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A Marketer’s Guide to Setting up And Optimizing a Data Warehouse

Learn how to setup a data warehouse for your business and proven strategies to overcome challenges affecting data warehousing success.

The range of marketing tools used by the average business to collect user data is steadily growing. There are several ad solutions, CMS, and social media platforms in addition to analytics systems.

Cloud-based data warehouses allow you to consolidate all of that information into a single location that’s designed for both storing and analyzing data.

Pandas dataframe and other data analysis tools are designed to make the jobs of marketers, data analysts, and business owners much easier and more efficient.

In practice, however, you’ll wind up with a slew of silos — systems that don’t interact properly with one another and never agree on key metrics.

Data silos create difficulties and conflict among teams. As new data control methods like cookieless tracking look set to influence future marketing efforts, it’s critical to work on optimizing your data warehouse to boost your marketing strategy.

What is a Data Warehouse?

A data warehouse is a centralised storage location for integrated data. Data warehouses collect current and historical data from your organization’s various source systems into a single location. They’re designed to support query and analysis.

Using a data warehouse in the marketing environment offers several advantages over alternative data analytics and storage solutions. Benefits range from inexpensive storage to flexibility, the option of enabling automation by centralizing data from several sources, and easy analysis.

Marketing and analytics teams can use data warehouses to combine data from a variety of sources, including Facebook, Google Analytics, and CRM systems like Salesforce, among others. Distributed file systems, such as the Hadoop Distributed File System (HDFS) are well-suited to large data sets.

Data warehouses use structured tables to make it straightforward to query the specific data you want to include in your analysis or report. The query language used in most data warehouses is SQL.

Data warehouses are made up of two primary components:


Data warehouses offer you storage and help you to organize, and combine granular data from a variety of sources over time. Many businesses use tools like Elasticsearch to index structured offline and online data.

You won’t have to rely on the data retention policies of your marketing platforms. Instead, all of the cross-channel data you’ll use in benchmarking and historical analysis is conveniently saved in one location – and at a minimal cost. As your dataset rises, so will your storage capacity.


Data warehouses can process tons of data in addition to storing it. With the cloud computing power of a data warehouse, you can crunch more data with only a few clicks. This can be a game-changer for analytics in scenarios where you need to query a specific dataset.

Why Do You Need a Marketing Data Warehouse?

The more your business grows, the more marketing tools you’ll need to keep up with the expansion. Without a central location for your data, you will be operating at reduced efficiency with data silos.

Investing in a marketing data warehouse is the only way to remedy the situation. You need a system that integrates marketing data straight from all sources into a central repository.

Here are a few reasons for you to consider a marketing data warehouse for your business:

Information from a single source

The main advantage of having a marketing data warehouse is that everyone in your company will work with the same metrics, generated in the same way, and coming from a single source. This saves you a lot of time and confusion, as well as potential disputes across teams and functions.

A full picture of the user’s journey

Since no single tool understands every aspect of your users’ journey, studying data from a single source doesn’t provide you with a complete picture of their experience.

When you combine data across all tools and sources, including online and offline, you get a far more comprehensive picture of the full user journey.

Furthermore, long-term measures such as user lifetime value are substantially more accurate as a result of this.

Accessing raw data

The user interfaces of most social media metrics tools and other marketing tools display aggregated data and derived metrics. This is the major reason why metrics from different channels rarely match – they essentially measure and estimate metrics like revenue, sessions, and conversion rate in different ways. You specify your aggregation and calculation methods when working with raw data; this should be agreed upon at the corporate level.

Manual data joining isn’t scalable

Exporting data from numerous sources and merging it into Spreadsheets can provide you with unique insights that you wouldn’t get in any one tool, but let’s face it, this technique isn’t particularly scalable. Data warehouses allow you to pre-join combined data or integrate them on a dashboard.

Setting Up A Marketing Data Warehouse

Whether it’s a growing amount of un-processable data or a pressing need to track ROI across different channels, campaigns, products, and tactics, the signals and timeframes associated with setting up a marketing data warehouse will look different for every business. An internet phone service is one useful way to streamline communications.

The steps and decisions required to get a marketing data warehouse running smoothly include identifying your data source, establishing how to feed the data into your warehouse, and selecting a reporting and analytical layer. Let’s take a look at each one separately.

Identify Your Marketing Data Sources

Marketing data warehousing success relies on the proper identification of your data sources. Common options include:

  • Ad platforms like Google Ads and Amazon;
  • Email marketing platforms like ConstantContact and Mailchimp;
  • Social media platforms like Instagram, Twitter, and Facebook;
  • Analytics tools like Adobe Analytics and Google Analytics; and
  • E-commerce platforms like PayPal and Shopify

Select an Appropriate Marketing Reporting Software

After confirming your data sources, the next logical step is to identify the reporting tool that will help you develop relevant insight via analytics and data visualization. Several marketing reporting software options cater to different brands and budgets, including Google Data Studio and Tableau.

You might be asking yourself why you need reporting software.

Many marketers are still having trouble reporting advertising performance or campaign results. Clients and stakeholders are still being sent heaps of worksheets with tabs and pivot tables to go through.

Effective marketing performance reporting software must produce reports loaded with relevant information for the reader, adapted to goal-based metrics, and delivered compellingly, be it a spreadsheet, dashboard, slide deck, or PDF.

The way your business visualizes and reports on marketing performance, both externally and internally, can have a significant impact on the company’s image and trajectory.

The approach which you use to report marketing performance results can have as much of an impact on campaign outcomes as the content of those reports.

Setup a Marketing Data Pipeline

Large data sets appeal to everyone in marketing, but few understand the technical aspects involved in correctly constructing data pipelines for a data warehouse.

A marketing data pipeline is a defined set of procedures and tools that receives, refines, and decodes data from origin to the warehouse, and then to reports.

Although there are many capable and educated vendors in the data pipeline market, such as Stitch and Xplenty, it is possible to build a data pipeline using free, open-source software.

What are the main reasons why most companies and brands do not have their data pipelines? Pulling data consumes the majority of the client reporting process’s workload and time, thus optimizing its quality and speed is critical.

To function, data warehousing relies on schemas and tables. To make cross-channel data comparable, employ consistent naming systems and standard schemas for your data warehouse, without delving too deep into data management procedures.

Points to Consider When Evaluating Your Data Warehouse Options?

There are numerous factors in favor of implementing a marketing data warehouse. The question now is how to select the most appropriate solution for your requirements.

With data quantities increasing daily, it is essential to properly assess your storage options. Data warehousing market projections indicate a staggering growth rate by 2028 citing the need for advanced analytics as a major driving factor.

There are two main warehousing options available; an on-premise database or a cloud database. To make your choice you should consider several essential functions including cost, speed, security, and scalability.

If you need to prioritize speed, control, and a robust security system, an on-premise database might suit your needs better. Cloud databases also offer sound security systems. The cloud’s major advantage is security. Working with the industry’s top vendors guarantees you get up-to-date security upgrades. Protocols are regularly updated, and any potential flaws are addressed as soon as they are discovered. Those guys stand to lose a lot if they aren’t quick and safe enough.

On-premise solutions provide a greater advantage in terms of speed. Your users will not have to wait for data or deal with constraints such as inadequate bandwidth or server capacity.

Both solutions offer advantages in terms of reliability. For the cloud, selecting the correct provider is critical. You can hire the professionals you need and have them physically present if an issue emerges with your in-house solution.

A cloud solution outperforms traditional solutions in terms of entry cost (no expenses on servers and hardware) and scaling capabilities. After all, the amount of space available in the cloud is almost limitless.

Optimize Your Marketing Efforts with a Marketing Data Warehouse

While using data to drive decision making is crucial, the quality of data-based decision-making is only as good as the data on which it is based.

Marketers require technology that reduces the number of hours spent interpreting data and shortens the time it takes to extract insights.

That’s why a well-designed marketing data warehouse with improved reporting may help you gain deeper insight and make more informed decisions.

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Pohan Lin Senior Web Marketing and Localizations Manager, Databricks
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global PySpark and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing.

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