How to Use Data Management Platform (DMP) the Right Way?

Volodymyr Bilyk
9 min readFeb 15, 2021


In this day and age — information is the most valuable thing in the world. No wonder — it is the stuff dreams. They are made off — something of a new universal currency. However, one should know how to use it to make it work for his benefit. And there is no industry where this is more apparent than in the advertising industry and Ad Tech in particular, where competition is so tough one false move can derail everything. And that is where Data Management Platforms (DMP) come in and make and work some sweet Ad Tech Magic.

The thing is — modern advertising is an extremely complex and multi-faceted operation. Another critical thing to note is that it is massive. Not just very big, but Godzilla-like imposing and intimidating massive. Every little thing counts and makes a difference. There are many moving parts involved, and all of them are equally important.

Modern DMP’s are required to process millions upon millions of events in a short period. Just think about that number — it is ridiculous. And it takes a significant computational capacity and very flexible scalability to make it all click.

But first, let’s sort things out.

What is Data Management Platform?

Data Management Platform (DMP for short) is a type of centralized tech platform that gathers data from various sources, segments it into predetermined categories, and further transfers it to attain the marketing campaign’s specific goals.

To put it merely, the Data Management Platform is one of the foundations of Ad Tech operation. It is one big dashboard of tools that gives you the big picture of what is going on with your efforts and provides instruments to turn the tide to your benefit.

DMP control panel is a war room where you can check the situation’s status and plan your next moves.

How DMP works in Ad Tech

The primary purpose of DMP in Ad Tech operation is to keep a firm grasp of the campaign’s proceedings. The use of information gathered by the Data Management Platform provides additional agility to the Ad Tech operation — it improves the definition of the target audience and subsequent ad distribution.

That gives marketers the agility to adjust the campaign as it goes on according to the target audience’s reactions.

The result of its DMP process is more efficient and precise targeting to the target audience that generates conversions that in turn enhance ROI from ad spending. This is a crucial thing because gestation and hesitation are rather destructive approaches in marketing.

Here’s what a Data Management Platform can handle:

  • Manage and adjust ad campaigns;
  • Provides stats that can help to increase conversion rates, improve user experience, and establish the brand;
  • Makes effective use of ad budget with a higher probability of return on investment;
  • Personalize content shown to the users to increase the probability of conversion and establish the brand;
  • Study the target audience’s behavior and preference to create a credible user profile for more efficient targeting.

What types of data DMP collects?

Data Management Platform is capable of collecting data from the selected source. All you need to do is to define what kind of information you are interested in.

Basically, a Data Management Platform is attached to the source of information (for example, a website) and gathers information regarding specific user activity.

This information is subsequently merged into one big picture that can help marketers to understand how to build the campaign and what kinds of approaches will be the most effective with the selected target audience.

All data is divided into first-, second-, third-party. Let’s break it down:

First-party data includes:

  • Web / App data
  • Data coming from the analytics tools (such as Google Analytics)
  • CRM
  • Transaction systems
  • Subscriptions
  • Audience information

Second-party data is precisely the same as first-party except for another company involved in the operation.

Third-party data is collected and segmented independently of the company and later sold to it.

Self-Hosted or Third-Party?

The difference between custom and third-party data management platform is rather peculiar. It all depends on the need of your business operation.

In the case of a third-party solution — you get a full package with a bob and bunny. It is ready-to-use and guaranteed to operate adequately. But there is one essential thing to note. When you implement a third-party solution — you pay for lots of features that you might not need at all. That is not exactly cost-effective. In fact, it can actively bleed your marketing budget if the turnaround will be big enough.

A custom solution is a more complicated but more reasonable approach. Sure, you need to do the heavy lifting of developing the foundation. But you create the platform precisely fitting to your Ad Tech operation’s needs, which makes it far more effective and capable of generating rapid ROI. However, in the case of self-hosted DMP, you need to find third-party data providers on your own.

Use of DMP in Ad Tech


Retargeting delivers relevant advertising content to the users based on a digital footprint and collected user data such as preferences and on-site behavior.

Retargeted ad content is based on the users’ current interests calculated out of their behavior on the source site. This makes ad content significantly more relevant to the users. That peculiar detail ups the chances of getting those sweet conversions, i.e., purchases or downloads.

Data Analytics

You can’t build an ad campaign without knowing how your target audience perceives your brand, behaves on your websites, and consumes your ad content. That is what Data Analysis is for.

DMP is useful for Data Analytics because of its scope. You get the big picture, and thus you can act accordingly. With a steady stream of data going through DMP, you can easily spot every little change in behavior, all while discovering trends and preferences, points of drop-offs, and so on.

That gives you a critical advantage as you can adjust on the fly without bouncing off.

Audience Research

Audience Research is such a significant element of Data Analytics that it deserves a different spot.

The thing is — targeting requires very clearly defines customer identity to click. Since users are not bound to use only one type of device — they are often visiting one place over multiple devices. All those visits can be processed as separate ones, which is not very helpful.

However, with a little help of DMP, you can construct a unified cross-device user profile (AKA single customer view) and target particular platforms he is using. How? DMP is matching cookies coming from the user and assigns them to a single profile.

SEO Optimization

Another vast area where the Data Management Platform is instrumental is SEO optimization. How? The whole audience research thing can be used not only for more efficient ad content delivery for your internal needs.

DMP helps on three SEO fronts:

  1. Content — a better understanding of what your target audience is interested in;
  2. Keyword Research — profiles can help to find more fitting keywords for content;
  3. Link Building — profiles can help to find better spots for guest posts;

Data Monetization

There is also another method of effective use of DMP. You can just gather information and sell it to other companies — i.e., you can be a third-party data provider.

In that case, you don’t need to worry about anything and just maintain a steady flow of data.

How DMP works?

Data Collecting

The initial stage of DMP operation is to collect data from the selected sources (i.e., first-party) and implement data from second and third-parties.

In the case of first-party data, the operation is performed in a variety of methods. Let’s count them down:

  • via Tags — with a little help of Tag Manager (Google’s is a fine one), you can insert snippets of code into a website’s pages that will be tracked according to determined function;
  • Cookies via Cookie Syncing — mapping and unifying user’s ID over the multiple platforms
  • Pixel Tracking
  • Integration with second-, third-party data suppliers;

Data segmentation

The next stage of DMP Operation is Data Segmentation. Once information is gathered — DMP organizes it according to the present taxonomy. It includes a variety of parameters. Some of them involve user’s personal data. Others have data regarding their interaction with monitored entities.

Segmentation taxonomy is wholly dependent on the selected marketing model and includes only those elements that are vital to efficient targeting.

Data Analysis

Once data is segmented, it is processed to construct a clearly defined customer profile for targeting.

Usually, this operation involves analyzing users’ past activity on-site, their events and impressions (clicks, etc.), preferences, and response to ads.

Data transfer

Once information is gathered, segmented, and analyzed — it can be transferred to ad exchanges, Supply-Side Platform (SSP), and Demand-Side Platform (DSP), which will deliver the goods to the advertisers.

This information will help to perform more accurate ad buys during real-time bidding operations.

Challenges with DMP


Before the whole data management starts — you need to set up the connection between sources. Your ultimate goal is to maintain a steady transmission of data from multiple sources without missing a beat and stumbling into a mess.

To make that happen — you need to be sure that all the sources meet system requirements and are compatible with others.

Dealing with Scalability

DMP is as good as its scaling capacity. That is one element essential to its successful operation. The thing is — standard Ad Tech operations consist of millions upon millions of various events happening on sites. And every single bit of this information must be collected.

And if the system can’t handle such workload — troubles ensue.

One of the most effective solutions for DMP scalability is to use a cloud platform. The majority of services provide automatic scaling features that will seriously ease up the challenge.

Data Storage

Managing data is one thing. But you also need to store it somewhere, and that is a challenge. You have an infinite stream of incoming information. It is continuously collected, processed, segmented, and transferred. You have data in active use and data that was already used. All these things have to be stored safely.

One solution is to have your server network. It is not exactly cost-effective, but it can be.

A much more feasible solution is by maintaining the data in cloud storage. Since you have to pay only for used space — it is more or less cost-effective. It also covers the necessity of maintaining backup storage as the cloud is an ultimate backup.

To make sense of storage spending, you need to apply a multi-faceted approach. In essence, it is further segmenting of the data according to its current value and relevance. In that case, that challenge is an incorrect definition of time windows for data transfer.

Refining Automation

DMP deals with large quantities of data from various sources — which creates a necessity of creating a set of automated scenarios that will handle the operation with its settings. We’re talking about millions of events per second — there is no chance a human being will be able to deal with it manually.

On the other hand, automation is far more reliable in comparison with an error-prone manual approach.

The challenge comes in determining where automation is necessary and where it might be abundant or ineffective.

Woes of Data Analysis

The thing with Data Analysis is that you have to know what you are studying data for in the first place to make use of it. If you don’t know what kind of data you need — you will end shooting in the dark, which is not exactly the most effective use of time and money.

To understand the most effective and feasible approach to analyzing incoming data, you need to understand the nature of the data sources and their credibility. Next go audience research, which gives you an understanding of target audience attitude, and then comes specification of data segmenting according to audience research.

Each step of the way must be thorough and through and through. The price of insufficient or downright faux data can be utterly devastating to the company.

Personal Data, Privacy & GDPR

The full adoption of GDPR is a game-changing moment for the Ad Tech industry.

On the one hand, GDPR’s expansion of the definition of user’s data is forcing drastic changes of approaches to deliver ad content. On the other, it is a chance to bring transparency and trust to a somewhat murky realm of Ad Tech.

In the long run, GDPR will turn Ad Tech’s use of DMP on its head. The thing is — GDPR compliance is not a joke.

Technically, that means you need to ask yourself the following questions:

  1. What kind of data are you going to collect and from what sources?
  2. Where is collected data going to be stored? How long is it going to be held?
  3. Who will have access to it?
  4. Are third-parties involved in the operation privacy-compliant?

Another important thing is to have a system that will store information about the use of data. That is a critical factor in building a fully transparent and trustworthy operation.


At the moment, Data Management Platforms are one of the most effective ways of delivering quality ad content to the target audience.

However, DMP is an entity that requires to be handled with care.

If done right, DMP is smooth and precise and helps keep the pulse on the marketing campaigns’ proceedings.

We hope this article explained what’s, how’s and why’s regarding this technology.