IQ International Webinars:
Data Quality integration to manage Data Privacy better
Today, the current need of organizations is to de-lineate Data Privacy from Information Privacy to achieve compliance with regulations like GDPR and to manage data related risks better. Further, Data Privacy services need to be set up and aligned with the Chief Data Office. In this webinar, we would look at the principles of data privacy to understand the level of integration required with Data Quality. Further, we will also understand the dimensions of data quality that are necessitated by Privacy management.
This webinar will discuss
- De-lineating Data Privacy and Information Privacy
- Data Quality needs to manage Data Privacy
- Achieving Cross Enterprise Governance between Dimensions
- Integrating Data privacy into Data or Risk Management
- Practical advice on certain challenges
17 January 2017
Does a profile for a privacy classified data element need to be stored in metadata repository.
The data element can be looked up for it's characterstics using querying or profiling capabilities. The profile of the data element needs to be defined from the characterstics (what can be called as required mix, max values or currency of the data required, timeliness or precision). This can be embedded in the meta-model or can be linked to a tool where the profile can be stored.It is not required to store the characteristics of the data as and when the data quality rules are run and would be an overkill.
Along with the thresholds, the data rules including data quality rules need to be stored in the data dictionaries. Most meta-models leverage this for the extended use for specializing the data rules for a project or context like reporting or BI.
What happens to data that has been collected without customer consent
If data is collected without consent, the same needs to be decayed. If there is historical customer PII data existing with loss of consents but there is a need to apply it for a purpose; a privacy notice for consent can be sent to the customer. Based on the consent the data needs to be applied or decayed but restrictions might apply on storing data without consents. One can refer to Gramm–Leach–Bliley Act (GLBA) for more information.
If Data is aggregated - how does classification work in this scenario.
Let us think that in this scenario, privacy classification for a data element like SSN or Address has been labeled as "Highly Restricted" or "Direct client identifying". One can apply the highest classification when data is aggregated in context or use a weighted criteria. The controls as requirements from Privacy, thereby will apply.
About the Author
Tejasvi Addagada is a Data Management and Governance consultant with nine years of success in assisting clients, develop and optimize data governance solutions. Tejasvi has worked with the top ten major global financial service providers by providing a wide range of services including strategy analysis, data analysis & architecting, service rationalization, digital transformation and process excellence. Tejasvi has diverse experience in the areas of consumer banking, commercial banking and capital markets. He has served as a process and domain consultant for the top ten major banks while receiving several accolades for the success achieved.
Now, he is delivering his services to a major investment management firm as a principal data consultant in the Data Governance Office. Before this stint, he has worked with two major banks in setting up and orchestrating data governance services from grass roots.
Tejasvi is an avid thought leader in the industry. He likes to connect with other thought leaders while blogging on the technological and business advances. His write ups address common challenges and opportunities that the firms need to tackle to make their way forward in the industry. Apart from that, he is an abstract painter and an art enthusiast and likes to spend time with family.
Tejasvi can be reached at Tejasvichand@gmail.com
About the Author
Robin Rappaport is the Data Quality Team Leader responsible for delivery of the Data Quality Initiative for Research Databases at the Internal Revenue Service (IRS). Her work and that of her team contributed to the IRS being awarded a Computerworld Honor and a Government Computer News (GCN) Gala Award. She has over 25 years of experience as a Data Quality practitioner. Her undergraduate degree was in Economics with Computer Science. Her graduate work was in Operations Research with a concentration in Mathematical Modeling in Information Systems. She has worked in both private (6 years) and public sectors (since June 1990). Her positions include Computer Programmer, Systems Analyst, and Operations Research Analyst.
In addition to IQ International, the International Association for Information & Data Quality, she is a member of the Institute for Operations Research and Management Science (INFORMS). She was Chairman, Individual Membership for the Washington, D.C. chapter from 1987- 1990. She was elected Secretary and served from 1990 - 1991.
Contact Robin by email at robin [dot] rappaport [AT] iaidq [dot] org
About IQ International Webinars:
An IQ International Webinar is a moderated webinar open to all, designed to serve as discussion platform for the data and information quality community. The webinars are supported and organized by IQ International -- the International Association for Information and Data Quality. IQ International seeks to engage experts with experience and expertise in the field of data and information quality.
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