Data Management Platform (DMP) - Revision history

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A '''Data Management Platform''' or '''DMP''', is a [[Software|software]] tool used primarily in [[Advertising|advertising]] and [[Marketing|marketing]] to build profiles of anonymous individuals, store summary [[Data|data]] about each individual, and share their data with advertising systems.<ref>What Is a DMP (Data Management Platform)? [https://www.treasuredata.com/glossary/what-is-a-data-management-platform-dmp/ Treasuredata]</ref>

== What a Data Management Platform Does<ref> What Do You Do With a Data Management Platform? [https://www.lotame.com/what-is-a-data-management-platform/ Lotame]</ref> ==
What you can do with all that data you collect? Here are just a few things you can focus on and what you can do with them:
*Audience targeting and targeted advertising: Specify your audience and target their interests and needs via video, visuals and content
*Content and product recommendations: Deliver personalized experiences for web and mobile users
*TV DMP: Match your audiences across TV & digital so you can reach the same audience when and where they are ready to buy
*Monetizing or selling data: Sell your valuable data for additional revenue
*Audience enrichment: Learn more about your audience, beyond what they do when they’re on your website or other properties.
*Grow your audience or customer base: Find a healthy supply of new customers to build brand loyalty
*Paid search: Use DMP-driven audiences to target, suppress or dynamically update paid search campaigns
*Paid social: Execute DMP-driven audience buys within social environments using Facebook and Twitter’s respective custom audience solutions

=== What a Data Management Platform Does Not Do<ref>What don’t DMPs do? [https://www.gartner.com/en/marketing/insights/articles/how-does-a-data-management-platform-work Gartner]</ref> ==
DMPs perform some of the same functions as other marketing technologies, like [[Data Analytics|data analytics]] platforms, demand-side platforms or [[Customer Data Platform (CDP)|customer data platforms]]. However, there are important differences.
*DMPs don’t perform the same breadth and depth of analysis as stand-alone data analytics platforms because the technology only gathers certain kinds of data and only analyzes ad performance from digital channels.
*DMPs can’t operate ad campaigns on their own, either. They connect with demand-side, supply-side or media platforms, which serve the ads. In fact, DMPs are often embedded in solutions like marketing cloud platforms, adtech platforms or media ecosystems as one component of these larger platforms.
Given their strengths and limitations, DMPs can be a key tool to enable more targeted and personalized ad campaigns. Many marketers already have them in their technology mix — although some may not know it. Before considering a new investment, “Contact your digital marketing, media or other agency partners who may already be using a DMP on your behalf to understand whether it is being fully used,” says Eric Schmitt, Senior Director Analyst at Gartner.

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 *'''First & Second Generation Programming Languages:''' During the 1950s, [[Data Management|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 Language (HLL)|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 Database|relational databases]], improved [[Data Processing|data processing]] and decreased duplicated data. This relational model allowed large amounts of data to be processed quickly and improved [[Parallel Processing|parallel processing]], [[Client Server Model|client-server computing]], and [[Graphical User Interface (GUI)|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.
+*'''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 Database|relational databases]], improved [[Data Processing|data processing]] and decreased duplicated data. This relational model allowed large amounts of data to be processed quickly and improved [[Parallel Processing|parallel processing]], [[Client Server Architecture|client-server computing]], and [[Graphical User Interface (GUI)|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 [[Customer|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.

← Older revision		Revision as of 16:37, 3 December 2021
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−	[[File:Data Management Platform.png\|~~400px~~\|Data Management Platform]]	+	[[File:Data Management Platform.png\|500px\|Data Management Platform]]

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 == History of DMP<ref>History of Data Management Platforms [https://en.wikipedia.org/wiki/Data_management_platform Wikipedia]</ref> ==
 *'''First & Second Generation Programming Languages:''' During the 1950s, [[Data Management|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 Language (HLL)|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.
+*'''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 Language (HLL)|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 Database|relational databases]], improved [[Data Processing|data processing]] and decreased duplicated data. This relational model allowed large amounts of data to be processed quickly and improved [[Parallel Processing|parallel processing]], [[Client Server Model|client-server computing]], and [[Graphical User Interface (GUI)|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.
+*'''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 Database|relational databases]], improved [[Data Processing|data processing]] and decreased duplicated data. This relational model allowed large amounts of data to be processed quickly and improved [[Parallel Processing|parallel processing]], [[Client Server Model|client-server computing]], and [[Graphical User Interface (GUI)|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 [[Customer|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.
+*'''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 [[Customer|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.
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 == DMP Vs. DSP<ref>DMPs “talk” to DSPs [https://martech.org/what-is-dmp-martech-landscape/ Martech]</ref> ==
 Data management platforms are where [[Customer Intelligence (CI)|audience intelligence]] data is stored, analyzed and segmented. Demand side platforms (DSPs) are the software that actually execute programmatic ad buys. A DMP will pass audience segment data to the DSP for ad targeting. The DMP then continues to pull in performance results of those segments, analyzes which audiences are performing well or poorly and feeds that information back to the DSP. The DSP uses that to optimize ongoing campaign bidding and targeting.

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 [[File:Data Management Platform.png|400px|Data Management Platform]]
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 But, what if you could imitate a Kaleidoscope to create one single view of your customer across the entire buyer journey? Well, you can. The solution is to implement a Data Management Platform that will allow you to break the silos and unify all your data in one place.
 == DMP Vs. DSP<ref>DMPs “talk” to DSPs [https://martech.org/what-is-dmp-martech-landscape/ Martech]</ref> ==
 Data management platforms are where [[Customer Intelligence (CI)|audience intelligence]] data is stored, analyzed and segmented. Demand side platforms (DSPs) are the software that actually execute programmatic ad buys. A DMP will pass audience segment data to the DSP for ad targeting. The DMP then continues to pull in performance results of those segments, analyzes which audiences are performing well or poorly and feeds that information back to the DSP. The DSP uses that to optimize ongoing campaign bidding and targeting.

← Older revision		Revision as of 16:17, 3 December 2021
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	But, what if you could imitate a Kaleidoscope to create one single view of your customer across the entire buyer journey? Well, you can. The solution is to implement a Data Management Platform that will allow you to break the silos and unify all your data in one place.		But, what if you could imitate a Kaleidoscope to create one single view of your customer across the entire buyer journey? Well, you can. The solution is to implement a Data Management Platform that will allow you to break the silos and unify all your data in one place.
		+
		+
		+	== DMP Vs. DSP<ref>DMPs “talk” to DSPs [https://martech.org/what-is-dmp-martech-landscape/ Martech]</ref> ==
		+	Data management platforms are where [[Customer Intelligence (CI)\|audience intelligence]] data is stored, analyzed and segmented. Demand side platforms (DSPs) are the software that actually execute programmatic ad buys. A DMP will pass audience segment data to the DSP for ad targeting. The DMP then continues to pull in performance results of those segments, analyzes which audiences are performing well or poorly and feeds that information back to the DSP. The DSP uses that to optimize ongoing campaign bidding and targeting.

@@ Line 36: / Line 36: @@
 === What a Data Management Platform Does Not Do<ref>What don’t DMPs do? [https://www.gartner.com/en/marketing/insights/articles/how-does-a-data-management-platform-work Gartner]</ref> ===
-DMPs perform some of the same functions as other marketing technologies, like [[Data Analytics|data analytics]] platforms, [[Demand Side Platform (DSP)|demand-side platforms]] or [[Customer Data Platform (CDP)|customer data platforms]]. However, there are important differences.
+DMPs perform some of the same functions as other marketing technologies, like [[Data Analytics|data analytics]] platforms, demand-side platforms or [[Customer Data Platform (CDP)|customer data platforms]]. However, there are important differences.
 *DMPs don’t perform the same breadth and depth of analysis as stand-alone data analytics platforms because the technology only gathers certain kinds of data and only analyzes ad performance from digital channels.
-*DMPs can’t operate ad campaigns on their own, either. They connect with demand-side, supply-side or media platforms, which serve the ads. In fact, DMPs are often embedded in solutions like marketing [[Cloud Computing|cloud]] platforms, [[Adtech (Advertising Technology)|adtech]] platforms or [[Media Ecology|media ecosystems]] as one component of these larger platforms.
+*DMPs can’t operate ad campaigns on their own, either. They connect with demand-side, supply-side or media platforms, which serve the ads. In fact, DMPs are often embedded in solutions like marketing cloud platforms, adtech platforms or media ecosystems as one component of these larger platforms.
 Given their strengths and limitations, DMPs can be a key tool to enable more targeted and personalized ad campaigns. Many marketers already have them in their technology mix — although some may not know it. Before considering a new investment, “Contact your digital marketing, media or other agency partners who may already be using a DMP on your behalf to understand whether it is being fully used,” says Eric Schmitt, Senior Director Analyst at Gartner.

← Older revision		Revision as of 16:00, 3 December 2021
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−	A '''Data Management Platform''' or '''DMP''', is a [[Software\|software]] tool used primarily in [[Advertising\|advertising]] and [[Marketing\|marketing]] to build profiles of anonymous individuals, store summary [[Data\|data]] about each individual, and share their data with advertising systems.<ref>~~What Is a~~ DMP (Data Management Platform)? [https://www.treasuredata.com/glossary/what-is-a-data-management-platform-dmp/ Treasuredata]</ref>	+	A '''Data Management Platform''' or '''DMP''', is a [[Software\|software]] tool used primarily in [[Advertising\|advertising]] and [[Marketing\|marketing]] to build profiles of anonymous individuals, store summary [[Data\|data]] about each individual, and share their data with advertising systems.<ref>Definition of DMP (Data Management Platform)? [https://www.treasuredata.com/glossary/what-is-a-data-management-platform-dmp/ Treasuredata]</ref>
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		+	__TOC__
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		+	== What is a Data Management Platform (DMP)<ref>What is a Data Management Platform (DMP) [https://liveramp.com/blog/what-is-dmp-data-management-platform-definition/ Live Ramp]</ref> ==
		+	According to Digiday, “In simple terms, a DMP is a [[Data Warehouse\|data warehouse]], a piece of software that sucks up, sorts, and houses [[Information\|information]] and spits it out in a way that’s useful for marketers, publishers, and other businesses.” A data management platform is an execution hub where audiences are collected and marketing campaigns are launched, analyzed, and optimized. This happens through three steps:
		+	*Aggregating Data: This type of platform imports and houses lots of information (the data). Primarily, they manage digital audience data like cookies, online behavioral data, and look-alike audiences. Most offline data (including your CRM, point-of-sale systems, subscription list, and third-party data providers) can be onboarded onto these platforms, but that requires external technology to do so—like an identity resolution provider.
		+	*Audience segmentation: Segments are built from that data. These segments are the cornerstone of people-based marketing, allowing marketers to group people sharing certain characteristics and target each in specific ways.
		+	*Analyzing and optimizing: After building segments, these platforms are able to manage and present all campaign activity and audience data based on those segments to help optimize present campaigns and establish best practices for future campaigns.
		+	They help enable data-driven marketing strategies, which allow for effective targeting and unique messaging. These principles matter because the more a consumer believes that a company knows their needs and wants, the more likely they are to engage.
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		+	[[File:Data Management Platform.png\|400px\|Data Management Platform]]