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Data / Software Development

Data Warehouses and Customer Data Platforms: Better Together

Used in tandem, they can provide numerous opportunities to deliver sophisticated, personalized, data-driven customer experiences.
Jun 29th, 2023 7:14am by
Featued image for: Data Warehouses and Customer Data Platforms: Better Together

Imagine you are the CTO of a large, global enterprise. Your teams are debating a new architecture for the marketing and analytics technology investments that will accelerate your business over the next year. But let’s be honest: Given the criticality of these workloads, you won’t have a lot of chances to make changes after the fact. The organization could be living with this new architecture for the next five to 10 years.

Two factions have broken out, proposing what seem like radically different approaches. The first has embraced the data warehouse/lake house architecture as a “single source of truth” for all data and believes in the need to adopt a “modern data stack” centered on the data warehouse, with no need for a customer data platform (CDP).

The other believes the biggest problem is around the challenge of ingesting high volumes of streaming customer data in real time and is concerned about preserving the ability to keep pace with the advanced use cases on the marketing and analytics roadmap. This team believes in a CDP that can extend and adapt to real-time data as business needs evolve.

They’ve asked you to review their pitches and make the call. The fate of the business (and many people’s careers) is in your hands. What do you decide?

Before you answer, consider that it’s not as simple as “data warehouse vs. CDP.” Let’s look at a few of the capabilities teams can gain by using a data warehouse with a CDP.

Identity Resolution at Scale

As a business scales, it becomes more difficult to recognize customers as individuals and offer them personalized options across all the touchpoints in their journey. As we move data from one place to another, connecting those data points to identities becomes a difficult data engineering problem. That’s where identity resolution comes in: taking vast amounts of data in motion — trillions of data points streaming at us every second — and turning it into a shared understanding of who a customer is, where they are in their journey and how to better serve them.

That’s what a CDP excels at. By working together with data in various applications, databases and the data warehouse, a CDP helps businesses collect, unify and ultimately activate identity-resolved customer profiles to link this data together.

Without a CDP, transforming identity resolution takes a significant amount of time and data science resources. SQL is not the right solution for advanced identity resolution, and at the end of the day, “excellent logic for identity resolution” is not a differentiator for most businesses. But the ability to drive personalized engagement and build delightful products for customers can make or break your business — and that comes from a deep understanding of users across touchpoints. The combination of a CDP and a data warehouse makes identity resolution easy, allowing data engineers and analysts to focus on other problems.

Shifting from Data Storage to Data Action

Data is useless if it’s just sitting there idly. But far too often, data gets stuck in the warehouse, where it goes unused. That’s because, historically, the consumers of data are not the ones who can tap into the power of the data warehouse — they need to turn to their engineering teams and ask them to implement a new SDK, to build an integration or to implement a new tool. It becomes an endless loop of chasing the newest tool, and IT and engineering teams get bogged down building and maintaining integrations.

That’s why you need to provide tools to enable self-service data analysis — and to enable analytics and data science teams to build models that the whole business can leverage. Do this, and you’ll replace the endless loop with a virtuous circle.

A CDP with reverse ETL (extract, transform, load) enables teams to automatically move data from the warehouse back to the downstream tools across marketing, sales and support, where it can be activated to create hyper-personalized customer experiences. A CDP also provides “no code” ways for business teams to activate data, with centralized audience- and journey-building tools.

With a CDP and a data warehouse you can build a trusted data infrastructure, laying the groundwork for any potential AI strategy. The “intelligence” in AI is trained on customer data. If your customer data is siloed, inconsistent, stale or incomplete, even the most innovative AI applications will have less impact.

A trusted data infrastructure, provided by a CDP, delivers the unified, consistent, real-time data that is critical to AI. You can use this infrastructure to prepare data stored in the warehouse for advanced model training.

Easily Streaming Data to the Warehouse 

Businesses can create a more complete view of their customer by streaming real-time data updates from many sources into their data warehouse. But integrating those data pipelines can be challenging, and that’s where a CDP integration comes in.

Let’s look at an example. MongoDB is a modern SaaS company with a complex sales cycle involving many stakeholders from different teams within a prospective customer’s organization. As a result, MongoDB has a lot of multithreaded, multichannel sales processes running simultaneously. To get a full picture of a prospect, MongoDB needs to consolidate 181 different datasets alongside its “golden profiles” (powered by the CDP) uniquely identifying each customer contact. Some of those are live chats happening on the marketing site, some are interactions taking place in the application layer. Some of this data is slow moving, sitting in tables in a data warehouse. Some of it is coming in at a speed that requires sub-second latency to resolve. Being able to bring all of those data streams together is a huge enabler for MongoDB’s team, and it’s only possible because MongoDB is using a CDP alongside its data warehouse.

By combining real-time event streaming, ETL and reverse ETL in a single platform, data teams no longer need to set up data pipelines and maintain them over time. And adding new sources and destinations is much easier with a CDP, creating faster time to value.

Building Customer Trust

Companies today need to be more vigilant than ever about how they’re using customer data, but you can’t let that get in the way of providing meaningfully personalized experiences. In fact, customers expect this.

Imagine picking up the phone and telling a customer service representative about a problem you have with their product and their reply is a generic marketing message that is totally irrelevant to what you just told them. That would be off-putting! In fact, 66% of consumers say they will no longer use a brand if it’s not providing relevant and personalized experiences.

Being able to provide that level of personalization and relevancy requires data, but you need to be transparent about how you are using that data. The most advanced companies are wildly transparent about how they’re collecting and using data, and they are providing advanced controls to end users.

Practically speaking, consent management means exposing granular sets of preferences for users so they can opt in or out of how their data is being used. ​​In other words, systematically informing users about how you collect private data and what you use it for, giving them the ability to give their consent or deny it, and then implementing those preferences consistently.

But applying those controls across a dynamic range of applications, internal teams and use cases — that is a difficult engineering challenge. This, again, is where the unified platform of a CDP that has consent and privacy baked into it can provide benefits. Those controls can span across data in the warehouse as well as real-time events in applications, ensuring that customers’ preferences are followed, consistently, everywhere they interact with you.

Of course, regulatory compliance is critically important here too. For GDPR, you may need a regional data center in the EU so that European customers’ data can be stored there. For HIPAA compliance, any vendor that you use will need to sign a Business Associate Agreement that governs how they manage protected health information. All of that also needs to be managed, and a CDP can provide a control layer that helps ensure everything that arrives in the data warehouse is compliant.

Better Together

In today’s data-driven world, organizations need to think carefully about the architecture that will best support their business needs. The data warehouse and the CDP are not mutually exclusive choices. In fact, a CDP enhances the data warehouse and provides numerous opportunities to activate the data in the warehouse and deliver sophisticated, personalized, data-driven customer experiences. And that, at the end of the day, is what customers are looking for.

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TNS owner Insight Partners is an investor in: Pragma.
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