Data Quality, Governance & Ethics

Data Quality data की fitness for use को दर्शाता है — data कितना accurate, complete, consistent, reliable और relevant है।

Key Point: "Garbage In, Garbage Out" — poor quality input data leads to unreliable conclusions.

• Accuracy — सटीकता: Data factual और error-free है। Correct values.
• Completeness — पूर्णता: All required data is present. No missing values.
• Consistency — संगति: Data is consistent across different records and sources.
• Timeliness — समयानुकूलता: Data is up-to-date and available when needed.
• Validity — वैधता: Data conforms to defined formats and rules.
• Uniqueness — अद्वितीयता: No duplicate records.
• Relevance — प्रासंगिकता: Data is appropriate for the intended purpose.
• Reliability — विश्वसनीयता: Data is trustworthy and can be depended upon.

Data Governance policies, rules, roles, standards और accountability का framework है जो data को manage, protect, share और use करने के तरीके को नियंत्रित करता है।

Core Elements:

• Data Ownership — डेटा स्वामित्व: Who is responsible for the data
• Data Stewardship — डेटा प्रबंधन: Day-to-day management and quality
• Data Standards — डेटा मानक: Consistent formats and definitions
• Metadata Management — मेटाडेटा प्रबंधन: Data about data
• Data Quality — डेटा गुणवत्ता: Ensuring accuracy and completeness
• Access Control — पहुँच नियंत्रण: Who can access what data
• Privacy and Security — गोपनीयता एवं सुरक्षा: Protecting data
• Interoperability — अंतर-संचालनीयता: Systems working together
• Accountability and Audit — उत्तरदायित्व एवं लेखापरीक्षा: Monitoring compliance
• Retention and Disposal — प्रतिधारण एवं निपटान: How long to keep data
• Ethical Data Use — नैतिक डेटा उपयोग: Responsible use of data

• Create/Collect — निर्माण/संग्रह: Data is generated or collected from sources.
• Store — भंडारण: Data is saved in databases or storage systems.
• Process — प्रसंस्करण: Data is cleaned, transformed, and analysed.
• Use/Share — उपयोग/साझा: Data is used for analysis, reporting, or sharing.
• Archive — संग्रहीत: Data is archived for long-term preservation.
• Dispose — निपटान: Data is securely deleted when no longer needed.

Governance applies across the entire life cycle.

Open Data वह data है जो publicly available है — anyone can access, use, share and republish it without restrictions (subject to open licenses).

Open Government Data in India:

• Platform: data.gov.in (Open Government Data Platform India)
• Developed and hosted by NIC under MeitY
• Purpose: Access to government-owned shareable data in machine-readable form
• Supports transparency and citizen engagement

Exam Trap: Open Data ≠ Personal Data. सभी government datasets public नहीं किए जा सकते। Personal, confidential, security-sensitive data को protect करना आवश्यक है।

• Availability — उपलब्धता: Data is available in a convenient format.
• Accessibility — पहुँच: Data is easy to access and retrieve.
• Machine Readability — मशीन-पठनीयता: Data can be processed by computers.
• Reusability — पुन:उपयोग: Data can be reused for different purposes.
• Timeliness — समयानुकूलता: Data is updated regularly.
• Non-discrimination — अभेद्यता: Anyone can access the data.
• Appropriate Licensing — उपयुक्त लाइसेंस: Clear terms for use.
• Privacy and Security Protection — गोपनीयता एवं सुरक्षा: Personal/confidential data protected.

Data Privacy individuals का यह right है कि उनकी personal information कैसे collected, used, stored और shared की जाती है — यह उनके control में हो।

Key Concepts:

• Privacy — गोपनीयता: Personal information पर individual का control
• Anonymisation — अज्ञातीकरण: Removing identifiers so individuals cannot be identified
• Pseudonymisation — छद्मनामीकरण: Replacing identifiers with codes
• Informed Consent — सूचित सहमति: User agrees with full understanding
• Purpose Limitation — उद्देश्य सीमा: Data used only for declared purpose
• Data Minimisation — डेटा न्यूनीकरण: Only necessary data collected

Exam Trap: Pseudonymised data को हमेशा fully anonymous नहीं माना जा सकता — re-identification possible है।

Data Security data को unauthorised access, breaches, corruption, theft से protect करना है।

Security Measures:

• Encryption — एन्क्रिप्शन: Data को unreadable format में convert करना
• Access Control — पहुँच नियंत्रण: Role-based permissions
• Authentication — प्रमाणीकरण: Verifying identity (password, biometrics)
• Firewall — फ़ायरवॉल: Blocking unauthorised access
• Regular Backups — नियमित बैकअप: Data recovery in case of loss
• Audit Trails — लेखापरीक्षा निशान: Tracking data access and changes
• Secure Data Disposal — सुरक्षित निपटान: Proper deletion when no longer needed

Data Ethics data के collection, use, sharing और analysis में moral principles का application है।

Major Ethical Concerns:

• Privacy Violation — गोपनीयता उल्लंघन: Unauthorised use of personal data
• Informed Consent — सूचित सहमति: Lack of proper consent
• Surveillance — निगरानी: Excessive monitoring
• Algorithmic Bias — एल्गोरिदमिक पक्षपात: Biased algorithms leading to discrimination
• Data Manipulation — डेटा हेरफेर: Misrepresenting data
• Selective Reporting — चयनात्मक रिपोर्टिंग: Only favourable results shown
• Misleading Visualisation — भ्रामक दृश्यीकरण: Distorting data visually
• Unauthorised Sharing — अनधिकृत साझा: Sharing data without consent
• Re-identification — पुन:पहचान: Identifying individuals from anonymised data
• Digital Exclusion — डिजिटल बहिष्कार: Excluding certain groups

• Respect for Persons — व्यक्तियों का सम्मान: Autonomy and dignity of individuals
• Beneficence — कल्याण: Maximising benefits
• Non-maleficence — हानि न पहुँचाना: Avoiding harm (Do no harm)
• Justice — न्याय: Fair distribution of benefits and burdens
• Honesty — ईमानदारी: Truthful representation of data
• Objectivity — वस्तुनिष्ठता: Avoiding personal bias
• Transparency — पारदर्शिता: Open about methods and limitations
• Accountability — उत्तरदायित्व: Responsibility for data practices
• Confidentiality — गोपनीयता: Protecting sensitive information

Digital Personal Data Protection Act, 2023 digital personal data के processing से संबंधित है — personal-data protection और lawful processing दोनों को recognise करता है।

Key Terms:

• Data Principal — डेटा प्रिंसिपल: वह individual जिससे personal data संबंधित है।
• Data Fiduciary — डेटा फिड्यूशियरी: वह entity जो processing का purpose और means तय करती है।
• Consent — सहमति: Specific, informed, and unambiguous permission.
• Purpose Limitation — उद्देश्य सीमा: Data केवल declared lawful purpose के लिए use हो।
• Data Minimisation — डेटा न्यूनीकरण: केवल आवश्यक data collect किया जाए।
• Security Safeguards — सुरक्षा उपाय: Unauthorised access and breach से protection।

• Trap 1: Open data = unrestricted disclosure of personal data → ❌ Personal data is protected.
• Trap 2: Data governance = only cybersecurity → ❌ Includes quality, access, standards, ownership, accountability.
• Trap 3: Anonymisation = pseudonymisation → ❌ Anonymisation is irreversible; pseudonymisation can be reversed.
• Trap 4: Data quality = only accuracy → ❌ Includes completeness, consistency, timeliness, validity, uniqueness, relevance.
• Trap 5: Data management = data governance → ❌ Governance sets policies; management executes them.
• Trap 6: Open data = copyright-free → ❌ Open data has licensing terms (attribution, share-alike).

Which data quality dimension ensures that data is up-to-date and available when needed?

• A. Accuracy
• B. Completeness
• C. Timeliness
• D. Consistency

Explanation: Timeliness means data is current and available when required.

Data Governance primarily deals with:

• A. Only data storage
• B. Policies, roles, standards, and accountability
• C. Only cybersecurity
• D. Only data analysis

Explanation: Data governance is about policies, roles, standards, and accountability — not just storage or security.

Which is the correct sequence of the data life cycle?

• A. Store → Create → Process → Share → Dispose
• B. Create → Store → Process → Use/Share → Archive → Dispose
• C. Process → Create → Store → Share → Dispose
• D. Create → Process → Store → Use → Dispose

Explanation: The correct data life cycle sequence is Create/Collect → Store → Process → Use/Share → Archive → Dispose.

In the context of data ethics, "purpose limitation" means:

• A. Data can be used for any purpose
• B. Data can only be used for the declared lawful purpose
• C. Data must be stored indefinitely
• D. Data must be shared with everyone

Explanation: Purpose limitation means data is used only for the declared purpose for which it was collected.

Which of the following is NOT a dimension of data quality?

• A. Accuracy
• B. Completeness
• C. Creativity
• D. Consistency

Explanation: Creativity is not a data quality dimension. Accuracy, completeness, and consistency are.

The Digital Personal Data Protection Act, 2023 deals with:

• A. Processing of digital personal data
• B. Open government data
• C. Data visualisation
• D. Data storage hardware

Explanation: The DPDP Act, 2023 regulates the processing of digital personal data.

Data Life Cycle:

• Create/Collect → Store → Process → Use/Share → Archive → Dispose

Open Data:

• Available, Accessible, Machine-readable, Reusable
• India: data.gov.in (Open Government Data Platform)
• Open Data ≠ Personal Data

Privacy & Security:

• Privacy = Control over personal data
• Security = CIA Triad (Confidentiality, Integrity, Availability)
• Anonymisation (irreversible) vs Pseudonymisation (reversible)

Data Ethics:

• Principles: Respect, Beneficence, Non-maleficence, Justice, Honesty, Objectivity, Transparency, Accountability
• Concerns: Bias, Privacy, Misleading visualisation, Selective reporting

DPDP Act, 2023:

• Data Principal (individual), Data Fiduciary (entity)
• Consent, Purpose limitation, Data minimisation, Security safeguards

Exam Formula:

UGC NET में Data Quality, Governance & Ethics के questions dimensions, governance vs management, privacy concepts, और ethics concerns पर based होते हैं।