Now that we have covered 4 Data Lifecycle Management phases:
Data Publication describes processes and activities when data moves from inside the enterprise, where it is controllable, to outside the enterprise, where management of data capabilities varies from less control to zero control.
The key concept here is the “management of data movement from internal to external.” This is where enterprise data integrity, accuracy, and resilience quality controls will be challenged by the public and regulatory authorities as well as those with malicious intent.
Below lists 5 ways of how data is published:
- People to System. People manually entering data into applications that capture the data into another enterprise system repository. Example: people entering SSN or EIN number into enterprise systems.
- unstructured data captured into structured data systems
- System to System. Systems that programmatically extract, transform, and load data from another system, or systems that capture data derived from one or more systems. Examples: ERP systems, CRM systems, machine learning systems, etc.
- structured data captured into structured data system
- System to People. Systems that allow people to extract data and transport it outside the enterprise. Examples: systems with online portals where people can view and download 10-K statements; systems that allow people to login to view bespoke data.
- structured data de-structured and shared to unstructured data applications
- People to People. People sharing enterprise data with other people via email, SMS, presentations, spreadsheets, word documents, etc.
- unstructured data shared to other unstructured data applications
- Data Breach. Unwanted data publication either through unintended system/person error or through malintent by hackers or data thieves.
Why do we care about the methods of publication?
We care about how data is published because the methods of control and quality assurance will depend upon the methods of publication. The tactics to ensure data is published with integrity and with compliance to regulatory requirements differs greatly based on the how data is moved from an environment controlled by the enterprise that generates the data to an environment that is controlled by the consumers of the data.
We will be covering the challenges and methods of data publication management in later discussions. The intent of this short post is to set the foundation to discuss a deeper dive later posts.
Leave your thoughts on the comments section. Or feel free to contact me at email@example.com
Happy to send an editable version of the 7 phases of a data lifecycle images, just send me an email with your request.
About the author:
Juliana Carroll is a problem solver, strategic thinker with 15+ years of Fortune 500 consulting experience delivering measurable results that align with both business competitive requirements and regulatory compliance.
Juliana delivered exceptional results for organizations including Morgan Stanley, Prudential, Merck, Guardian Life Insurance, Blue Cross Blue Shield of Florida, and Deutsche Bank.