
Data management platform

A data management platform (DMP) is a software platform used for collecting and managing data. They allow businesses to identify audience segments, which can be used to target specific users and contexts in online advertising campaigns. DMPs may use big data and artificial intelligence algorithms to process and analyze large data sets about users from various sources. Some advantages of using DMPs include data organization, increased insight on audiences and markets, and effective advertisement budgeting. On the other hand, DMPs often have to deal with privacy concerns due to the integration of third-party software with private data. This technology is continuously being developed by global entities such as Nielsen and Oracle.
Characteristics.
Purpose.
A DMP is any kind of software that manages the gathering, storage, and organization of data so that useful information can be leveraged from it by marketers, publishers, and other businesses. The data stored may include customer information, demographics, and mobile identifiers or cookie IDs, which the DMP will analyze to allow businesses to create targeting segments for advertisements. DMPs can help brands learn more about their customer segments to inform acquisitions strategies and increase their sales. They also allow businesses to gauge the effectiveness of their advertising campaigns.
History.
First & Second Generation Programming Languages.
During the 1950s, data management became a problem for companies as computers were not quick with computations and needed a great amount of labor to deliver results. Companies started by storing their data in warehouses. Early programs were written in binary and decimal and this was known as Absolute Machine Language, which later was called the First Generation Programming Language. 
After this, Assembly Language - which came to be known as Second Generation Programming Languages - came into existence. This symbolic machine code grew popular among programmers as they were able to utilize alphabet letters for coding. This led to less errors in programs and improved code readability.
High Level Languages.
Throughout the 1960s and 1970s, as technology continued to progress and programmers became more in touch with computers, the First and Second Generation Programming Languages evolved into High Level Languages (HLL). These languages are known for being easily readable by a human and were important for allowing one to write a generic program that does not depend on the kind of computer used. HLL were known for emphasizing memory and data management and many of the languages that came out in this era (i.e. COBOL, C, and C++) are still widely used today.
Online Data Management & Databases.
Online transactions soon were a big part of many industries. This was possible by Online Data Management systems. These systems can analyze information quickly and they allow programs to read, update and send information to the user.
In the 1970s, Edgar F. Codd developed an easy-to-learn language, Structured Query Language (SQL) that had English commands. This language dealt with relational databases, improved data processing and decreased duplicated data. This relational model allowed large amounts of data to be processed quickly and improved parallel processing, client-server computing, and graphical user interfaces and it made multiple users to interact simultaneously.
To deal with the processing and research of Big Data, NoSQL came into existence. NoSQL's greatest power is its ability to store vast amounts of data. NoSQL was present in 1998, however its popularity among developers grew after 2005.
Cloud & AI.
Nowadays, data management has transferred over from local storage to the cloud. In the late 1990s and early 2000s, Salesforce and Amazon popularized the concept of internet-based services, which appealed to customers as it reduced in-house maintenance costs and increased flexibility in changing the needs of a business. With the rising prevalence of Artificial Intelligence (AI), it is now easier than ever to store and sort through immense sets of data. It is in this era that DMPs have experienced their rise to prominence as the astronomical amount of user data in the world can now be processed and presented to companies for marketing purposes.
Data Pipeline.
DMPs first begin by gathering raw data. This requires data collection from various sources such as webpage visits and registration forms found online. Moreover, the available data is not limited to one's computer, as mobile devices, social networks, and smart devices all act as constant sources of raw data. From a technical standpoint, JavaScript trackers and APIs are used to inform the server when the user is performing an action that should be recorded and stored.
Once the DMP has collected the data, it moves on to then process and integrate all of it. It first cleans the data by filtering out any junk or missing values. Then, it utilizes machine learning algorithms to find patterns across sets of users and organize them on a broad scale. This helps create a 360 degree customer view which in turn helps to integrate first-, second-, and third-party data types into one database.
Next comes the data management phase. Here, the DMP assists its clients - which are other companies seeking to leverage their user data - in creating user profiles. User profiles are segments of specific customer demographics that are intended to help visualize patterns and tendencies across an industry. They are also useful for shedding light on undiscovered market opportunities.
The final step in this process is the activation stage. Once all the data has been gathered, processed, and properly organized and segmented, it is put into use in the marketplace through servers or DSPs. From here, advertisers uses other third-party services to access a DMP and provide targeted content to their intended audiences.
Functionalities.
DMPs are used for profiling, analyzing, and targeting online customers in digital marketing. They work in the following areas:
Advantages & Disadvantages.
Advantages.
There are six major advantages to using a data management platform: gathering data in one place, using third party data to discover new markets, gaining audience insights, creating a full view of customers, targeting your audience, and effectively budgeting your expenditures on marketing.
Disadvantages.
Data management platforms rely heavily on Cookie technology to identify behaviours. Recent moves from Apple and now Google are moving towards blocking third party advertising cookies which places the Data management platform value proposition at risk.
Moreover, the adoption of such a platform may be difficult in an organization's current environment. This is because data aggregation technology is complex and requires the correct technical knowledge for implementation.
Another area of concern is the quality of the imported data: if it is of low quality, then the DMP will fail to provide meaningful results.
Ownership of Collected Data & Privacy Concerns.
There are three main types of data in general:
There are also three main types of data collected by DMPs:
DMPs are beneficial in helping digital marketers discover new audiences based on third-party data. Although this is the case, the General Data Protection Regulation (GDPR) makes it harder for DMPs to obtain third-party data. Previously, DMPs processed third-party data through cookies and existing laws did not require user consent for such data collection. However, the GDPR now demands that personal data - which includes data collected through the use of cookies - can only be used with user consent. Going forward, this means that collecting third-party data will become harder for companies, and DMPs will have greater legal obligations. As a result, future DMPs may rely more heavily on first-party and second-party data.