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How can you improve data privacy awareness in your company's architecture?

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Data privacy is a crucial concern for any company that collects, processes, or shares personal information. It can affect customer trust, regulatory compliance, and business reputation. However, data privacy is not only a matter of policies and procedures, but also of architecture. How can you improve data privacy awareness in your company's architecture? Here are some tips to help you design and implement a data architecture that respects and protects privacy.

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Michael Thieme

Passion for shaping trusted data products
CHARLES EVANS OGEGO OTIENO

C, C++, PHP, Javascript, Dart & Python ???? | Chess | ?? I focus on FinTech & Entertainment Apps

1 Assess your data privacy risks

Before you start designing your data architecture, you need to identify and evaluate the potential risks to data privacy in your context. This involves mapping your data flows, sources, and destinations, as well as the legal, ethical, and operational requirements and expectations of your stakeholders. You can use frameworks such as the Privacy Impact Assessment (PIA) or the Data Protection Impact Assessment (DPIA) to guide you through this process. By assessing your data privacy risks, you can prioritize the areas that need more attention and mitigation.

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Michael Thieme

Passion for shaping trusted data products
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For a sustainable management of data privacy risks, it is also advisable to use concepts such as #datamesh. By using #dataproducts in the context of data mesh, a structure can be achieved in a #dataarchitecture by dividing data into independent domains. This clearly defines business cases, as each domain is responsible for specific business processes. The precise separation of data areas minimises #dataprotection risks, as access to sensitive data is restricted to the respective domains. This also makes it possible to define appropriate measures to ensure the level of protection, as the data is linked to a business purpose.

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2 Apply privacy by design principles

Privacy by design is an approach that embeds privacy considerations into the design and development of systems, processes, and products that involve personal data. It means that privacy is not an afterthought, but a core value and a default setting. Some of the principles of privacy by design are: minimize the collection and retention of personal data, anonymize or pseudonymize data where possible, encrypt and secure data in transit and at rest, limit access and disclosure of data, and enable user control and consent over data.

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Michael Thieme

Passion for shaping trusted data products
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"#PrivacyByDesign" and "#PrivacyByDefault" are two important concepts for integrating #dataprotection into the data architecture. Privacy-by-design has already been described in this chapter. This could be applied, for example, when creating new #dataproducts, where privacy impact assessments (PIAs) could be carried out to identify potential risks and take appropriate risk mitigation measures. "Privacy-by-default" refers to the fact that the highest possible data protection standards should apply by default. This means that sensitive data should be automatically protected and only made accessible to authorised users. This can be achieved by implementing access controls, encryption and #anonymisation techniques.

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3 Use privacy-enhancing technologies

Privacy-enhancing technologies (PETs) are tools and techniques that help you implement privacy by design principles in your data architecture. They can help you reduce the amount and sensitivity of personal data that you collect and process, as well as protect the data from unauthorized or malicious access or use. Some examples of PETs are: differential privacy, which adds noise to data to prevent re-identification of individuals; homomorphic encryption, which allows computation on encrypted data without revealing the plaintext; and federated learning, which enables distributed machine learning without sharing raw data.

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4 Monitor and audit your data activities

Data privacy is not a one-time project, but a continuous process that requires regular monitoring and auditing. You need to keep track of your data activities, such as collection, processing, sharing, and deletion, and ensure that they comply with your privacy policies and regulations. You also need to detect and respond to any data breaches or incidents that may compromise data privacy. You can use tools such as data lineage, data quality, and data governance to help you monitor and audit your data activities.

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Michael Thieme

Passion for shaping trusted data products
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In addition to the actions mentioned in the "Monitor and review your data activities" section, it is advisable to carry out regular #dataprotection audits to ensure that the measures implemented are effective and comply with the applicable regulations. These audits can also help to uncover potential weaknesses in the #dataarchitecture and take appropriate countermeasures. Overall, it is important to consider data protection as an integral part of the data architecture and to continuously monitor and adapt it to ensure the protection of #personaldata.

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5 Educate and train your data team

Data privacy is not only a technical issue, but also a cultural and behavioral one. You need to educate and train your data team on the importance and best practices of data privacy, as well as the roles and responsibilities of each member. You also need to foster a culture of data privacy awareness and accountability in your company, where everyone understands the value and impact of data privacy on your customers, partners, and society. You can use resources such as online courses, workshops, webinars, and podcasts to help you educate and train your data team.

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6 Review and update your data architecture

Data privacy is not a static state, but a dynamic and evolving one. You need to review and update your data architecture regularly to reflect the changes in your data environment, such as new data sources, use cases, regulations, or technologies. You also need to solicit feedback and input from your data team, customers, and other stakeholders on how to improve your data privacy practices and performance. You can use methods such as surveys, interviews, focus groups, and testing to help you review and update your data architecture.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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CHARLES EVANS OGEGO OTIENO

C, C++, PHP, Javascript, Dart & Python ???? | Chess | ?? I focus on FinTech & Entertainment Apps
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Enhance data privacy by implementing training, clear policies, and access controls. Use encryption, conduct security audits, and minimize data. Develop an incident response plan, integrate security into development, and assess third-party vendors. Implement strong authentication, appoint a Data Privacy Officer, and stay compliant with laws. Keep systems updated, launch awareness campaigns, and monitor for suspicious activities. This comprehensive approach strengthens data privacy in your organization.

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How can you improve data privacy awareness in your company's architecture?

1

2

3

4

5

6

7

1 Assess your data privacy risks

2 Apply privacy by design principles

3 Use privacy-enhancing technologies

4 Monitor and audit your data activities

5 Educate and train your data team

6 Review and update your data architecture

7 Here’s what else to consider

Data Architecture

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How can you improve data privacy awareness in your company's architecture?

1

2

3

4

5

6

7

1 Assess your data privacy risks

2 Apply privacy by design principles

3 Use privacy-enhancing technologies

4 Monitor and audit your data activities

5 Educate and train your data team

6 Review and update your data architecture

7 Here’s what else to consider

Data Architecture

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