In the world of martech and CX, the only constant is change, and this past 12 months have been no different. At Oracle, we have the privilege of speaking with some of the world’s biggest organization’s about the waves of change they are going through. Whether an organization’s change was rooted in a brand new enterprise AI strategy, delivering cost efficiencies, or even age old questions like data ownership… the common thread remained clear: trusted and unified first-party data is mission-critical to business outcomes.

Two technologies have taken shape as the key ingredients to an enterprises first-party data strategy: the data warehouse and the customer data platform (CDP).

So when I hear the question, “should I just leverage my data warehouse as a CDP?” it’s not something that’s completely out of the blue. At Oracle, we are no strangers to the strategic value of a cloud data warehouse in customer data management. In our own martech stack we leverage Unity CDP alongside Oracle Autonomous Datawarehouse to leverage the full breadth of our enterprise first-party data.

Still, ‘Composable CDP’ is a big buzzword in the martech universe suggesting the idea that Customer Data Platform buyers leverage software that provides a UI for audience building and an activation layer on top of their existing data warehouse.

A composable CDP strategy in and of itself may simplify customer data flow diagrams within the enterprise, but it also presents both limitations for enterprises and new processes (especially marketing and IT teams) to grapple with around their most valuable enterprise asset: their first-party customer data.

Let’s talk through some of those:

1. Complexity in Assembly and Maintenance

Think of a composable CDP like building a custom car from scratch. Sure, you can choose every part to suit your needs, but you also have to put it all together. In driving towards a composable architecture you’ll need to pick your cloud infrastructure, database vendors, data integration tools – and that’s before you even get to the actual CDP with added complexities like ID linking, modeling the data and even thinking through how you’ll develop audiences that are required in the enterprise across the spectrum of B2C (ex. households) and B2B (ex. accounts, buying groups). It can quickly become overwhelming, and maintaining this patchwork system is no small feat​.

2. New Math for Total Cost of Ownership

Estimating total cost of ownership is a key factor in ensuring that your move to composable architecture is actually driving the cost efficiencies that you expected. Data warehouses are priced on consumption-based models that factor in compute, storage, data ingress/egress which is different than most every other pricing model that business users anticipating access to that data will be used to.

Budgets and usage questions around the chosen DW need to be addressed by marketing and IT teams as they think through long-term storage of new data sources that customer engagement use cases rely on (ex. real-time events), a significant increase in the frequency of retrieving data and even scaling capacity for peak volumes (ex. Black Friday week).

3. Latency and Real-time Activation

Moments matter in customer experience. Teams and tools are only as good as the data that they have access to deliver an experience in the moment. With a composable CDP approach teams will be waiting for data syncs both within the data warehouse itself as well as how quickly a sync can be done through a chosen composable CDP provider out to engagement technologies across marketing, sales, service, analytics and more.

This becomes even more complex as marketers and CX teams look to AI to augment and improve real-time decisioning.  Does data need to shift between clouds in order to reach the model, and vice versa for predictions, scores and recommendations? How much data does the model have access to and how fresh is it?

4. Security and Compliance

Handling customer data always means dealing with security and governance. In a composable CDP setup, ensuring that all your components meet security standards and comply with regulations can be complex, especially in global enterprises needing to flexibly meet a variety of regulations. Once you have down, you then need to navigate governing access to data across business units for different users. Each vendor has its own security protocols, and making sure they all align can leave gaps and vulnerabilities that are hard to manage.

5. Innovation and Technology Development

When approaching this composable CDP question, it’s often good for IT and marketing teams to ask themselves “what do we want to be known for?”. Each and every business needs to prioritize what’s going to make an impact on their business and ultimately drive growth. Many innovation hours can be spent on building, maintaining and even developing an internal roadmap of innovation for a composable approach to CDP… and in the end it certainly drive growth outcomes for your business. But is that where your enterprises professional resources and cycles are best spent and maintained? And does it allow you to innovate in the areas that matter for your business?

Let’s Return Back to Key Ask – Customer Data

Now that we’ve talked through some of the considerations, let’s talk through opportunities to rethink how we’re maximizing the value of enterprise customer data in this new AI-driven, first-party world that we now find ourselves living in.

First, the data warehouse and the CDP shouldn’t be a “this or that” discussion within your enterprise, but rather two technologies that are better together.  In fact in this year’s Gartner Digital IQ Index, found that the “Genius” and “Gifted” brands were the most likely to have deployed both a CDP and CDW rather than just one or the other.

The operational customer insights that exist in back-office systems like data warehouses or even ERP systems (transactions, product ownership, financials, etc) are too valuable to be left out of the customer experience equation. Those operational insights are critical to enabling a CDP to be leveraged for more complex use cases like upsell and cross sell campaigns and more complete customer analytics. One Unity CDP customer, Vertiv, looked to their back office data source (ERP) as one of the most critical data sources for improving marketing and sales processes as well as uncovering revenue opportunities within their install base. Also as I mentioned earlier, our Oracle@Oracle marketing team is leveraging both CDW and CDP as integrated components to our own martech stack.

With CDP and CDW being seen “hand-in-hand” technologies in the enterprise rather than opposing forces, marketing and IT teams can make more strategic and efficient decisions about what data is needed where, and for which use cases. Should a profile in the CDP simply be enriched with an enterprise data set, or score rather than porting the actual data over? Do we need every real-time event stored in the DW or can we leverage that for our CDP and journey orchestration only?

Second, in this new world, there’s real power in a vertically integrated approach. Oracle’s vertically integrated approach to infrastructure, data and the applications that sit on top of it provide flexibility for organizations to actually blend CX applications and that data infrastructure that underpins it. Let me take you through some of these advantages very briefly:

  1. Federated and flexible data management: First-party data needs to be accessible, available and contextual. Because we own every layer of our infrastructure and our data resides on a common model, CX apps, like Unity CDP, can natively access both front and back office data without movement within the Oracle ecosystem. This reduces overhead and enables IT to manage customer data wholistically while providing access out to business user applications that need to consume it. We also provide flexible, enterprise level integration to ensure that any third-party technology (including data warehousing) that can also be integrated without fees for ingress or egress.
  2. Streamlined costs: Speaking of costs, our investments in the infrastructure level help us pass on unique advantages to our customers. “Cost of doing business” fees that exist for a majority of CDPs in the market like data ingress/egress, attributes and even data activation aren’t part of our pricing model.
  3. Compute, AI and Data Next to Engagement: That leads to more unique advantages, even outside of costs, like being able to place unified customer data (any first-party data you need) next to embedded AI models for real-time decisioning, scoring, prediction and ultimately activation to power personalization at the engagement layer. Giving you more insights to deliver the right action; built right in.
  4. Faster Time to Market: Ultimately we’re rethinking data architecture because we want to leverage that data to drive growth for our business. Marketers and line of business users ultimately may not care which of their audiences are copied from a CDW, or exactly how much compute they’ll need to run a prediction across a journey. But they will care that they have access to the data they need to move fast, streamline their workflows, serve their customers and grow revenues.

 

Learn more:

· Oracle Unity CDP

· How Oracle’s own martech stack leverages both CDP and DW

Webinar: Can You Adapt Fast Enough to Today’s Martech Challenges?