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  • Tejasvi A

Data protection can be a cultural design change through sustainable leadership

The practice of “Privacy by Design” has been embraced for some years now, and the data protection laws including GDPR have formalized the sample principles in protecting the rights of customers whenever new capabilities are developed in an organization.

While there is an increase in interest in innovation around privacy while designing for a new solution, there is a need for practical guidance to incorporate these principles into your software, data, or process development life-cycle.

Data Management and Data Governance enable us to harness the right data fit to raise an organization’s confidence and trust in its data while data protection ensures that the controls are available to protect personal data. There can be added benefits when the privacy control environment is achieved through data governance.



There is a definite value associated with leveraging the right data and at the same time, there is also privacy risk associated with data and its operations. Capability-Based Planning is a widely embraced technique in defense and other spaces like Data Management as well, to plan for any strategic changes. The analysis used in this technique deal with the uncertainty of the outcomes, estimation of risk, and the way of making choices that deliver required benefits.

The principle of protection-by-design is to manage uncertainty up front rather than to discount it; this approach further assists in expressing and managing risks that are highly apt for the scenarios that simulate data-breaches.

If an organization can standardize its data planning, all the involved people working on a change will be aware of the data-capabilities in a project or solution. You must hear this often – the acronym POSMAD signifies the six phases of data lifecycle – Plan, Obtain, Store, Share, Maintain, Apply, Dispose. The project management office can further assist in integrating the data lifecycle with the project lifecycle – be it waterfall, agile, or scrum. I have detailed this in my book “Data Management and Governance Services – Simple and Effective Approaches.”

  1. Institutionalize active management of data privacy requirements

  2. Establish a documented way to incorporate your data privacy requirements during planning for a change. One can call this out in non-functional requirements with a separate section for data privacy.

  3. Non-functional requirements are specifications that specify criteria used in setting expectations on operations for existing or new capabilities instead of specific behavior as is the case with functional requirements.

  4. They are sometimes known by other names, including System privacy specifications, protection Requirements, or protection characteristics.

  5. This approach prevents privacy breaches before they manifest. Because privacy has been integrated into the solution, security controls can be traced with the privacy specifications through the design process.


  • Scope high-level data domains against data-lifecycle and purpose

  • It’s as simple as using data from a customer, marketing, or finance domain and further analyzing the lower-level data as you go through the process.

  • Then, label the dataset for the planned activity like storage, distribution, and application.

  • Further, classify a domain or dataset based on the privacy risk – If personal data of customers is being planned for in a solution.

  • A Glossary can be an essential tool to manage domains, definitions, and privacy classifications along with the data elements associated with the domains.


  • Identify an enterprise privacy modeling tool to enable your change planning

  • Managing privacy and protection requires integration with the Enterprise-data-planning phase.

  • As data elements related to a change are scoped, their association with the purpose has to be documented (as a regulatory ask or business interest, etc). It will be helpful if this documentation is maintained live in a tool meant for enterprise privacy planning.

  • Further classification as mandatory or good-to-have can assist in a data-minimization activity

  • End-to-End data lineage of personal data can assist in understanding the impact of changes on the landscape.


  • Plan for archival, disposal of data while planning for data

  • Elicit the standard time, for which you want to have mastered, transactional data active on your databases while defining policies for archival and disposal.

  • These aspects can be recorded in the catalog as well.


  • Architect for data and integrate privacy from start

  • If you want to leverage Machine learning capabilities on the cloud, a best practice is to funnel minimal personal data that is required for modeling rather than having to re-host the entire domain on cloud storage.

  • “Bring-it-as-you-want” to a warehouse or lake is a good practice for personal data.

  • Masking, Anonymization, column-level encryption at rest, or pay-load encryption in motion along with key-management for the cloud, are suggested controls while architecting platforms or systems. Planning for these aspects up-front can reduce the risk of data breaches.

For further reading, this post is first published on Thriveglobal - https://thriveglobal.com/stories/data-privacy-by-design-and-aligning-it-to-enterprise-data-planning/

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