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TDAN (The Data Administration Newsletter
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The Data Administration Newsletter for all things data.
The Data Administration Newsletter for all things data.

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Privacy Engineering and Data Governance - Finneran, Dennedy, Fox - bit.ly/1Qq3cQF

Privacy engineering may be defined as using engineering principles and processes to build controls and measures into processes, systems, components, and products that enable the authorized, fair and legitimate processing of personal information. One privacy leader defines it as the inclusion and implementation of privacy requirements as part of systems engineering. How do you define privacy engineering?

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Business Glossaries and Metadata: Data Governance of Enterprise Reference Data – Why do we care?: Lowell Fryman - bit.ly/1TOEV7X

There is often a struggle managing Reference data as an enterprise data asset. Many organizations still view Reference Data as an individual application problem and thus Reference Data is often an application afterthought. Why should we care?

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Risk Data Aggregation and Reporting for BCBS 239 By Silvano Stagni - bit.ly/21zLlho

The lack of any prescribed reporting framework, workflow, or any other prescribed action is perhaps the most striking difference between BCBS 239 and any other set of rules. BCBS 239 is based on two concepts and has a number of stated objectives but achieving those objectives is left to the implementation of fourteen principles. Do you know of any others?

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How to approach Enterprise Content Management - Danny Greefhorst - bit.ly/1ngpjgI

The typical interpretation is that Enterprise Content Management (ECM) includes everything related to … managing content - from documents to images to messages on social media. How do you define ECM?

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How to Avoid the Requirement Trap By Jason Williscroft - bit.ly/1LSCRoO

There is major risk around gathering requirements. It manifests as an inability to articulate solid high-level requirements and decompose them into low-level requirements. This is the Requirements Trap. It is eminently avoidable, and the consequences of falling into it are severe. How do you avoid the Requirement Trap?

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Big Changes in the World of Data Modeling - Dave Wells - bit.ly/1Qpjshd

The real value of big data is in the new knowledge and understanding that can be gained by analyzing the data. But the most exciting thing about big data is that it is driving changes in the world of data modeling. The cahnge is not coming from the big data itself; it’s about a radically changed data pipeline and different kinds of data that don’t fit neatly into rows and columns. How do you think Big Data is changing Data Modeling?

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Mastering Your Data: An Algorithm to Clean Names By Hussein Fareed - bit.ly/1UwuoPv

It has been a great challenge in many companies to preserve name consistency with written Latin characters in countries where the main alphabet is not Latin. In this article the author offers a suggested algorithm to clean names. How have you resolved this issue?

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Pragmatic Data: Another Good Reason to Model Data - William Brooks - bit.ly/216J1Jb

At some point in your career you have probably heard the words, “why do I need a data modeler?” Perhaps it was in response to a budget estimate. Maybe someone just didn’t understand what data modeling meant for their project. Or you yourself might have been the one questioning the time and expense of formal data modeling. How do you answer the question?

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Thank Contributors Robert S. Seiner wishes to thank the following for their contributions to the March issue of TDAN.com. Authors: Jason Williscroft, Silvano Stagni. Hussein Fareed; Columnists: Daragh O'Brien, Dave McComb, Lowell Fryman, William Brooks; Feature Writer: Tom Finneran; Bloggers: Danny Greefhorst, Dave Wells. TDAN.com

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Start Data Governance by Protecting Your Data By Robert S. Seiner - bit.ly/1LtQujD

There are a lot of rules around protecting data. Everybody in your company needs to be aware that there are rules, be told the rules, be told how to follow the rules, and follow the rules. Sounds pretty easy. That is … unless your organization has never formally protected data before. Focusing on data protection is a great way to start a Data Governance program. How did you start your program?
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