Like many martech and adtech categories, data management platforms (DMP) are tricky to pin down. We wrote earlier about the difference between first-party DMPs and third-party DMPs, but another question is how DMPs mesh with enterprise data warehouses (EDWs). Since a sizable chunk of companies that have yet to invest in a DMP have invested, often heavily, in data warehouses, those companies are understandably leery of adding something that “manages” data when they already have a warehouse.
Data warehouses and DMPs have a few similarities, but they also have some major differences that allow the two to play potentially very different roles with audience data. We’ll compare the two, so you can begin to decide whether and how they fit into your organization.
Manage and store data
Let’s start with the big similarity. At the end of the day, EDWs and DMPs store data.
An enterprise data warehouse integrates data from a whole lot of sources. That data is cleaned, organized, and processed to create business intelligence (BI) reports, track trends, and support corporate decision-making. The typical users are BI analysts with the technical know-how to write queries and build reports for internal executives. Some EDWs integrate with BI dashboards like Tableau to make those reports more visual.
A data management platform also integrates data from disparate sources, but is focused on marketing and ad campaigns. Third-party DMPs will leverage cookie data to target users on ad networks, while first-party DMPs will use personally identifiable information (PII) to market and advertise to people beyond only display ads (e.g., email, social networks).
First-party DMPs are closer to EDWs in data management since they both collect first-party data, while third-party DMPs are generally restricted to cookies or mobile device IDs.
EDWs: All your data
But even in the management of the data, there’s already a massive difference between warehouses and data management platforms. Because of the sheer quantity and types of data that data warehouses collect, they require a lengthy process to implement and the data itself needs to go through multiple steps to be usable.
First, they pull the data from the sources into the warehouse, which includes CRM (customer relationship management), POS (point of sales) and ERP (enterprise resource planning) software.
That data is output to users through a data mart. Essentially, a data mart is a slice of the warehouse that takes the data down from “enterprise” to “department-specific.” Finally, it goes to the BI dashboard (e.g., Tableau) for digestible insights and charts.
Here are some challenges to expect when you’re dealing with that much data and complexity:
$$$. You’ll need to consider not only the initial cost of implementation, but the ongoing maintenance, training and support.
Slow implementation. Since you’re not working within a few select departments, but often across many or all departments in your organization, you’ll have to get buy-in from all those decision makers. Even with all that buy-in, a data warehouse will take months to set up.
Advanced technical skills. The data in a data warehouse isn’t necessarily much more accessible. In fact, just to get the insights you want out of the warehouse, you might end up staffing up with BI professionals, analysts and systems ops so that data actually is useful.
Lag times. Because of the layers involved with storing and indexing data, real-time is hard to come by in an EDW. Without a new development cycle, you’re tied to fixed, pre-determined queries to visualize, and even that won’t be instantaneous.
DMPs: The data you need
Since they contain actionable audience data, DMPs pull in more defined sets of data. For third-party DMPs, that includes cookie and mobile device IDs appended by demographic information.
For first-party DMPs, that includes a subset of the data that would be collected within a data warehouse, but only what’s useful for acting on audience data. For sports and entertainment companies, those sources could include CRM, transactional data, ESP (email service provider), and social data (e.g., likes and interests from Facebook logins). Unlike data warehouses, It wouldn’t include ERPs or other “non-essentials” for selling tickets and merchandise.
And for the marketers, salespeople, sponsorship teams and others who need that audience data, it is, or should be, easily accessible. Umbel, for example, allows any user not only narrow in on any segment within their audience, but compare that with another segment—instantly.
Activation vs statistics
One aspect that’s not implied in the “data management” part of DMP is the activation of that audience data. Because while it’s nice to look at how season ticket buyers compare to the rest of your fan base, it’s even nicer to use that season ticket buyer profile to seek out similar profiles in your database.
A first-party DMP can analyze your audience profiles based on the sum total of data across channels and export that data to run hyper-targeted campaigns on Facebook, email, etc. or find new fans who look like your best customers (based on any number of factors, including someone’s past game attendance, when the last time they checked in was, or their overall profile). The performance of those campaigns can then be fed back into the DMP for a continuous improvement feedback loop.
A third-party DMP is similarly actionable, targeting groups of users that meet particular attributes (e.g., visited website in the last 90 days), but is limited to exporting to ad serving networks that leverage cookies.
Data warehouses are far less actionable than either form of DMP. While data can be exported, that’s not the intent of a data warehouse. The typical definition you’ll find is similar to Informatica’s, which is that “data warehouses are typically used to correlate broad business data to provide greater executive insight into corporate performance.”
That is, the major utility of the data warehouse ends after data is analyzed and displayed on a BI dashboard. And because of the time delay in the data and lack of optimization for a specific purpose (e.g., selling tickets), the utility of that export is minimal compared to using a DMP to find a segment, immediately exporting it and then running a campaign.
Acquire vs only unite
Where a first-party DMP differs from both third-party DMPs and data warehouses is the ability to bring entirely new data into the mix. All three are able, to varying degrees, ingest data from existing sources. By also using data collection campaigns to add to those other sources, you can acquire and enrich profiles in a first-party DMP, or in the case of live events, can bring users into your database that are otherwise unknown (e.g., people who buy tickets off the secondary market or tag along with a group or family member).
This ability to gather new data solves a large issue for a lot of organizations, which is a lack of quality data. And without a solid data set, regardless of whether you invest in a DMP or a data warehouse, any benefit you get will be limited.
Data warehouses solve a lot of needs, but they do so as high cost, long-term investments in both financial and human capital. Which isn’t to say those investments aren’t worth it, but data on selling tickets and selling tickets are not synonymous. That may come as a surprise to the 72 percent of marketers focused on knowledge gathering rather than acting on data.
When you’re deciding how EDWs and DMPs fit into your tech stack, it’s important to look at your business needs, as well as the needs of your audience. Being able to bring audience data together is important, but being able to do so and act on that data is critical.