What is a data warehouse?

A data warehouse is a home for data generated in other applications, like your back-end sales system or your marketing platform. If you’re a little hazy on what a data warehouse does, you’re not alone. Some marketers don’t have a lot of experience moving data in and out of a warehouse because their analytics or engineering co-workers handle it, and some business leaders might feel that this lies in the realm of data only. Others might have a basic understanding of the concept but not understand why they should invest in data warehousing solutions.

The value becomes clear when you look at how teams use data today. In the modern data stack, a data warehouse is much more than a storage system. It gives teams a central place to organize business and marketing data for reporting, analytics, and better decision-making. All aspects of business, including marketing, benefit from a data warehouse.

Discover how a data warehouse helps add vital ingredients to your marketing mix by learning:

• What is a data warehouse?
• How a data warehouse works.
• The key benefits of a data warehouse for marketers.
• How business intelligence tools rely on effective data warehouse solutions.

Data warehouse defined

A data warehouse is a system used for storing data. The data in a warehouse is used for reporting and analysis, making the warehouse a core business intelligence (BI) component. BI tools analyze data and provide insights, and the data warehouse stores and organizes the data that these tools need to create accurate reports and dashboards.

The basics of a data warehouse

But what is a data warehouse in simple terms? To break this down a bit, let's get into the kitchen. You want to cook a brilliant meal for someone. You need delicious, raw ingredients. But if you simply heap those raw ingredients into a pile, you don't have a meal. You have a mess.

Instead, you need to go to various storage units in the kitchen. Maybe you go to the pantry or the icebox. You select specific ingredients that you need for the task at hand. Those ingredients go into bowls, mixers or on the weighing scales. Finally, you're ready to start.

This is exactly how your data warehouse works. Instead of raw ingredients, you're working with raw data, semi-structured data or even highly structured data from different sources. The pantry might be a CRM (customer relationship management system) for marketers, holding juicy customer details and information on leads. The icebox might be interactions on social media.

Moving business data from various sources into the right data storage locations in a usable format means you can create meaningful reports, visualizations, and even predictions.

You can take the analogy further by considering more distant or detailed sources. As a home cook, the ingredients in your kitchen come from different places. You might get the bulk of your food from the local grocery store, but your salad greens and tomatoes may be from a farmer’s market. Storing those with your root veggies might mean they get bruised or spoiled.

Similarly, if your social media data is generalized and mixed together, you might miss details showing that you're getting far more interaction on TikTok than via Instagram reels. An effective data warehouse brings data from multiple sources, stores it, and allows BI tools to pick out insights that keep business leaders and stakeholders satisfied.

What is a data warehouse vs. a database?

Many marketers might think that it's pretty much the same when it comes to a data warehouse vs. a database.

That's like saying hamburger is the same as filet mignon because they’re both beef.

Non-relational and relational database systems store data in a single place. However, they usually only store data from one source or multiple sources of the same type of data. Databases are transactional systems designed for updating and delivering operational data, but not necessarily providing insights.

The "data warehouse database" debate isn't one versus the other. They have different roles, and, indeed, your data warehouse system should utilize historical data from existing databases.

A data warehousing system stores data from multiple sources and often has built-in analytics capabilities. A data warehouse is designed to be highly scalable for data storage and processing. Because it carries data on various aspects of an organization, it can drive improvements in business processes and decision-making.

What is a data warehouse vs. a data lake vs. a data lakehouse

Data warehouses, data lakes and data lakehouses all store data, but they serve different purposes. The difference comes down to how the data is stored, organized and used.

Data lakes are useful when teams want to keep raw data in flexible formats for data science or exploratory analysis. Lakehouses add more structure to that flexible storage so teams can support BI and advanced analytics. Data warehouses are built for structured data that teams can use for reporting and analysis.

For marketing teams, a data warehouse is often the most practical starting point because campaign reporting depends on consistent historical data from many sources, including ad platforms, CRM systems and web analytics tools. This warehouse architecture makes the data easier to query, compare and use across teams.

Data warehouse architecture: How a data warehouse works

As a home cook, do you use the jar of marinara sauce or make it from scratch? Either way, you get a delicious dish. But the second option gives you important insights into why it's so delicious. With that in mind, let's give you a taste of exactly how enterprise data warehouses work.

Components of a typical data warehouse architecture include:

• Multiple data sources may be corporate systems, files, or off-site sources such as SaaS tools, social media platforms, or blogs and websites.
• Staging area — many data integration solutions utilize a separate staging area between the data sources and the warehouse for cleansing and transforming data.
• Storage area — some data warehouses might have an area for raw, unstructured data, but this is usually stored in data lakes rather than a data warehouse. Most data warehouses store structured data and metadata, allowing BI tools or business analysts/engineers to access and utilize it.
• Data mining and BI tools — these combine analysis and reporting capabilities.

The staging area is often an ETL tool or suite of tools. ETL stands for "Extract, Transform, Load." As the name suggests, these tools extract data, ensure it's in a readable format, and load data into the warehouse. Imagine you just had someone undo the lid on the jar, check the aroma of the sauce, and then pour it into your pasta. It's a way of making sure the data warehouse only carries data it can handle or present to data analysts/BI tools in a format that makes sense.

Another method of transformation for an enterprise data warehouse is ELT. Unsurprisingly, this stands for "Extract, Load, Transform." Data is loaded into your central repository before transformation. This utilizes the resources of the data warehouse itself rather than third-party tools to perform data cleansing. While this can be more costly for businesses, it's also often faster, providing actionable insights in almost real-time.

Your modern data warehouse and related analytics tools utilize artificial intelligence and machine learning. These evolving technologies recognize patterns and quickly deliver insights. AI and machine learning work with on-premise or cloud data warehouses to create forecasts, prompting marketers to make campaign changes in line with predicted shifts in markets. All these tools reduce the need for businesses to invest in data science specialists.

Machine learning can also assess transactional data and remove duplicated entries from data mart or databases. This decreases data redundancy and improves data storage efficiency.

Why are data warehouses valuable for marketers?

As of 2024, the data warehousing market is worth $10.01 billion and is set to rise to a value of $16.94 billion by 2029. It’s crucial for businesses to invest in an effective operational data storage system in order to remain competitive.

Data warehouses are valuable in your data stack if you want fully integrated data. Quick note: Your data stack is the collection of tech-based tools and solutions you use to store, manage, and analyze data. Think of it as the knife rack in the kitchen. Without it, you can see the ingredients but can't do anything useful with them.

Most cloud data warehouses empower companies to consolidate all their current and historical data into a single source of truth. Often, when teams or companies are trying to answer critical questions about their business performance, like "What's causing leads to increase in March but not sustain similar volumes throughout the year?" they need to be looking at the data holistically.

Holistically means examining the impact across the whole of the organization. If your leads dropped in April and you only examine the data from your CRM, you might miss the fact that your insightful SEO blogs had fewer hits that month. When you finally check on that, it turns out the link to the blogs shared via an email campaign was broken. Customers wanted to engage with your content, but a simple technical fault prevented it.

It's like cracking eggs into a pan with a hole in the bottom. That's never going to become an omelet.

Data warehousing use, combined with the right data analytics solutions, helps you patch those holes before they cause problems. This data management approach connects marketing, sales and customer data into one single destination. Marketers can hone campaigns, understand audience segmentation, and respond to client feedback more effectively than they can when they rely solely on operational databases.

Common data warehouse use cases

When ad data, CRM data, web analytics and sales data are connected in one environment, marketing teams can move from platform-level reporting to a fuller view of marketing performance.

Cross-channel campaign reporting

Campaign performance is much harder to understand and compare when you’re constantly switching between separate ad platforms. A data warehouse connects data from multiple marketing channels into one place so marketers can easily compare spend, clicks, impressions, conversions and revenue across channels.

Say you’re running a product launch across Google Ads, Meta, LinkedIn and TikTok. In the data warehouse, you can compare results side-by-side: TikTok is driving low-cost traffic, Meta is generating the most form fills and Google Ads is capturing high-intent search demand.

Now your team understands how each channel contributes to the campaign and can plan future spend more effectively.

Connecting ad data with CRM and sales data

Ad platform data can show clicks, conversions and cost, but it does not always show what happens after someone becomes a lead. A data warehouse helps connect campaign performance with CRM and sales data, including leads, opportunities, pipeline and revenue.

Instead of looking only at vanity metrics or platform-reported conversions, teams can better understand which campaigns are actually driving business outcomes. For example, one campaign may generate a high number of form fills, but CRM data may show that very few of those leads become qualified opportunities.

Teams can then adjust targeting, refine the offer or shift budget toward campaigns that generate stronger downstream results.

Web analytics and customer journey analysis

Web analytics data shows what people do on your website, but it’s more useful when it’s connected with campaign, CRM and sales data. With a data warehouse, teams can connect campaign touchpoints with what users do next on the website and later in the sales process.

An email campaign may drive strong traffic to a landing page, but the data may show that mobile users abandon the form at a much higher rate. This helps teams identify a specific drop-off point and investigate problems such as page speed, form design or mobile layout.

Historical performance tracking

Marketing teams need historical data to understand whether performance is actually improving or just changing in the short term. A data warehouse helps teams look beyond short reporting windows by storing historical campaign, channel and customer data in one place. Marketers get a clearer benchmark for year-over-year analysis, budgeting and forecasting.

For instance, before increasing your Q4 budget, your team can review how different channels performed in previous holiday seasons. Maybe paid social performs better earlier in the season, while paid search captures higher-intent buyers closer to major shopping dates.

So, you allocate more budget to paid social earlier in the season to build demand and increase paid search spend closer to the holidays.

BI dashboards and marketing intelligence

Once data is connected in the warehouse, teams can use it across BI dashboards and marketing intelligence platforms. That means marketing, finance and leadership can work from a single source of truth.

Whether marketing needs a channel performance view, finance needs a budget efficiency report or leadership needs an executive revenue summary, the warehouse helps ensure reports draw from the same underlying data, so teams spend less time debating which numbers are right.

Choosing the right marketing data warehouse for current and historical data storage

When choosing a data warehouse, marketing teams should consider their existing cloud environment, data volume, internal data skills and reporting needs.

Here’s a quick comparison of popular cloud data warehouses:

Data quality and effective data integration come from investing in the right data warehouse and analytics solutions. For data warehousing, businesses have two primary choices: A cloud data warehouse or an on-premises data warehouse.

On-premises data warehouses utilize an on-site data center. This is like cooking in your own kitchen. But remember, the more adventurous you get with your culinary skills, the more space you need for ingredients, cookbooks, and tools. Likewise, data stored in on-site data warehouses has a finite space, which can limit scalability.

Cloud data warehouses from third-party providers offer managed solutions that free up business space and resources. It's like ordering out for a gourmet meal. It tastes just as good, and you get to keep your kitchen clean. However, can you be sure that a restaurant employee didn't sneeze on your lasagna?

In a similar way, when sourcing any cloud data warehouse vendor, businesses must assess their security credentials. Third parties have to offer you the same security standards you offer your clients and also meet any industry-specific data protection and security requirements your organization is held to.

Your marketing team needs a modern data stack that allows them to quickly see and understand business data trends that drive current and upcoming campaigns. Not all businesses can afford to invest in full-time data scientists, which is why sourcing a data warehouse that can become a central repository for structured and semi-structured data is essential.

Online analytical processing solutions are more than just the seasoning on the top of the data warehouse omelet. For marketers, data warehouses and data analytics tools can:

• Create a single source of truth for all marketing insights
• Help marketers connect to a data lake or data mart — data marts being business-area-specific data storage solutions
• Reduce time-to-insight
• Perform a truly holistic analysis of how marketing efforts are impacting the overall business
• Create analysis and predictive models to optimize ongoing marketing efforts
• Store all historical data and optimize data security
• Reduce reliance on multiple third parties for data access and ownership

This last point is critical. If you go to the fridge for milk but the carton has someone else's name on it, can you really use it with your eggs? In a similar way, if you need data from a particular external platform and you don't have effective data storage and integration tools, you could struggle to transfer that data from where it's held to your secure ownership.

How Funnel supports your marketing data warehouse

Better marketing analysis starts before the data reaches the warehouse.

Funnel acts as the marketing data layer before the warehouse, helping teams collect, standardize and prepare marketing data before sending it to warehouses like BigQuery, Snowflake or Redshift.

Funnel’s Data Hub turns messy, fragmented marketing data into consistent data that is ready to use. Teams can confidently compare cost, clicks, impressions and conversions across channels, knowing the data underneath is consistent. Instead of spending time on API maintenance, field cleanup and report preparation, marketing teams can focus on using the warehouse for BI dashboards, measurement, forecasting and long-term performance analysis.

Data warehouse management for marketers

Previously, with traditional data warehouses, marketers would have to either learn how to make their own SQL (structured query language) queries or work with data scientists or data mining specialists to get the most out of their data. Today, marketers have access to numerous tools designed for business users to get the most out of the data they gather from operational systems, relational databases, and other sources.

Data analysis, transformation of raw data, and transaction processing should all occur via effective, trusted software. Access to transactional databases, data lakes, and other repositories of raw data should be managed simply via intuitive interfaces and support from experienced providers. When marketers don't have to worry about the exact data model their storage system follows, they can focus on utilizing their data warehouse for campaign-enhancing insights and business growth.

It's important to note that data warehouse models are evolving all the time. Marketers currently using so-called monolithic data warehouses like Snowflake and BigQuery may find that the predicted rise of real-time data warehousing solutions becomes relevant. A traditional data warehouse model could look very different in just a few years from what marketers use now.

Marketers and business leaders that invest in future-proof, AI and machine learning-powered tools for storing data and data management are likely to bring more flavor to the table than those that don't.

Want to learn more about why a data warehouse is so valuable for marketers and which data warehouse solution is best for you? Read our very own Data Engineer, Ishan Shekhar's article on Marketing Data Warehouses.

FAQs

What is a data warehouse in simple terms?

A data warehouse is a data management system that stores data from different systems in one place. It helps teams organize current and historical data so they can build reports, compare performance and analyze business results without switching between separate tools.

Do I still need a data warehouse if I already have a database?

Yes. If you need to analyze data across multiple sources or look at long-term performance trends, you’ll still need a data warehouse.

Databases store and process real-time transactional data for daily operations, while data warehouses aggregate historical data for BI, reporting and analytics. In other words, databases help run the business now, while data warehouses help you analyze it over time.

Do I need a data warehouse for marketing?

You may not need one at the start. But as marketing data spreads across more platforms, a data warehouse gives teams a stronger foundation for analysis.

It connects campaign activity with leads, revenue, customer behavior and long-term performance, so marketing teams can make more informed business decisions.

Is a cloud data warehouse worth the cost?

A cloud data warehouse is worth the cost when it helps your team save time, improve reporting accuracy and make better decisions. The cost depends on data volume, query usage, storage needs, number of users and how much engineering support is needed.

Smaller teams with only a few data sources may not need one right away. But as reporting becomes more complex, a warehouse can provide a stronger foundation for scalable reporting, historical analysis, forecasting and marketing intelligence.