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Examples of personal data include:

• Full name
• Home address
• Email address
• Social security number
• Passport number

The GDPR defines persona data as the following:

Article 4(1):‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person;

However, this kind of data is very often mistaken for causing confusion for organizations that collect, store and analyze such data.

What’s more, under the GDPR you can consider Cookies as personal data because according to the Recital 30:

Natural persons may be associated with online identifiers provided by their devices, applications, tools and protocols, such as internet protocol addresses, cookie identifiers or other identifiers such as radio frequency identification tags. This may leave traces which, in particular when combined with unique identifiers and other information received by the servers, may be used to create profiles of the natural persons and identify them.

To find out more about personal data visit the Piwik PRO blog:

• What is PII, non-PII, and personal data? [UPDATED]
• 4 burning questions about onboarding personal data and personally identifiable information (PII) to your analytics platform

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• Appoint a chief privacy officer (CPO) responsible for your company’s compliance with the regulation.
• Maintain personal information policies that are clear, understandable, and readily available with the types of data you collect, the third parties you share it with, and any potential risks for the individual.
• Obtain an individual’s consent to collect, use or disclose personal information. However, note that PIPEDA’s consent rules aren’t as restrictive as those in the GDPR.
• Only collect the personal information needed to satisfy the specified processing purposes.
• Supply an individual with a product or a service even if they refuse consent unless the information is essential to the transaction.
• Ensure the collected and maintained personal data is accurate, complete and up-to-date. Allow the individual to review the correctness and completeness of the data.
• Protect personal information using security measures proportional to the sensitivity of the data.
• In the case of a personal information breach, report it to the Privacy Commissioner of Canada, notify the affected individuals, and keep a record of the breach.

Lately, the Canadian authorities have taken steps to make their law better aligned with data protection regulations worldwide. Canada has proposed new bills to reform the privacy law and enact the Consumer Privacy Protection Act (CPPA).

Learn more about PIPEDA and CPPA:

PIPEDA & CPPA: How the Canadian privacy laws impact your analytics [Updated]

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Personally identifiable information (PII) is an American legal term for any data that could identify a specific individual.

PII may include the following:

• Full name
• Home address
• Email address
• Social security number
• Device identifiers
• Email addresses
• Passport number
• Driver’s license number
• Credit card number
• Date of birth
• Telephone number
• Owned properties, e.g., vehicle identification number (VIN)
• Login details
• Processor or device serial number
• Media access control (MAC)
• Internet Protocol (IP) address
• Device IDs
• Cookies

US government agencies and non-governmental organizations often reference PII. The US lacks an overriding law covering PII in all 50 states, so your understanding of PII may differ depending on the state or sector you operate in. A standard definition is provided by the National Institute of Standards and Technology (NIST):

PII is any information about an individual maintained by an agency, including (1) any information that can be used to distinguish or trace an individual‘s identity, such as name, social security number, date and place of birth, mother‘s maiden name, or biometric records; and (2) any other information that is linked or linkable to an individual, such as medical, educational, financial, and employment information.

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Key components of predictive analytics include:

• Data sources: Predictive analytics utilizes historical and real-time data from various sources, including transactional data, customer interactions, and operational metrics.
• Statistical techniques: It incorporates regression analysis, classification, clustering, and time series analysis to uncover relationships within the data.
• Machine learning: Advanced algorithms learn from new data inputs to improve the accuracy of predictions over time.

Predictive analytics is applicable across numerous industries and can be used for various purposes, including:

• Predicting customer behavior: Organizations can anticipate customer needs and preferences, enhancing marketing strategies and customer satisfaction.
• Risk management: Businesses can identify potential risks, such as credit defaults or fraud, allowing them to take preventive measures.
• Improving operational efficiency: Companies can forecast equipment failures or maintenance needs, optimizing resource allocation and reducing downtime.
• Sales forecasting: By analyzing past sales data, organizations can better predict future sales trends and adjust their strategies accordingly.

Benefits of predictive analytics include:

• Informed decision-making: It provides actionable insights that help organizations make strategic decisions based on data rather than intuition.
• Proactive strategies: By anticipating future trends and behaviors, businesses can proactively address challenges before they arise.
• Improved efficiency: Resources can be allocated more effectively based on predictions about demand and operational needs.

Predictive analytics is a powerful tool for organizations that use data to forecast future outcomes and enhance decision-making processes. Businesses can benefit from predictive analytics by using analytics platforms that employ predictive metrics.

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Privacy by design under the GDPR

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• Data anonymization and pseudonymization: Techniques that remove or disguise personally identifiable information (PII) from data sets, making it more difficult to identify individuals. Examples include data masking, tokenization, and anonymization algorithms.
• Encryption: The process of converting data into a form that can only be accessed or decrypted by authorized parties. This ensures that data remains confidential and secure, even if it is intercepted by unauthorized users. Examples include end-to-end encryption, secure sockets layer (SSL), and transport layer security (TLS).
• Privacy-preserving authentication: Methods for verifying users’ identities without revealing sensitive information such as passwords or biometric data. Examples include zero-knowledge proofs, anonymous credentials, and cryptographic protocols like Secure Multi-Party computation (SMPC) or homomorphic encryption.
• Differential privacy: A technique for analyzing and sharing data in a way that protects the privacy of individuals by adding noise or randomness to the data while still allowing useful insights to be derived. This prevents the disclosure of specific information about individuals while preserving the overall statistical properties of the data.
• Privacy-enhanced data storage and processing: Technologies that enable secure and private storage, sharing, and processing of data while minimizing the risk of unauthorized access or data breaches. Examples include secure enclaves, federated learning, and decentralized storage platforms.
• Privacy-focused browsers and tools: Web browsers and software applications that prioritize user privacy by blocking tracking cookies, preventing data collection, and offering features such as private browsing modes and ad blockers.
• Privacy by design principles: Design approaches that integrate privacy considerations into the development of technology systems and products from the outset. This includes adopting data minimization practices, implementing privacy policies and controls, and conducting privacy impact assessments.

Privacy-enhancing technologies play a crucial role in safeguarding individuals’ privacy rights in an increasingly interconnected and data-driven world. By incorporating these technologies into digital systems and processes, organizations can help mitigate privacy risks, build trust with users, and comply with privacy regulations and standards.

You might also like:

• What is privacy-friendly analytics?
• Marketing and advertising in a privacy-first world

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• Health Insurance Portability and Accountability Act (HIPAA) – It regulates the use and disclosure of personal health information (PHI) by healthcare providers and health insurers.
• Gramm-Leach-Bliley Act (GLBA) – This law applies to financial institutions and obliges them to implement tight security measures for data protection.
• Children’s Online Privacy Protection Act (COPPA) – It sets an obligation for web and app providers to receive parental consent before collecting any personal data on children under 13 years old.

The law that revolutionized the approach to data privacy in the US was the California Consumer Privacy Act (CCPA), expanded with the California Privacy Rights Act (CPRA) and applicable to businesses in California. It gives consumers rights concerning their personal data and imposes certain obligations on businesses that collect or sell it.

Only a few other US states have their own data privacy laws, including:

• Utah Consumer Privacy Act (UCPA)
• Colorado Privacy Act (CPA)
• Virginia Consumer Data Protection Act (VCDPA)
• Connecticut Data Privacy Act (CDPA)

US companies operating in the EU are also obliged to adhere to GDPR.

You may also like:

Data privacy laws in the United States and how they affect your business

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EU-US data privacy framework

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