter’s Goods.” Mrs. Potter’s business is booming, and she’s got data pouring in from all directions:
- Sales data from the CRM systems.
- Customer data from the loyalty program.
- Inventory data from the warehouses.
- Marketing data from the online campaigns and social media.
All this data is scattered across different systems, making it hard for Mrs. Potter to get a complete view of her business. To tackle this, Mrs. Potter implements a data warehouse (DWH), using its core features to reap significant benefits while addressing everyday challenges.
Core Features of the Data Warehouse
- Centralized Storage. The data warehouse combines sales figures, customer information, inventory levels, and marketing campaign results into one centralized location.
- Data Integration. The data warehouse stores the data integrated from multiple sources and keeps it consistent and accessible for analysis. So, sales data from different stores, even using various POS systems, is now harmonized.
- Historical Data Management. The data warehouse stores historical data. It allows tracking changes and trend analysis and provides LTV, ROI, channel attribution, etc. You can find the necessary data in the DWH when comparing this quarter’s sales to the same quarter last year or analyzing long-term customer behavior.
- Optimized for Complex Queries. DWHs provide appropriate data for complex queries that generate detailed reports and analytics.
Challenges and How the Data Warehouse Solves Them
The table below shows the typical challenges and how DWHs may solve them.
| Criterion | Problem | Solution |
|---|---|---|
| Data Silos | Before implementing the data warehouse, Mrs. Potter struggled with data being trapped in different systems, making it difficult to get a holistic view of her business. | By integrating data from all the systems into one centralized repository, the data warehouse eliminates silos, giving Mrs. Potter a unified view of her operations. |
| Data Quality Issues | With data coming from various sources, inconsistencies in formatting and accuracy were typical, leading to unreliable reports. | The DWH standardizes and cleanses data during the integration process, ensuring that all data is consistent and accurate, improving the quality of its insights. |
| Complex Reporting Needs | Mrs. Potter needed to perform complex analyses, such as identifying seasonal trends or customer preferences, which was complex with her previous systems. | The data warehouse’s ability to handle complex queries and large datasets enables Mrs. Potter to generate detailed, insightful reports that inform her business strategies. |
Benefits
- Enhanced Decision-Making. With all the data integrated and centralized, Mrs. Potter can easily generate reports that provide actionable insights. It helps to make smarter decisions about inventory, marketing, and sales strategies.
- Time Efficiency. Instead of spending hours gathering and reconciling data from various sources, Mrs. Potter can now access all the information she needs from the data warehouse, saving time and reducing errors.
- Scalability. As Mrs. Potter’s business grows, the data warehouse scales with it, accommodating the increasing volumes of data. She can add new stores and systems without worrying about data management complexity.
What is a Data Mart
Unlike a data warehouse, a data mart focuses on a subset of data to make it easier and faster to access and analyze.
For example, Mrs. Potter implements a data mart specifically for her inventory management team. She’s noticed that her inventory managers often struggle with getting the precise data they need from the broader company systems geared more toward sales and customer analytics.
Core Features of the Data Mart
- Focused Scope. Data marts handle specific data relevant to a business area, like sales, marketing, or inventory management.
- Tailored Data Structure. The data is organized and structured to meet the specific needs of the department it serves, allowing for faster and more efficient queries.
- Ease of Access. Because a data mart contains a smaller, more focused dataset, it’s quicker and easier to retrieve and analyze data.
Challenges and How the Data Mart Solves Them
The table below shows the common challenges and how data marts solve them.
| Criterion | Problem | Solution |
|---|---|---|
| Overloaded Systems | Mrs. Potter’s previous system tried to serve all departments with a single, centralized database, leading to slow performance and difficulty accessing relevant data. | The data mart lightens the load by focusing only on inventory data, improving system performance, and ensuring that the inventory team has quick access to the needed information. |
| Lack of Specialized Reports | The generalized reports from the company’s main system didn’t provide the detailed insights Mrs. Potter’s inventory team needed to manage stock effectively. | With a data mart, the team can generate specialized reports tailored to their specific needs, like stock turnover rates or supplier performance metrics. |
| Complex Data Management | Managing a large, centralized system was complex and time-consuming, often leading to data retrieval and reporting delays. | The data mart simplifies data management by focusing only on the relevant data for inventory, making it easier to maintain and use. |
Benefits
- Improved Efficiency. With a data mart, users like Mrs. Potter’s team no longer have to sift through irrelevant data. This streamlined access leads to faster decision-making and more efficient operations.
- Targeted Insights. The data mart provides the users with the necessary information without the noise of unrelated data. It helps the team spot trends, manage stock levels effectively, and reduce waste.
- Cost-Effective Solution. Compared to a full-scale data warehouse, the data mart is more cost-effective for meeting the specific needs of Mrs. Potter’s or someone else’s businesses. It provides all the benefits of a larger system but on a smaller, more manageable scale.
Key Differences
While data warehouses and marts play vital roles in data management, they’re about different organizational scopes and needs. The table below shows the differences that help companies choose the right solution based on their data requirements.
Let’s highlight the benefits of using DWHs and data marts to help users choose the right approach depending on their organization’s requirements and resources.
| Criteria | Data Warehouse (DWH) | Data Mart |
|---|---|---|
| Complex Query and Analytics Support | Optimized for running complex queries and large-scale analytics across the entire organization. | Supports simpler queries and analytics specific to the department’s data needs. |
| Historical Data Storage | Stores large volumes of historical data for long-term analysis and reporting. | Typically focuses on current and relevant data for the department, with limited historical data. |
| Cost | Higher cost due to extensive infrastructure, storage, and maintenance requirements. | Lower cost, more budget-friendly for specific departmental needs. |
| Decision-Making | Enables comprehensive, organization-wide decision-making with insights from all departments. | Speeds up decision-making within departments by providing quick access to relevant data. |
When to Use DWH or Data Mart
Let’s return to Mrs. Potter and her growing chain of boutique stores. As her business expands, she’s collecting data from sales transactions, customer loyalty programs, inventory systems, and even her online marketing campaigns. At some point, Mrs. Potter realizes she needs a comprehensive view of her business to stay ahead of the competition and make informed decisions.
When to Use a Data Warehouse
- Large-Scale Insights. Mrs. Potter wants to consolidate data from all stores and online platforms into one centralized location to get a complete picture of the business’s overall performance rather than just looking at individual stores or departments.
- Complex Analytics. This business owner is interested in identifying long-term trends (based on historical data), such as how sales have grown yearly, which customer segments are most profitable, and which products are best-sellers across all her stores. The data warehouse allows complex queries that involve large datasets to run.
When a Data Mart is the Perfect Solution
- Focused Insights. The users only need data related to customer purchases, marketing campaign results, and customer segmentation. A data mart allows them to focus on just the information they need without being overwhelmed by irrelevant data.
- Quick Access to Specific Data. Mrs. Potter’s marketing team needs to make quick decisions about upcoming campaigns, so they need fast access to relevant data. A data mart provides this by streamlining the data they work with.
- Cost-Effective Solution. Setting up a data warehouse for the marketing department alone would be overkill. A data mart offers a more affordable and easier-to-maintain solution for their specific needs.
Role of Data Integration Tools
Data integration tools make users’ lives easier when working with data warehouses and data marts.
For example, Mrs. Potter runs a business with tons of data from sales, marketing, inventory, and customer feedback. She relies on a data warehouse or data mart to make sense of all this data and use it effectively. But all that data needs to be gathered, cleaned, transformed, and loaded into these systems, and that’s where data integration tools like Skyvia, Talend, Fivetran, etc., come into play.
Bridging the Gap Between Systems
Such integration tools act as the bridge between all data sources and DWH or data mart. Whether Mrs. Potter pulls data from cloud applications like Salesforce or databases like MySQL, these tools handle the heavy lifting. They extract the data, transform it to fit the needed format, and load it seamlessly into the data warehouse or data mart. So, Mrs. Potter doesn’t have to worry about manually moving data around or dealing with inconsistencies. Tools like Skyvia automates the whole process.
Ensuring Data Accuracy and Consistency
One of the biggest challenges in data management is ensuring that data is accurate and consistent. If Mrs. Potter’s sales data from her online store doesn’t match the data from her physical stores, it can lead to incorrect analysis and poor decision-making. Data integration tools help standardize and clean the data before it reaches the DWH or data mart. So, Mrs. Potter’s team runs reports or analyzes trends; they’re working with reliable data that gives a true picture of the business.
Supporting Collaboration Across Teams
Multiple departments, such as sales, marketing, finance, and operations, often use data warehouses within the company. Data integration tools support this collaboration by ensuring that data from all sources is integrated smoothly into the warehouse. So, everyone in the organization works from the same data set, leading to more consistent and aligned decision-making.
For instance, if Mrs. Potter’s marketing team uses a data mart focused on customer data, Skyvia can integrate data from various marketing tools, CRM systems, and customer feedback forms, ensuring the marketing data mart is up-to-date and accurate. Meanwhile, the finance team can rely on the data warehouse, where Skyvia aggregates financial data from various systems to provide a complete financial overview.
Flexibility and Scalability
As Mrs. Potter’s business grows, so does the data she needs to manage. Data integration tools like Skyvia offer the flexibility to scale with the company, integrating new data sources or expanding the capacity of existing pipelines as needed. So, whether she’s opening new stores or launching new marketing campaigns, the data integration processes remain efficient and effective.
Conclusion
To summarize, a data warehouse is the go-to solution for businesses that need a broad, comprehensive view of their entire business. It is capable of handling complex analytics and large datasets. On the other hand, a data mart is ideal for more focused, department-specific needs, offering quick access to targeted data without the complexity and cost of a full data warehouse. Whether you’re like Mrs. Potter, managing a growing business, or handling specialized departmental tasks, choosing the right approach will be helpful to making data-driven decisions that propel your business forward.
