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Difference between revisions of "Data Portability"

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===What is Data Portability<ref>[https://www.integrate.io/blog/what-is-data-portability-and-why-is-it-important/ Defining Data Portabilit]</ref>===
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'''Data Portability''' is the concept that the users or owners of a given dataset should be able to easily move or copy this data between different software applications, platforms, services, and computing environments. The term “data portability” actually encompasses two related, but separate, issues:
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*First, organizations should be able to easily import and export the data they collect and store, converting between different formats and standards if necessary.
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*Second, individuals should have the right to migrate their personal data between different providers or data processors.
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In general, the second concept of data portability (which is more philosophical) depends on the first (which is more technical). To provide consumers with their personal data, organizations must be able to efficiently migrate this data between different IT environments in the first place.
  
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The rise of cloud computing services has heightened technical concerns about data portability. In particular, many organizations are worried about potential “vendor lock-in,” where users feel stuck or trapped with a particular IT provider because of the costs of migrating their data to another provider. For example, an IT provider may store data in a proprietary format that makes it difficult to convert to another, more usable format.
  
  
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===The Importance of Data Portability<ref>[https://www.techtarget.com/searchcloudcomputing/definition/data-portability Why is portability of data important?]</ref>===
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Data portability has become commonplace - although not universal - among applications designed for use on many vendors' personal computers (PCs) and servers. The same cannot yet be said for Cloud Service Providers (CSPs). As more organizations move data and data processing to cloud services, a lack of data portability can cause problems if, for example, customers want to move data from one cloud platform to another or change their service provider.
  
  
  
== See Also ==
 
  
  
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*Different CSPs commonly have proprietary data formats, templates, and related parameters that can lock users into specific platforms. Often, these formats are not standardized, making data portability difficult. According to the Institute of Electrical and Electronics Engineers (IEEE), cloud interoperability and data portability are major challenges for enterprise adoption of cloud computing services.
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*For consumers, data portability lets people easily coordinate the personal data they keep on multiple social networking sites. On social networking sites, such as Facebook, LinkedIn, and Twitter, users can share their contacts, posts, photos, videos, sound clips and personal or professional information across the various platforms. In that way, users know their data is current and consistent, without having to modify the content on each service's site. Users can, of course, opt out of this data-sharing feature if they want to show different portfolios on different services.
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*In 2010, Facebook improved its data portability with a feature that lets users download all their network content as a single zipped file for viewing with a browser offline. This feature helps users to keep track of their data without fear that crackers might permanently alter or destroy it. The downloading feature backs up the data so it can be easily replaced in the event of a network failure causing data loss in the cloud. If the network has an outage or some other problem, users can simply upload their backed-up data to replace the damaged network data.
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*Data portability provides users of social networking services with added convenience when different services allow reciprocal access to first-party data. For example, a user on Facebook may import contacts from Google's Gmail email service. In a perfect world, all social networking services would allow users to freely and easily migrate data among them. Things haven't worked out that way. Instead, services sometimes take a territorial attitude toward user data.
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*Without data portability, a person's data is accessible only through the platform where it is stored. Such a siloed approach to data can result in vendor lock-in, inaccessible data, and even data quality issues.
  
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===See Also===
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<div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
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[[Data Compatibility]]<br />
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[[Data Access]]<br />
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[[Data Analysis]]<br />
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[[Data Analytics]]<br />
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[[Data Architecture]]<br />
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[[Data Asset Framework (DAF)]]<br />
 +
[[Data Buffer]]<br />
 +
[[Data Center]]<br />
 +
[[Data Center Infrastructure]]<br />
 +
[[Data Center Infrastructure Management (DCIM)]]<br />
 +
[[Data Cleansing]]<br />
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[[Big Data]]<br />
 +
[[Big Data Integration]]<br />
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[[Big Data Maturity Model (BDMM)]]<br />
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[[Metadata]]<br />
 +
[[Data Collection]]<br />
 +
[[Data Compatibility]]<br />
 +
[[Data Consolidation]]<br />
 +
[[Data Deduplication]]<br />
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[[Data Delivery Platform (DDP)]]<br />
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[[Data Description (Definition) Language (DDL)]]<br />
 +
[[Data Dictionary]]<br />
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[[Data Discovery]]<br />
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[[Data Driven Organization]]<br />
 +
[[Data Element]]<br />
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[[Data Enrichment]]<br />
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[[Data Entry]]<br />
 +
[[Data Federation]]<br />
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[[Data Flow Diagram]]<br />
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[[Data Governance]]<br />
 +
[[Data Health Check]]<br />
 +
[[Data Hierarchy]]<br />
 +
[[Data Independence]]<br />
 +
[[Data Integration]]<br />
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[[Data Integration Framework (DIF)]]<br />
 +
[[Data Integrity]]<br />
 +
[[Data Island]]<br />
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[[Data Item]]<br />
 +
[[Data Lake]]<br />
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[[Data Life Cycle]]<br />
 +
[[Data Lineage]]<br />
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[[Data Loss Prevention (DLP)]]<br />
 +
[[Data Management]]<br />
 +
[[Data Migration]]<br />
 +
[[Data Minimization]]<br />
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[[Data Mining]]<br />
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[[Data Model]]<br />
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[[Data Modeling]]<br />
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[[Data Monitoring]]<br />
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[[Data Munging]]<br />
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[[Data Portability]]<br />
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[[Data Preparation]]<br />
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[[Data Presentation Architecture]]<br />
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[[Data Processing]]<br />
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[[Data Profiling]]<br />
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[[Data Proliferation]]<br />
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[[Data Propagation]]<br />
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[[Data Protection Act]]<br />
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[[Data Prototyping]]<br />
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[[Data Quality]]<br />
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[[Data Quality Assessment (DQA)]]<br />
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[[Data Quality Dimension]]<br />
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[[Data Quality Standard]]<br />
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[[Data Reconciliation]]<br />
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[[Data Reference Model (DRM)]]<br />
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[[Data Science]]<br />
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[[Data Security]]<br />
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[[Data Stewardship]]<br />
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[[Data Structure]]<br />
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[[Data Structure Diagram]]<br />
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[[Data Suppression]]<br />
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[[Data Transformation]]<br />
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[[Data Validation]]<br />
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[[Data Value Chain]]<br />
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[[Data Vault Modeling]]<br />
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[[Data Virtualization]]<br />
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[[Data Visualization]]<br />
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[[Data Warehouse]]<br />
 +
[[Data Wrangling]]<br />
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[[Data and Information Reference Model (DRM)]]<br />
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[[Data as a Service (DaaS)]]<br />
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[[Database (DB)]]<br />
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[[Database Design]]<br />
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[[Database Design Methodology]]<br />
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[[Database Management System (DBMS)]]<br />
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[[Database Marketing]]<br />
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[[Database Schema]]<br />
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[[Database System]]<br />
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</div>
  
  
 
== References ==
 
== References ==
 
<references />
 
<references />

Revision as of 17:03, 17 October 2022

What is Data Portability[1]

Data Portability is the concept that the users or owners of a given dataset should be able to easily move or copy this data between different software applications, platforms, services, and computing environments. The term “data portability” actually encompasses two related, but separate, issues:

  • First, organizations should be able to easily import and export the data they collect and store, converting between different formats and standards if necessary.
  • Second, individuals should have the right to migrate their personal data between different providers or data processors.

In general, the second concept of data portability (which is more philosophical) depends on the first (which is more technical). To provide consumers with their personal data, organizations must be able to efficiently migrate this data between different IT environments in the first place.

The rise of cloud computing services has heightened technical concerns about data portability. In particular, many organizations are worried about potential “vendor lock-in,” where users feel stuck or trapped with a particular IT provider because of the costs of migrating their data to another provider. For example, an IT provider may store data in a proprietary format that makes it difficult to convert to another, more usable format.


The Importance of Data Portability[2]

Data portability has become commonplace - although not universal - among applications designed for use on many vendors' personal computers (PCs) and servers. The same cannot yet be said for Cloud Service Providers (CSPs). As more organizations move data and data processing to cloud services, a lack of data portability can cause problems if, for example, customers want to move data from one cloud platform to another or change their service provider.



  • Different CSPs commonly have proprietary data formats, templates, and related parameters that can lock users into specific platforms. Often, these formats are not standardized, making data portability difficult. According to the Institute of Electrical and Electronics Engineers (IEEE), cloud interoperability and data portability are major challenges for enterprise adoption of cloud computing services.
  • For consumers, data portability lets people easily coordinate the personal data they keep on multiple social networking sites. On social networking sites, such as Facebook, LinkedIn, and Twitter, users can share their contacts, posts, photos, videos, sound clips and personal or professional information across the various platforms. In that way, users know their data is current and consistent, without having to modify the content on each service's site. Users can, of course, opt out of this data-sharing feature if they want to show different portfolios on different services.
  • In 2010, Facebook improved its data portability with a feature that lets users download all their network content as a single zipped file for viewing with a browser offline. This feature helps users to keep track of their data without fear that crackers might permanently alter or destroy it. The downloading feature backs up the data so it can be easily replaced in the event of a network failure causing data loss in the cloud. If the network has an outage or some other problem, users can simply upload their backed-up data to replace the damaged network data.
  • Data portability provides users of social networking services with added convenience when different services allow reciprocal access to first-party data. For example, a user on Facebook may import contacts from Google's Gmail email service. In a perfect world, all social networking services would allow users to freely and easily migrate data among them. Things haven't worked out that way. Instead, services sometimes take a territorial attitude toward user data.
  • Without data portability, a person's data is accessible only through the platform where it is stored. Such a siloed approach to data can result in vendor lock-in, inaccessible data, and even data quality issues.


See Also

Data Compatibility
Data Access
Data Analysis
Data Analytics
Data Architecture
Data Asset Framework (DAF)
Data Buffer
Data Center
Data Center Infrastructure
Data Center Infrastructure Management (DCIM)
Data Cleansing
Big Data
Big Data Integration
Big Data Maturity Model (BDMM)
Metadata
Data Collection
Data Compatibility
Data Consolidation
Data Deduplication
Data Delivery Platform (DDP)
Data Description (Definition) Language (DDL)
Data Dictionary
Data Discovery
Data Driven Organization
Data Element
Data Enrichment
Data Entry
Data Federation
Data Flow Diagram
Data Governance
Data Health Check
Data Hierarchy
Data Independence
Data Integration
Data Integration Framework (DIF)
Data Integrity
Data Island
Data Item
Data Lake
Data Life Cycle
Data Lineage
Data Loss Prevention (DLP)
Data Management
Data Migration
Data Minimization
Data Mining
Data Model
Data Modeling
Data Monitoring
Data Munging
Data Portability
Data Preparation
Data Presentation Architecture
Data Processing
Data Profiling
Data Proliferation
Data Propagation
Data Protection Act
Data Prototyping
Data Quality
Data Quality Assessment (DQA)
Data Quality Dimension
Data Quality Standard
Data Reconciliation
Data Reference Model (DRM)
Data Science
Data Security
Data Stewardship
Data Structure
Data Structure Diagram
Data Suppression
Data Transformation
Data Validation
Data Value Chain
Data Vault Modeling
Data Virtualization
Data Visualization
Data Warehouse
Data Wrangling
Data and Information Reference Model (DRM)
Data as a Service (DaaS)
Database (DB)
Database Design
Database Design Methodology
Database Management System (DBMS)
Database Marketing
Database Schema
Database System


References