Marketing Data Warehouse: A Simple Step-By-Step Guide

In this article, we’ll answer:

1. What is a marketing data warehouse? 
2. Why does every business need a marketing data warehouse? 
3. How does a marketing data warehouse differ from other data storage?
4. How to set up your marketing data warehouse architecture?
5. How to set up a functioning marketing data warehouse with Keboola? 

What is a marketing data warehouse? 

A marketing data warehouse (DWH) is a data storage solution that allows you to store and consolidate marketing data across all your various data sources into a single central location. 

The various sources include: 

• Marketing channels: LinkedIn ads, Google Ads
• Web analytics solutions: Google Analytics, Hotjar
• CRM tools: Salesforce, Hubspot
• Social media data: TikTok, Instagram

Why does every business need a marketing data warehouse? 

The main use case of a marketing data warehouse is to improve marketing analytics by optimizing how data is stored. However, it also has other benefits and can help you improve your marketing and data operations in other ways: 

1. Rally your people behind a single source of truth 

A marketing data warehouse organizes data into a single centralized location. When all teams, from marketing to sales, have access to the same customer data and metrics, it fosters a shared understanding of customer behavior and campaign performance. This helps you break down silos and align disparate stakeholders. 

Bonus benefit: The model of the data (called a schema) and the data warehouse architecture guarantee that this single source of truth is both accurate and reliable, free from inconsistencies and duplicated information.

2. Increase informed decision-making

With historical data storage capabilities, a marketing data warehouse allows you to analyze past marketing campaigns and performance metrics. 

This historical perspective enables you to make informed marketing decisions and fine-tune your marketing strategy based on historical insights and trends.

In turn, these improved decisions not only enhance marketing outcomes but also positively impact overall business metrics, boosting ROI, ROAS, and ultimately improving the bottom line.

📚Recommended read: Big Data In Marketing: 9 Examples & Use Cases

3. Accelerate data-driven marketing with business intelligence tools

Integrate your marketing data warehouse with business intelligence (BI) tools and reporting tools to speed up the creation of actionable insights. 

The structure in which marketing data warehouses store data is tailored to integrate seamlessly with BI tools. This allows for quick creation of data visualizations and dashboards. As a result, your marketing analytics team can derive data-driven and actionable insights faster than with alternative data storage methods.

Bonus: A top-tier data warehouse solution will handle report generation automatically, freeing you up to focus more on devising effective marketing strategies and less on dashboard creation.

4. Attribute and optimize marketing campaigns

A marketing data warehouse merges data from various sources, providing a unified view. This allows you to see customer interactions across every channel in the omnichannel journey.

By connecting the dots between different marketing channels, you can more accurately determine which marketing campaigns are leading to successful conversions. This clarity helps you channel your time and budget more effectively towards campaigns that yield the best results.

So how does a marketing data warehouse compare with other storage and analytics solutions?

Marketing data warehouse vs. other data storage options 

Data warehouse vs. database

At first glance, data warehousing solutions might seem similar to traditional databases. Both use SQL for data access, store data in a table-like format (think spreadsheet tables), and are frequently utilized in marketing analytics.

However, they differ in two primary ways:

1. DWHs usually store larger amounts of data: If you're dealing with a limited number of data sources or smaller datasets, a traditional database might be more appropriate. It's generally simpler and less demanding to set up and manage a database than a data warehouse.
2. DWHs are better structured for data analytics tasks: While databases offer flexibility and can store data even without a predefined structure, data warehouses emphasize strict adherence to their schema. This structured approach is what makes DWHs particularly effective for marketing analytics. They're designed to organize data optimally for easy analysis by BI tools and data analysts.

📚Dive deeper into the differences between a database and a data warehouse.

Data warehouse vs. data lake

While data warehouses and data lakes both handle vast amounts of data from various sources and support storage, analytics, scalability, and integration, they differ in several key aspects:

• Data structure: Data warehouses predominantly store structured data that adheres to a strict schema, while data lakes have the flexibility to hold structured, semi-structured, and unstructured data.
• Data processing: Data is processed before being ingested into a data warehouse, to guarantee its quality and accordance with data that already exists in the DWH. Unlike data warehousing solutions, data lakes ingest raw data, and data gets cleaned only later before it is used in a data analytics application. 
• Use cases: While data warehouses are ideally suited for business intelligence and structured data analytics, data lakes are optimized for tasks like data mining, exploration, and big data machine learning.

📚Dive deeper into the differences between a data lake and a data warehouse.

Data warehouse vs. data mart

Both data warehouses and data marts are data management solutions offering data storage, integration from diverse sources, and pre-ingestion data transformation.

The primary distinctions between the two are:

1. Scope: A data warehouse is a comprehensive, centralized repository that stores data from various sources across an entire organization. It serves multiple departments and supports a wide range of analytics. While a data mart is a smaller, more focused subset of a data warehouse. It is designed to serve the specific needs of a particular business unit or department, containing data relevant to their functions.
2. Data granularity: While data warehouses house data at a macro level, suitable for wide-scale reporting and strategic analysis, data marts store more detailed data to address specific departmental analytical needs.
3. Accessibility: Multiple teams can access data warehouses, fostering organization-wide data consistency. In contrast, data marts cater solely to a particular business unit, providing them exclusive access to relevant data.

How to set up your marketing data warehouse architecture?

Here are the 9 crucial steps to correctly configure your DWH architecture. Steps marked with (*) require technical expertise. For those less technically inclined, we've provided user-friendly alternatives.

Step 1: Define your data sources and objectives

Determine which data sources, such as CRM systems, web analytics, ad platforms, and social media, to integrate into your marketing data warehouse. Outline your marketing goals and key metrics to monitor. This forms the foundation for subsequent steps.

Step 2: Pick the right data storage solution*

Select a storage infrastructure for your data warehouse, keeping scalability, performance, and security in mind.

If you’re new to this or not very technical, opt for a cloud-based data warehouse like Snowflake, Google BigQuery, Amazon Redshift, or Microsoft Azure. Unlike on-premise data warehousing solutions, these are fully managed by cloud providers, allowing you to focus on marketing analytics instead of infrastructure maintenance. 

💡If you want to lower overhead even further, consider Keboola with its in-built Snowflake warehouse. This gets you a fully managed data warehouse, while also accessing other tools you’ll need to collect, clean, and move data around. 

Step 3: Create a data model or schema*

Design the schema that maps your data sources to the desired analytical schema. This will help you determine which raw data you can simply ingest and which ones you have to transform, clean, or aggregate before ingesting in the data warehouse. 

Looking for a refresher?

• If you’ve got plenty of time (a couple of months), Stanford’s course “Databases: Modeling and Theory” is still one of the best resources online to set you up with the right prerequisite knowledge. 
• Short on time? We’ve made a course on designing business data models that takes just a few hours. 
• For a quick overview during a coffee break, refer to our guide on distinguishing the main schema types, their pros and cons, and picking the right schema.

Step 4: Design and implement your data pipelines*

Set up an ETL process to move data from various sources into your warehouse. Opt for tools with pre-built connectors for seamless data extraction and loading. 

Instead of manually handling APIs and scripts, use connectors like those Keboola offers—with over 250 pre-built connectors, just input credentials to begin data transfer. Ensure all raw data aligns with your schema requirements.

Make sure to handle raw data according to your schema specifications. 

Step 5: Ensure data security*

Implement robust security measures to protect sensitive customer data within your marketing data warehouse. This should include encryption for data both at rest and in transit, regular access key rotations, taking database snapshots, and having clear restoration policies

By doing this, you’ll ensure compliance with data privacy regulations like GDPR, HIPAA, and SOC2.

💡Pick data warehouse vendors that offer many of the security features out-of-the-box and as part of their SLAs. For example, Keboola offers enterprise-grade security features alongside its other product offerings. 

Step 6: Maintain and monitor regularly*

Establish a schedule for routine maintenance tasks like data backup, system updates, and performance optimization.

Implement monitoring tools to track the health and performance of your data pipeline and warehouse.

Instead of dealing with the overhead of setting up observability yourself, look for a tool that offers monitoring out-of-the-box. Data platforms like Keboola offer a view, where you can see all your jobs and inspect their performance. For example, you can easily find data pipelines that produced an error:

Additionally - and unlike other platforms - Keboola also exposes all its telemetry data. This means you can build sophisticated monitoring reports and analyses on top of Kebola’s pre-built reports.

Step 7: Validate data (quality assurance)*

Set up validation processes to ensure data accuracy and quality throughout the ETL pipeline. Address any data anomalies or errors promptly to maintain the integrity of your marketing data.

You don't need coding skills to validate data. With no-code transformations, such as in Keboola, you can easily eliminate duplicate rows in a table with just one click.

Step 8: Data accessibility

Provide access to your marketing data warehouse for relevant teams and stakeholders: set up RBAC (Role-Based Access Control), export data to a BI tool for analysis, or build data apps that help them analyze data faster.

Tip: Utilize data visualization tools and reporting dashboards to make insights accessible to non-technical users. For example, with Keboola, you can quickly set up a digital marketing report to empower performance advertisers with the data they need. How? Build one yourself or use Keboola’s pre-built data app that you can launch in just a click. 

📚Recommended read: 8 Things You Can Do With Data Apps in Keboola

Step 9: Continuously improve

Evaluate your marketing data warehouse architecture to identify areas for improvement, such as job performance speed (lower the delays between data processing and data accessibility) or gap analysis (identify which data sources, datasets, metrics, and KPIs are missing in your data warehouse).

Adapt to changing data sources, business needs, and emerging technologies to enhance your data infrastructure.

How to set up a functioning marketing data warehouse with Keboola?

Keboola is designed to automate all your data operations. In fact, with Keboola you can set up a fully working marketing data warehouse (with 0 coding skills!):

1. Quick setup: Launch a marketing data warehouse in Keboola with a click. Keboola handles Snowflake DWH setup or lets you integrate your existing data warehouse.
2. Easy data collection: Collect marketing data with 250+ pre-built components. You simply configure access, and Keboola starts transferring data from a data source to your data warehouse or from the data warehouse to the BI tool of your choice. 
3. Intuitive ETL pipelines: Build data pipelines with the Visual Flow Builder. Simply drag-and-drop components in place. 
4. Zero coding skills needed: Clean data and compute marketing metrics using no-code transformations. Get your data analysis-ready without a single line of SQL. 
5. Pre-built data templates: Use data templates to automate data extraction, transformation, and loading for typical marketing scenarios. 

Keboola speeds up your marketing analytics efforts without requiring engineers. However, if you want them on board, it also has low-code features that simplify their workflows. 

Use Keboola to speed up marketing insights 

Keboola provides the technology to automate the infrastructure and processes for getting marketing data fast. You can then focus on optimizing marketing campaigns and building marketing strategies that delight your audience. In the meantime, Keboola sets up, maintains, and keeps your marketing data warehouse secure. 

Did we say it comes with an always-free pricing plan?

Try Keboola yourself today.