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Cloud Data Warehouse

Marketers: Here’s why you need a data warehouse

Why use a data warehouse? Dig into the benefits of a data warehouse and how data warehousing can both improve and future-proof your marketing efforts.

Anthony Rotio

Anthony Rotio

For the last few months, you’ve spent countless hours migrating your data into your team’s new CRM. You’ve invested a ton of time and dollars into this data centralization project because, at the end of it, you will be a data-driven marketer.

But what happens when you have a new data source you want to add to the mix — say, survey data from your customer team? Or you want to sync data with a new marketing channel. Or your data storage budget maxes out.

The solution to these problems is a data warehouse. 

But why use a data warehouse when your CRM or marketing automation platform can store customer data and use it on multiple marketing channels? Is a data warehouse something your marketing team really needs?

Let’s dig into the benefits of a data warehouse and how data warehousing can both improve and future-proof your marketing efforts.

What is a data warehouse?

Before answering the critical question “why use a data warehouse,” let’s define what it is. 

A data warehouse — sometimes called an enterprise data warehouse (EDW) or data cloud — is a central repository for company-wide customer data. By centralizing customer data, teams across the company can access, analyze, and activate that data, leading to more comprehensive analysis and decision-making. 

Data warehouses, especially cloud data warehouses, are built to store large amounts of varied and complex data. Some of the most common cloud data warehouse vendors include Google BigQuery, Snowflake, and Amazon Redshift.

How does a data warehouse work?

A data warehouse can store data from various online and offline sources — through a process called data ingestion. Most common data sources are marketing channels, external POS systems, mobile apps, etc.

Once the data is in the data warehouse, it’s processed and organized into different tables. When someone needs to retrieve data from the warehouse, they write a query to pull the data. The query is based on how the table is structured and organized. Often, these queries are written in SQL and data teams manage the data retrieval process. 

The role of data warehousing in martech

Much of today’s marketing technology (martech) ecosystem existed long before the data warehouse. Marketing clouds — the original all-in-one solution — were the go-to resource for storing data, creating audiences, and deploying campaigns across traditional channels. 

The primary problem with this solution is that it siloed data in the marketing cloud separately from the rest of the business’ data like transactions, customer service interactions, etc. Marketers could only use the data for the channels the marketing cloud supported. And the audience data didn’t translate across different martech tools, because it was organized for that specific platform. 

But over time, the data warehouse started gaining popularity as a central location for customer data. Data teams were the primary users, relying on the warehouse to build dashboards that reported on analytics and results for business leaders. Sophisticated data teams also built machine learning and predictive models in the data warehouse. 

With more organizations adopting the data warehouse, more teams outside of the engineering and data teams were also using it as a universal data layer to build audiences for marketing campaigns. 

Enter the composable CDP

Being able to view data and analytics stored in the data warehouse was a great start, but marketers needed access to activate that data to marketing channels. Enter the composable CDP.

The composable CDP solution sits on top of a cloud data warehouse architecture. It provides marketers with a no-code interface to create audiences using customer data in the warehouse. Then, marketers can activate those audiences across any channel they use for cross-channel campaigns — CRMs, email marketing tools, or ad networks.

A diagram showing how a composable CDP functions.
A composable CDP sits on top of the data warehouse, activating data to marketing channel destinations.

With thousands of marketing channels available today, more organizations are finding it more effective to use a composable CDP to build and activate audiences directly from the data warehouse. This removes the need to create separate data instances and audiences in each tool or platform. The composable architecture makes it easy to switch marketing tools and add new ones as the need allows.

Does my marketing team need a data warehouse?

Most marketers today use some kind of marketing automation, CRM, or marketing cloud tool to create campaigns. So, why use a data warehouse? 

If you’re wondering whether a data warehouse is the right path for your team, the following questions are a good place to start. 

Does your current solution support all the marketing channels your team needs now and in the future?

The marketing automation or CRM tool you use today likely covers a range of marketing channels — email, social media, SMS texting, etc. But the marketing channels your audience uses may change over time.

Consider Threads, Meta’s answer to Twitter. It has more than 100 million users and may release an ad platform in 2024. If your audience uses this channel, you’ll need to find a way to reach them there. 

But your existing marketing automation platform may not be able to support a new platform quickly. It can often take months for new features and connections to roll out, which means your marketing team plays catch-up with other brands who were able to hop on the platforms sooner.

Leveraging a data warehouse for your universal data layer opens up opportunities to use more flexible solutions like a composable CDP. Using a composable CDP on top of a data warehouse like BigQuery, Snowflake, or Amazon Redshift, you can add connections to new channels and customize your marketing stack with best-in-breed tools. And with a centralized repository of customer information, you can launch campaigns across all these channels using a single source of truth rather than maintaining lots of copies of your customer data across different, siloed tools.

Are you questioning your data’s accuracy, completeness, or freshness?

If you’re finding data discrepancies across different marketing tools and dashboards, it may be time to consider a data warehouse to centralize all your customer information and campaign metrics. 

Unfortunately, many CRM and marketing automation tools have limitations on data transfer rate, number of data fields, and data storage. These limitations can delay customer information updates or, worse, create inaccuracies. But with a data warehouse, you can ingest all data sources and organize that data in a complete, 360-degree view of the customer that’s updated in near real time. 

Audience inaccuracies will ultimately cost time and money across the business — from underperforming campaigns to eroding customer loyalty and missed sales opportunities. But with reliable data, creating accurate audiences for your sales and marketing campaigns is easy.

Do you have a talented data team who wants to provide better customer analysis? 

One of the perks of having a talented data team is its ability to create robust analyses and models that help your business make more informed decisions. But it can be challenging to create these analyses using audience data in a CRM or marketing automation tool or getting that marketing data into a business intelligence platform. The platforms often do not have a way for data teams to access the data using SQL or other programming languages like Python and they are effectively locked out of the black box where the data lives.

However, a data warehouse speaks the right languages for a data team and if your marketing tools are pushing campaign data into the warehouse, your data analysts can use and build on comprehensive audience information from one reliable source. 

Has your data centralization project dragged on for months or years? 

Many marketers today are involved in a months-long data centralization project — maybe you’re one of them. These projects aim to organize data into a single marketing automation tool or marketing cloud. But they take a long time (sometimes six to 12 months) and can come with a hefty price tag. 

If your team has been spinning its wheels on a data centralization project, the data warehouse may be a faster option. Some CRM-to-warehouse connection tools can have your data up and running within a day, meaning your team can start with a few small use cases in hours, not months. 

Who doesn’t need a data warehouse?

Here’s the truth: Not every company needs a data warehouse. 

If you’re a small organization with a few thousand customers and don’t expect to grow anytime soon, sticking with a CRM or marketing automation tool is probably fine. This is especially true if you don’t foresee your marketing expanding beyond your current channels and you’re satisfied with the types of campaigns you’re running today.

How secure is a data warehouse? 

Security isn’t just for businesses in healthcare and finance. More organizations across industries prioritize data privacy and security as part of their customer acquisition and retention strategy. That’s because one in four Americans won’t do business with data-breached companies.

The good news is that security is a primary focus for data warehouse vendors, especially industry leaders like Google Cloud BigQuery, Snowflake, and AWS Redshift. When it comes to security measures, it’s difficult to put a martech or SaaS provider on the same plane as these platforms.

It’s also important to remember that centralizing your data in one location can reduce your risk of a security breach. A single source of truth means you’re not copying data into different platforms and channels, each creating a potential security weakness.

Are data warehouses worth the cost? 

There’s no question that a data warehouse will take some financial investment. But consider that investment against the cost of your current or previous data centralization project.

Moreover, how will the data warehouse cost compare to the marketing automation tool as your organization — and data — grows?

Here are some essential questions to consider when comparing the costs of a data warehouse vs. your current marketing automation solution.

  • How do the price points change as our team adds data, channels, or audiences?
  • Am I paying for redundant copies of my customer data in many platforms today?
  • How quickly will our team be able to see value with this solution?
  • Will this solution free up time and resources from the data team (operational costs)?
  • Will this solution allow us to improve our marketing campaign ROI?  
  • Will this solution prevent potentially costly security breaches? 

As your organization works to take more control over your data, a robust and secure solution will serve you best — both from a privacy and customer personalization standpoint. While a data warehouse may not be the solution for every team, you’ll likely find it’s the most affordable, flexible, and growth-savvy option. What’s more, it may be the answer to future-proofing your data and marketing needs, helping your organization maintain success and strong customer relationships as it grows.

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