Data Policy vs. Data Standard: Polar Opposites or Perfect Partners?

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For companies striving to maximize business value from data, robust governance is essential. This requires tight integration between data policies and standards, which are often viewed as polar opposites. But are they truly opposites, or perfect partners?

Data policies provide high-level vision and direction for data management. They articulate how data should be ethically and legally collected, stored, accessed, analyzed and deleted.

Data standards offer the nitty-gritty technical details for executing policies effectively. They define consistent structures, formats, terminologies and quality rules for smooth data operations.

At first glance, policies and standards seem like very different creatures. Policies live in the world of strategy and principles. Standards operate in the realm of systems and execution.

But when properly aligned, they can work beautifully together to enable governance. Policies decide direction, standards make it operationally possible. They balance vision with pragmatism, abstraction with specificity.

Like yin and yang, policies and standards may appear contradictory but actually complement each other. To assess their integration in your organization, consider:

• Do policies and standards tell a cohesive data story?
• Are standards explicitly traceable back to policies?
• When policies and standards clash, which prevails?

The divide between policies and standards is real. But it need not lead to paralysis or confusion. With thoughtful design, they can be integrated into perfect data governance partners.

Data Policy: The Visionary Leader

The data policy serves as the constitution or manifesto for your organization’s data assets. It establishes overarching principles and guidelines aligned to business objectives, regulatory compliance, and customer expectations.

For example, a data policy for a bank may state: “All customer financial information is highly confidential and can only be accessed on a need-to-know basis for legitimate business purposes.” This aligns with privacy regulations like GDPR and builds customer trust.

Key elements of a data policy include:

• Data ownership — Who owns specific data assets (e.g. customers own personal data)
• Data access — What personnel roles can access, modify, delete data
• Data usage — How data can be legally and ethically used for business value
• Data protection — Measures like encryption, access controls, retention limits
• Breach response — Actions taken in case of unauthorized access

Data Standards: The Detail-Oriented Implementer

Data standards provide the specific rules and protocols to actually enforce and execute the data policy vision. They ensure consistency and interoperability across systems.

For example, a standard may specify: “All customer account numbers must be 10 digits starting with two letters identifying the region.” This enables efficient data exchange and analysis.

Typical data standards define:

• Data formats — Date, time, address, ID, amount fields
• Meta-data — Definitions of data elements
• Naming conventions — Rules for filenames, variables, databases
• Reference data — Standardized sets of permitted values
• Data quality — Accuracy, completeness, consistency metrics

Bridging the Divide: How Policies and Standards Work Together

While data policies and standards play different roles, their power lies in working together to enable governance. Here are some examples:

Policy Sets Strategic Direction, Standards Make it Operationally Possible

• The data policy may declare that customer information must be highly secured. The standards then provide the specific mechanisms like encryption, access controls, and auditing to achieve this.
• A policy aims to build customer trust and satisfaction through ethical data usage. Standards ensure this by enforcing complete, accurate and timely data for personalized services.
• A policy prioritizes business agility and innovation through data democratization. Standards then facilitate easy data discovery, system interoperability, and analytics.

Policies Adapt to Changing Needs, Standards Maintain Stability

• Data policies may evolve to address new regulations, technologies, and business priorities. Standards change less frequently to maintain consistency.
• When policies and standards conflict, policies often prevail to align with strategic objectives. But standards cannot be ignored if they reveal unrealistic policy goals.

Education and Reviews Keep Policies and Standards Aligned

• Periodic policy reviews assess if principles require updating. Standards are refined if they no longer meet policies.
• Training and documentation make sure all stakeholders understand policies and follow standards consistently.
• Assessments verify standards are traceable back to a specific policy mandate. Gaps highlight areas requiring alignment.

Remember the Fundamentals

• Data policies provide the vision and “what”. Data standards enable the “how”.
• Integrated well, they bridge the strategy-execution divide for robust governance.
• Reviews, training and assessments keep policies and standards in harmony.

Integrating Policies and Standards: Key Takeaways

• Data policies provide the overarching vision, principles, and “what” for data management.
• Data standards enable consistent technical execution by defining the operational “how”.
• Policies adapt flexibly to changing business needs and external factors. Standards maintain stability for smooth data operations.
• Both policies and standards require ongoing education, awareness and periodic reviews to stay aligned as needs evolve.
• When unsure whether a guideline is policy or standard, remember: policy for high-level direction and strategy, standards for detailed implementation.
• Well-integrated policies and standards allow organizations to meet compliance needs, gain customer trust, enable innovation through analytics, and provide reliable data products and services.

Critical Assessments

• Do your policies and standards tell a cohesive governance story? Identify gaps.
• Can you trace standards back to specific policy requirements? Make connections explicit.
• Do conflicts arise between policies and standards? Understand why and assess what needs to adapt.

Ongoing Success Factors

• Maintain harmony through robust education programs, training, and documentation.
• Schedule periodic reviews of policies and standards to keep pace with internal and external changes.
• Utilize assessments and audits to validate alignment and uncover areas for improvement.

Examples of data policies and standards from different industries:

Banking/Finance

Data Policy:

• Customer financial data is confidential and only accessible to authorized employees.
• Transaction records must be retained for 7 years under financial regulations.
• Data breaches must be reported to authorities within 72 hours.

Data Standards:

• Account numbers are 10 digits starting with a 3-digit routing number.
• Transaction dates use YYYY-MM-DD format.
• Currency fields allow up to 2 decimal places.
• Customer name fields have separate first, middle, last name standard.

Healthcare

Data Policy:

• Patient health records are private and accessed only for treatment purposes.
• Consent is required to share data with third parties.
• Medical images must be de-identified before use in research.

Data Standards:

• Conditions are coded using ICD-10 diagnosis codes.
• Medications follow RxNorm terminology standards.
• Dates use ISO 8601 YYYY-MM-DD format.
• Names are stored as first middle last suffixes.

Retail

Data Policy:

• Customer contact data can be used for marketing with opt-out consent.
• Purchase history data helps personalize shopping experiences.
• PII like SSNs cannot be retained after transactions complete.

Data Standards:

• Product IDs use standard SKU format.
• Prices allow 2 decimal places.
• Sale dates use MM/DD/YYYY format.
• Addresses follow Street, City, State, Zip structure.