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Security

As explained in Effective Data Analysis

Data protection is the safeguarding of personal information through controls on its collection, access, processing, storage, sharing, retention, and deletion.

Security safeguards personal information across collection, access, processing, storage, sharing, retention, and deletion. It combines organizational controls, legal obligations, and clear communication with the people whose data is handled. section 8.2.1 section 8.1.2

Effective security practice includes:

  • Limit access to personal data and collect only what is needed for a specific purpose. section 8.2.1
  • Retain data only as long as necessary, and support deletion requests and withdrawal where applicable. section 8.2.1 section 8.1.2
  • Use anonymization, secure storage, and practical guardrails when handling personally identifiable information or protected health information. section 8.3 section 8.1.2
  • Explain collection, use, storage, confidentiality measures, and opt-out choices in readable consent materials. section 8.1.2

Security responsibilities vary with jurisdiction and sector. Analysts may coordinate with IT, security, and legal teams, while smaller organizations may carry more responsibility for enabling individual rights. Sensitive categories include identifying, health, financial, demographic, and authentication data; compromised credentials can expose other data. section 8.2.1 section 8.3

Figure 8.3 Many types of sensitive information are governed by sector-specific laws, both in the US, as in the examples shown here, and around the world.
figure

Use the sector-law overview to compare how different kinds of sensitive information receive specialized protection. section 8.2.1

How Effective Data Analysis covers it

How deep it goes
Defines regulatory expectations and practical safeguards, but stops short of detailed implementation procedures or technical security architecture.
Examples
Uses PII, PHI, financial information, authentication credentials, sector-specific laws, and product-analytics consent documents as concrete cases.
What it assumes
Assumes familiarity with analytics data and stakeholders while defining sensitive-data categories, privacy rights, and consent expectations.
What you can do after
Operate/configure data practices by limiting access, reducing collection, protecting storage, explaining use, and supporting deletion or withdrawal.
How it connects
Connects data protection with informed consent, anonymization, personally identifiable information, protected health information, and regulatory compliance.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.