It’s Wednesday night, humpday, we are now midway through the working week. We are also more than halfway through exploring the 7 phases of data lifecycle management.
DLM Phase 4 is where we finally find value in the data that we have captured, maintain, and synthesize.
What are the different types of data usage?
Data for Business Use
Googling “data as the new currency” will yield many articles on the subject. The volume of search returns clearly show a trend that not only this concept is possible, but it is not that far away from becoming truth.
- “Any discussion about data must start with the realization that it is now an extreme disruption point on the path to progress.”
- “The digital universe is doubling in size every two years, and by 2020, it will reach 44 zettabytes, or 44 trillion gigabytes.”
- “Data as the new currency.”
The bottom line is clear, businesses require quality data to drive timely and accurate decision making processes: to identify opportunities, adapt to trends, identify and remediate challenges quickly to stay ahead of the competition.
And perhaps to generate its own currency. Imagine if you can legally print your own dollar bills…
Data for Regulatory Use
Organizations are required to submit regulatory reports to attest that they do business in compliance with regulatory requirements.
Examples: Tax reporting, K1 Reports, Anti Money Laundering attestations, Comprehensive Analytics Review (CCAR), etc.
Use of data is governed, enforced with consequence
Nations such as EU, US, Swiss, and many others have issued data protection requirements e.g. EUD GDPR was put in place to strengthen and unify data protection for individuals for all enterprises.
In the financial industry, regulatory bodies such as SEC, Federal Reserve, etc., are very specific in how data must be reported with accuracy, provenance, and quality.
Severe penalties are enforced to ensure compliance.
More to come on the subject of data for regulatory and operational use, we will also cover permitted use and data governance in later posts.
Hopefully you get a sense of where we are going with these series of data lifecycle management posts. These early posts are meant to lay the foundation and build a common reference of data lifecycle phases before we continue on with deeper discussions of enterprise data management.
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.