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Data Governance

Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making.

Ethical Principles around Data

  1. Autonomy - The right to control your data, possibly via surrogates
  2. Informed consent - You should explicitly appove use of your data based on understanding
  3. Beneficence - People using your data should do it for your benefit
  4. Non-maleficence - Do no harm

ODPi​

ODPi creates open source standards to help you use and understand data across all platforms.

https://www.odpi.org

https://searchdatamanagement.techtarget.com/definition/data-governance

https://en.wikipedia.org/wiki/Data_governance

https://www.oreilly.com/content/data-governance-and-the-death-of-schema-on-read

Data governance frameworks​

  1. DGI - Data Governance Institute
  2. DAMA DMBOK - Data Management Body of Knowledge (DMBOK)
  3. McKinsey
  4. Eckerson
  5. PwC
  6. Deloitte
  7. Control Objectives for Information and Related Technology (COBIT)
  8. SAS Data Governance Framework
  9. BCG Data Governance Framework

Data Governance Framework: Guide, Examples, Template

Top Data Governance Frameworks in 2024

Cheat Sheet​

What is Sensitive Data?​

Personally Identifiable Information (PII), Protected health information (PHI), intellectual property, financial information, education and legal records are all sensitive data.

Most countries have laws and regulations that require the protection of sensitive data. For example, the General Data Protection Regulation (GDPR) in the European Union sets stringent rules for data protection and privacy. Non-compliance with such regulations can result in hefty fines, legal actions, and sanctions against the violating entity.

When we design systems, we need to design for data protection.

Encryption & Key Management​

The data transmission needs to be encrypted using SSL. Passwords shouldn’t be stored in plain text.

For key storage, we design different roles including password applicant, password manager and auditor, all holding one piece of the key. We will need all three keys to open a lock.

Data Desensitization​

Data desensitization, also known as data anonymization or data sanitization, refers to the process of removing or modifying personal information from a dataset so that individuals cannot be readily identified. This practice is crucial in protecting individuals' privacy and ensuring compliance with data protection laws and regulations. Data desensitization is often used when sharing data externally, such as for research or statistical analysis, or even internally within an organization, to limit access to sensitive information. Algorithms like GCM store cipher data and keys separately so that hackers are not able to decipher the user data.

Minimal Data Permissions​

To protect sensitive data, we should grant minimal permissions to the users. Often we design Role-Based Access Control (RBAC) to restrict access to authorized users based on their roles within an organization. It is a widely used access control mechanism that simplifies the management of user permissions, ensuring that users have access to only the information and resources necessary for their roles.

Data Lifecycle Management​

When we develop data products like reports or data feeds, we need to design a process to maintain data quality. Data developers should be granted with necessary permissions during development. After the data is online, they should be revoked from the data access.

managing sensitive data

Data Governance