Inhouse Data Management Platform – Visualization 

 Challenge & Selection 

 I used the Visualization tool to get help with Visual Thinking , for understanding the benefits of having an   inhouse developed and owned Data Management Platform and to identify and define possible   challenges that we could get over just by having a unique data as being a mobile network operator . I use   this technique with my upper management to convince them to invest on a long term platform ,   including development and data fine tuning tasks , which are heavy effort for any company that has this   size of data and number of customers . Thus , the position of a DMP in digital advertising ecosystem is   very critical for the future of this business . 

 Application 

 First of all , every stakeholder should align on , where does DMP fit in all digital advertising ecosystem ,   from advertisers , to agencies , to adplatforms ( DSP and SSPs ) and publihers .. While big data is   instrumental to effective data - driven marketing campaigns , you ca n’t do much with the raw   information . You need it sorted and converted into a usable form , at least so you can understand what   you ’re looking at . This is the power of a DMP . 

 A DMP collects and organizes data from a variety first- , second- and third - party data sources , and makes   it available to other platforms such as DSPs , SSPs , and ad exchanges to be used for targeted advertising ,   personalization , content customization and beyond . Some people describe a data management platform   as the “ pipes ” of ad tech — connecting many platforms in a neutral way so marketers can use their   powerful audience data when and where they want . 

 This first - party data — that is the data you own and have collected directly from your own customers —   can be collected based on specific behaviors such as clicks , downloads , video uploads or video   completions , interests like sports , football , parenting , museums and travel or demographic information .   It can also include demographic data , socio - economic data , influencer and action data . As an example , a   business can use a DMP to collect and organize data , then use that data to target a particular ad to   moms who are 25 - 35 , there are endless applications of a DMP . 

 Once the first - party data is collected , it is organized into a series of segments called a “ hierarchy ” , which   can change based on each end user ’s business models . A large publisher network may have their   hierarchy divided up into different buckets based on each of the individual websites they own . An   agency can have separate accounts for each of their advertiser clients . Marketers could manage   different brands ’ data separately , while also having an overall holistic view of the data at the top level . 

 If appropriate to the situation , then I will create a visual , either based on a high - level process flow with   swim lanes for different stakeholders , or another format . 

 The final step is to activate the data , by putting it to work ! This activation step relies on the DMP having   integrations and open APIs with other platforms , so that the audiences you build in the DMP can be   seamlessly transported to DSPs , SSPs , and beyond . The most common DMP use case is running a   targeted campaign to a specific audience via a DSP . Or , you can connect the DMP to your content   management system ( CMS ) to adjust the content of your website for certain audience groups . The   possibilities and use cases for data activation with a DMP are limitless . 

 Insight & Approach 

 What I have learned from conducting this exercise with several groups is that once we ’ve collected and   organized our data , it ’s time to build unique audiences in DMP so we can identify most valuable   segments and use them across any digital channel . For any particular targeted campaign , a marketer can   predefine the target audience for that campaign based on data gathered from your DMP audience . So   instead of choosing to spend your advertising dollars on a particular financial website to reach an   audience of folks interested in finance , you can instead build a financial audience based on actual   behaviors across the wider Internet . 

 Audiences can be built inside the data management platform using any combination of first- , second-   and third - party data sources , which are combined using Boolean logic , which breaks down all values into   either true or false . You can build as niche an audience as you wish , and are only really limited by the   scale you need . Generally , the more niche the audience , the smaller the size . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Inhouse Data Management Platform – Visualization 

 Challenge & Selection 

 I used the Visualization tool to get help with Visual Thinking , for understanding the benefits of having an   inhouse developed and owned Data Management Platform and to identify and define possible   challenges that we could get over just by having a unique data as being a mobile network operator . I use   this technique with my upper management to convince them to invest on a long term platform ,   including development and data fine tuning tasks , which are heavy effort for any company that has this   size of data and number of customers . Thus , the position of a DMP in digital advertising ecosystem is   very critical for the future of this business . 

 Application 

 First of all , every stakeholder should align on , where does DMP fit in all digital advertising ecosystem ,   from advertisers , to agencies , to adplatforms ( DSP and SSPs ) and publihers .. While big data is   instrumental to effective data - driven marketing campaigns , you ca n’t do much with the raw   information . You need it sorted and converted into a usable form , at least so you can understand what   you ’re looking at . This is the power of a DMP . 

 A DMP collects and organizes data from a variety first- , second- and third - party data sources , and makes   it available to other platforms such as DSPs , SSPs , and ad exchanges to be used for targeted advertising ,   personalization , content customization and beyond . Some people describe a data management platform   as the “ pipes ” of ad tech — connecting many platforms in a neutral way so marketers can use their   powerful audience data when and where they want . 

 This first - party data — that is the data you own and have collected directly from your own customers —   can be collected based on specific behaviors such as clicks , downloads , video uploads or video   completions , interests like sports , football , parenting , museums and travel or demographic information .   It can also include demographic data , socio - economic data , influencer and action data . As an example , a   business can use a DMP to collect and organize data , then use that data to target a particular ad to   moms who are 25 - 35 , there are endless applications of a DMP . 

 Once the first - party data is collected , it is organized into a series of segments called a “ hierarchy ” , which   can change based on each end user ’s business models . A large publisher network may have their   hierarchy divided up into different buckets based on each of the individual websites they own . An   agency can have separate accounts for each of their advertiser clients . Marketers could manage   different brands ’ data separately , while also having an overall holistic view of the data at the top level . 

 If appropriate to the situation , then I will create a visual , either based on a high - level process flow with   swim lanes for different stakeholders , or another format . 

 The final step is to activate the data , by putting it to work ! This activation step relies on the DMP having   integrations and open APIs with other platforms , so that the audiences you build in the DMP can be   seamlessly transported to DSPs , SSPs , and beyond . The most common DMP use case is running a   targeted campaign to a specific audience via a DSP . Or , you can connect the DMP to your content   management system ( CMS ) to adjust the content of your website for certain audience groups . The   possibilities and use cases for data activation with a DMP are limitless . 

 Insight & Approach 

 What I have learned from conducting this exercise with several groups is that once we ’ve collected and   organized our data , it ’s time to build unique audiences in DMP so we can identify most valuable   segments and use them across any digital channel . For any particular targeted campaign , a marketer can   predefine the target audience for that campaign based on data gathered from your DMP audience . So   instead of choosing to spend your advertising dollars on a particular financial website to reach an   audience of folks interested in finance , you can instead build a financial audience based on actual   behaviors across the wider Internet . 

 Audiences can be built inside the data management platform using any combination of first- , second-   and third - party data sources , which are combined using Boolean logic , which breaks down all values into   either true or false . You can build as niche an audience as you wish , and are only really limited by the   scale you need . Generally , the more niche the audience , the smaller the size .
### Output: 