Customer data platform vs. data warehouse: Who wins? It’s an old debate that’s become new again – and it misses the point of these technologies.
If you’ve been following the news in recent months, it may seem like data warehouses — often called enterprise data warehouses (EDW) — are somehow new. This is because industry players such as Snowflake continue to make waves on the stock market and garner lots of headlines.
EDWs claim to solve enterprise data challenges by recycling the same tempting promises, such as data marketplaces that allow for the exchange of free or monetized data sets. Seemingly every few years a new player announces they’re building the “Google of Data.”
The pitch is always the same: All the data you’ll need will be “formatted and ready to be queried.” That is, data will be available and searchable, eliminating the need for engineers to extract, transform and load it from one system to another in order to become usable for your business use cases.
If it sounds too good to be true, that’s because it is.
Data warehouses aren’t new: They’re only moving from on-premises to the cloud and being presented as managed solutions. While EDWs are a must-have for enterprise companies, they’re very different from (and are intended to work jointly with) customer data platforms (CDP).
How data needs to be formatted depends on how exactly you want to query it, as well as the other data sources you need to match it with. And the truth is, getting data into a database was never the problem.
Beyond the bottlenecks of struggling to understand data definitions or adjusting your data to fit your models, the biggest barrier has always been much more straightforward. And that barrier is: Getting your data into the hands of your everyday business users so they can make data-driven decisions.
EDWs are a system of record — it’s data at rest. CDPs integrate directly with them, add additional data sources and provide business users with direct (but controlled) access to data for insights at activation. That’s data in motion.
Read on to learn everything you need to know about CDPs versus data warehouses — especially how they can unlock business value for you when working together as a unit.
CDP vs. Data Warehouse: The Evolution of Data Storage
To understand EDWs, it helps to start with another technology: data lakes.
What is a Data Lake vs. Data Warehouse?
A data lake is a centralized archive where a company can store all its raw, unfiltered data. This can include customer data as well as transaction, product, service, store, financial and human resources data.
Data lakes are used by IT teams to support and run enterprise-wide machine learning, analytics, data science and more. The downside to business users is that this data is only accessible via programming languages such as Java, SQL, Python and R.
While data warehouses are similar to data lakes, EDWs are used to store structured and filtered (not raw) data that’s already been processed and filtered for certain use cases.
And a data lake and data warehouse share the same disadvantage: They are built for and only accessible by technical professionals, not everyday business users. In other words, both leave business users at a loss and IT teams overburdened.
Drilling Deeper: CDP vs. Data Lake vs. Data Warehouse
So what is the difference between a CDP and a data warehouse or a data lake?
Simply put, an EDW and a data lake are both repositories for data. A CDP is a tool for business users to access and activate that data into customer experiences.
CDPs directly integrate with a data lake or data warehouse and make it available to business users for analysis and experience orchestration (no more dependencies on IT teams required every time someone from marketing, sales or customer service has a new use case).
Enterprise organizations with world-class marketing technology stacks should always have data lakes or EDWs for enterprise data storage and analytics — and a CDP on top of it to create a single customer view (a customer 360) and serve as the omnichannel hub. This allows marketers to run advanced data-driven campaigns that enhance customer experiences across all touchpoints.
In terms of data sharing, EDWs and data lakes often send deep customer profile and transaction data to CDPs. And in turn, CDPs often send their customer activation data back to data lakes and data warehouses to allow for greater precision and accuracy when performing enterprise-wide analysis.
Customer Data Platform vs. Data Warehouse: The Democratization of Data
To democratize data means to make it readily available and accessible to everyone who needs it in your organization, whether they are technical or business users. And customer data platforms are precisely designed to put valuable customer insights into the hands of the people who can take action on them.
Because data lakes and EDWs are built for IT and analytics professionals, they lack the intuitive user interfaces necessary to make customer data usable by everyday business users.
Unlike EDWs and data lakes, CDPs connect the dots and are uniquely positioned to democratize data for your organization. With a CDP, more teams – such as marketing, customer service and sales – can self-service and make data-driven decisions that drive profits rather than wasting time throwing strategies at the wall and seeing what sticks.
For example, a CDP like ActionIQ can quickly export your customer and interaction history from an EDW. Then, business teams can use it to personalize customer engagement across different channels as part of a segmented marketing campaign.
As a result, CDPs can enhance the performance of your martech stack and eliminate time-consuming manual processes. This frees up your IT team and speeds up the usefulness of data company-wide.
Customer Data Platform vs. Data Warehouse: The Best of Both Worlds
It’s important to note that CDPs are not intended to be the system of record for enterprise companies.
CDPs are designed to take the data from your EDW and help marketing, sales and customer service teams transform it into personalized, memorable customer experiences that let your brand stand out in a saturated marketplace.
CDPs and data warehouses should work in concert, with your EDW feeding your CDP data that business teams can easily access, analyze and activate across all online and offline channels whenever they need it.
The beauty of a CDP is that it allows companies to sidestep the classic challenges that prevent them from:
- Creating and continually updating an accurate view of the customer by unifying and deduplicating customer profiles
- Streamlining advanced audience segmentation to allow for highly targeted engagement across marketing, sales and customer service channels
- Generating predictive insights to inform strategies surrounding churn prevention, customer growth and more
- Orchestrating real-time customer experiences and sophisticated omnichannel journeys across the entire customer lifecycle
But remember that not all CDPs are created equal. To eliminate the technical bottlenecks everyday business users face with EDWs, your CDP must give non-technical teams an intuitive user interface they can leverage for accessing and activating customer data.
So while some data warehouse companies may be getting lots of hype, it’s important to remember that the same challenges remain.
The gold lies in empowering your organization to take action on customer data. And that’s exactly where an enterprise data warehouse falls short and a CDP shines.
In a nutshell: Data warehouses and data lakes should all be present in addition to your CDP. And your CDP should be usable by all channels (not just one specific channel such as email) in order to gather omnichannel data and facilitate seamless CX across the board.
Learn More About CDPs vs. Data Warehouses
Download our CDP Market Guide to navigate the different types of customer data platforms and how each can join forces with your enterprise data warehouse to deliver extraordinary customer experiences.
Got specific questions about the ActionIQ CDP? We invite you to contact our experts.