Data Management Principles
REFLECT
Consider the purpose, quality, and impact of data usage—ensuring it aligns with ethical, sustainable, and organizational commitments.
1. Validity & Reliability
Data must be accurate, consistent, and trustworthy. Ongoing assessments ensure quality and fitness for use across the data lifecycle.
● Use data that is accurate, complete, and relevant for its intended [CS1] purpose.
● Regularly test for reliability and quality under different operational conditions.
● Detect and mitigate bias through proactive data profiling and quality monitoring.
● Ensure diverse and representative data sources to minimize systemic bias.
2. Ethical Data Use
Data practices must align with organizational values and ethical standards, respecting individual rights, data ownership and broader social impact[CS2] [CS3] .
● Incorporate fairness, equity, and inclusion in data sourcing and use.
● Avoid using data in ways that may reinforce discrimination or inequity.
● Ensure usage complies with ethical, legal, and regulatory frameworks.
● Only use data that is authorized and properly licensed; obtain explicit permission from data owners or stewards where required.
● Respect the right of individuals or organizations to revoke consent and request deletion or purging of their data.
3. Sustainability-Aware Data Management
Evaluate and manage the environmental and social impact of data storage, processing, and management.
● Optimize data storage to reduce carbon footprint and infrastructure waste.
● Minimize duplication and data sprawl to conserve resources.
● Support ESG goals through transparent and responsible data handling.
REFRAME
Redesign governance, roles, and systems to ensure resilient, secure, and compliant data ecosystems.
4. Transparency & Explainability
Data management practices must be transparent, traceable, and understandable to both internal and external stakeholders.
● Clearly communicate policies on data collection, use, and retention[CS4] .
● Maintain data lineage and [CS5] [ch6] provenance for end-to-end traceability.
● Provide meaningful and accessible explanations about how data influences decisions and outcomes.
● Tag and document synthetic datasets clearly to differentiate them from real-world data and enable appropriate usage, governance, and auditability.
5. Accountability & Oversight
Establish clear ownership, roles, and enforcement mechanisms to ensure responsible data stewardship.
● Assign data ownership and stewardship across business and technical functions.
● Implement ongoing audits and data governance reviews.
● Monitor adherence to compliance requirements and take corrective actions when needed.
6. Data Literacy & Stewardship Culture
Empower people to be effective data citizens, understanding the implications of their data use and responsibilities.
● Promote data literacy across business and technology teams.
● Train teams on data ethics, privacy, and governance practices.
● Foster a culture of shared responsibility for data integrity and security.
REIMAGINE
Leverage responsible data practices to create innovative, inclusive, and human-centric digital experiences.
7. Human-Centered Data Design
Design data systems that respect individual rights and are geared toward enhancing human outcomes.
● Ensure privacy-by-design and protection of personal data.
● Support individuals’ control over their data through consent and transparency.
● Leverage data to enhance—not replace—human decision-making.
8. Inclusive Data Access & Utility
Data benefits should be equitably distributed, empowering a wide range of stakeholders.
● Promote access to high-quality data across functions while preserving security.
● Ensure underserved or marginalized groups are not excluded from data-driven value.
● Enable responsible data sharing across the ecosystem with proper safeguards.
9. Responsible Innovation with Data
Innovation using data should be grounded in principles of responsibility, ethics, and impact awareness.
● Use data to responsibly accelerate innovation in products, services, and insights.
● Apply the same scrutiny for risk, ethics, and sustainability to experimental data use as with production systems.
● Continuously evaluate the downstream impact of new data-driven solutions.
Directives to Achieve/Implement Responsible Data Management Principles
REFLECT ACTIONS
Assess and align data practices with ethical, sustainable, and business goals.
1. Validity & Reliability
● Conduct periodic data quality assessments (accuracy, completeness, consistency) across critical datasets.
● Classify and prioritize data domains based on business impact and risk level.
● Establish baseline metrics for data reliability and track anomalies and trends.
● Use profiling tools and validation frameworks to identify and remediate data quality issues.
● Mandate regular testing of data pipelines to ensure stability and integrity under evolving business conditions.
2. Ethical Data Use [CS7] [ch8]
● Identify and document potential ethical risks in current data sourcing, handling, and usage.
● Incorporate business unit stakeholders into ethics reviews for sensitive or high-risk data domains.
● Establish data usage guardrails that explicitly prohibit discriminatory, invasive, or unethical use cases.
● Develop decision trees to determine when additional ethical oversight is required (e.g., for personal, behavioral, or biometric data).
● Maintain an internal registry of datasets and their permissible use cases to avoid scope creep or misuse.
● Define quality standards and validation steps for synthetic data to ensure it meets fitness-for-purpose and does not introduce bias or hallucinated patterns.
● Clarify data ownership, licensing, and authorized use—especially when sourcing third-party or externally acquired datasets.
● Apply strict controls and encryption policies for data hosted on open or public cloud platforms to ensure compliance with privacy, jurisdictional, and security requirements.
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3. Sustainability-Aware Data Management
● Identify and quantify data storage impact on cloud and on-prem infrastructure (e.g., energy consumption, e-waste).
● Assess the environmental footprint of data retention policies and revise to limit “dark data” accumulation.
● Develop strategies to decommission unused or redundant data repositories.
● Evaluate data center regions for energy efficiency and consider data residency policies that support sustainability goals.
● Engage ESG and operations teams to ensure data practices support corporate sustainability KPIs. 
REFRAME ACTIONS
Redesign governance, infrastructure, and education to support responsible data stewardship.
4. Transparency & Explainability
● Document and publish data collection, access, and usage policies for all internal and external stakeholders.
● Enable automated data lineage tracking across systems for transparency and audit readiness.
● Implement metadata management tools that make data attributes, definitions, and usage context easily discoverable.
● Require data owners to provide understandable descriptions of sensitive datasets and known limitations.
● Include transparency metrics in data governance scorecards (e.g., % of critical datasets with lineage and business definitions).
5. Accountability & Oversight
● Define data stewardship roles across business units, with training and clear accountability structures.
● Establish a Data Governance Council or Center of Excellence (CoE) to oversee responsible data use.
● Integrate data governance checkpoints into project lifecycles, particularly for data sourcing, sharing, and retention.
● Conduct regular audits to track compliance with data policies, privacy laws, and contractual obligations.
● Develop escalation workflows for addressing breaches of data policy or unapproved usage.
6. Data Literacy & Stewardship Culture
● Launch organization-wide training programs on data ethics, privacy, security, and governance fundamentals.
● Create role-specific data literacy tracks (e.g., analysts, engineers, executives) aligned to data responsibilities.
● Promote stories and case studies internally that highlight responsible data use or misuse, and lessons learned.
● Implement an internal certification program for data stewards and owners.
● Encourage cross-functional data communities to share best practices and resolve governance challenges.
REIMAGINE ACTIONS
Innovate responsibly by embedding data principles into future-facing strategies and operations.
7. Human-Centered Data Design
● Apply “privacy by design” principles in the architecture of new systems and data products.
● Enable granular use[ka9] [ka10] [ch11] r controls for consent, data sharing preferences, and data-access requests.
● Establish policies for ethical anonymization, re-identification prevention, and data retention limits.
● Honor individuals’ data-ownership and privacy rights by promptly complying with requests to delete or export their personal data. Include end-user representatives in system design processes to advocate for data rights, usability, and accessibility.
● Promptly comply with user requests to delete, export, or restrict the use of their personal data, in accordance with regulatory and ethical standards.
● Include end-user representatives in system design processes to advocate for data rights, usability, and accessibility.
● Introduce feedback loops that allow individuals to correct or dispute, or update data about them.
8. Inclusive Data Access & Utility
● Build data products with multilingual, accessible, and inclusive design in mind.
● Ensure equitable access to governed data sources across business units and locations.
● Establish inclusive data sourcing guidelines that reflect the diversity of customers and communities served. Regularly evaluate whether access restrictions unintentionally exclude stakeholders who could benefit.
● Use feedback mechanisms to identify and address gaps in data utility across user groups.
9. Responsible Innovation with Data
● Establish “data innovation sandboxes” for exploring emerging data use cases with governance oversight.
● Pilot new analytics or AI models with synthetic or anonymized data before scaling.
● Conduct scenario planning to assess downstream impacts of new data-driven products or policies.
● Design experiments that measure both business value and ethical alignment of innovative data use.
● Align innovation KPIs with responsible data management goals (e.g., reduction in dark data, increased data reuse rate).
Sample Breakdown of Principle
1. Validity & Reliability
Data must be accurate, consistent, and trustworthy. Ongoing assessments ensure quality and fitness for use across the data lifecycle.
● Use data that is accurate, complete, and relevant for its intended purpose.
● Think at 4 levels- Purpose, Level (org, Architectural, etc)
| Process | Actionable Step | Impact | Value Delivered |
|---|---|---|---|
| Business Process | Define business-critical metrics and the data required to support them | Aligns data usage with business priorities | Improves decision-making accuracy and strategic focus |
| Establish data accountability (owners/stewards) for key domains | Clarifies responsibility for quality and relevance | Enables faster issue resolution and data trust | |
| Implement regular data quality reviews in operational reporting cycles | Identifies gaps and inconsistencies in business use | Reduces risk of using outdated or incorrect data | |
| Enforce policy that decisions should be backed by trusted data sources | Promotes data-driven culture | Reduces reliance on gut feel or shadow data practices | |
| Architecture Process | Integrate automated data quality checks into ETL/ELT pipelines | Ensures only accurate and complete data flows into systems | Reduces downstream errors and maintenance burden |
| Use canonical models and reference data frameworks | Standardizes data definitions and formats across domains | Enhances interoperability and consistent reporting | |
| Maintain data lineage and impact analysis capabilities | Tracks source and transformation of data | Improves trust and traceability for audits and compliance | |
| Enforce metadata tagging for data accuracy, completeness, and quality status | Enables context-aware data usage | Improves discoverability and confidence in data | |
| Design Process | Define field-level validation rules (e.g., mandatory fields, dropdowns) | Prevents incomplete or invalid data entry | Enhances input quality at the source |
| Align data models with business intent and analytical use cases | Ensures relevancy and avoids unused or redundant fields | Reduces data bloat and simplifies analytics | |
| Embed quality KPIs (accuracy %, completeness %, etc.) in dashboards | Surfaces issues early in the data lifecycle | Drives continuous improvement and accountability | |
| Design interfaces that highlight stale or low-quality data | Promotes user awareness of data reliability | Avoids erroneous actions based on bad data |
BTABoK Owner and Author
The BTABoK owner is the primary contributor and concept owner. She chairs any discussions on the update and modification of this contribution. The Iasa and the profession thank our contributors for their serious industry contributions!!
Chitra Sundaram
Digital Executive - https://www.linkedin.com/in/chitra-sundaram/
Chitra Sundaram is a data and analytics leader and Practice Director of Data Management at Cleartelligence, with over 15 years of experience leading enterprise data strategy, governance, and digital transformation initiatives. She advises organizations on defining data vision, establishing data governance frameworks, driving data monetization, ensuring data quality and security, fostering a data-driven culture, and overseeing data architecture. Chitra has a proven track record of excelling in these areas, building high-performing teams, advising C-suite executives, and delivering impactful solutions across various industries.
Her passion for sustainability has driven her to actively contribute to environmentally responsible technology practices. She serves as a track lead within IASA’s SustainableArchitectures.org, helping shape sustainable technology frameworks and conversations. Chitra also holds multiple technology certifications and executive education credentials from institutions such as UC Berkeley and MIT Sloan.
[CS1]Create a drill down on just this principle to see how it impacts Business process, Architecture ad Design process. What’s the value.
[CS2]Add a principle around only using authorized data and obtaining license from Data Owners
[CS3]Add another principle to mention flexibility to purge data owned by an individual/ org as part of data ownership
[CS4]Maybe detail out on impact on sustainability.
[CS5]Specify tagging synthetic data sets
[ch6]Added a 4th principle
[CS7]Quality of synthetic data. Data Ownership. Usage of data on open cloud platforms
[ch8]added 3 more bullets
[ka9]Add the responsibility to comply with requests by a user to have their data deleted
[ka10]Data privacy and retention of data ownership and rights
[ch11]Incorporated