Data Governance vs. Data Standards: What’s the Difference?
When it comes to managing data effectively, terms like “governance” and “standards” can feel interchangeable. But they’re not. Understanding the difference is crucial for any decision-maker looking to improve their organization’s data strategy. Let’s break it down simply, clearly, and with practical insights to empower your decisions.
The Basics: Governance Policies vs. Standards
The distinction is straightforward:
• Standards are templates.
• Policies set boundaries—both minimum and maximum.
Think of data standards as the blueprints you can use right away. They align with best practices, work well with vetted tools, and can be implemented almost instantly.
Policies, on the other hand, define the playing field. They set the must-haves (minimums) and the sanity checks (maximums) to ensure you’re using your data effectively without overcomplicating the process.
Why Both Matter
Imagine building a house. Standards are like the architectural plans—precise, reusable, and ready to guide the work. Policies are the zoning laws and safety codes, ensuring the structure is safe, functional, and within limits.
When managing data, you need both standards and policies to:
✔️ Ensure consistency.
✔️ Maintain quality.
✔️ Avoid inefficiencies.
What Are Data Standards?
Data standards are ready-made solutions. They’re like plug-and-play templates built on best practices. When applied, they streamline your processes and ensure your data is consistent and usable.
Examples of Data Standards:
• File formats: CSV, JSON, XML.
• Metadata standards: ISO 11179 for metadata registries.
• Data quality dimensions: Completeness, accuracy, and timeliness.
By using these standards, your organization can avoid reinventing the wheel every time a new dataset is introduced.
The Challenge of Applying Standards
Not every data set will fit perfectly into a predefined standard.
For example:
• Some tables are too large.
• Some data changes too often to follow rigid standards.
• Some datasets might not even justify the effort of standardization.
This is where policies step in to fill the gaps!
What Are Data Governance Policies?
Data governance policies set the rules of engagement. They ensure that standards are applied appropriately and that your data strategy aligns with your broader business goals.
Policies create boundaries:
• Bottom boundaries (must-haves): The absolute minimum requirements for data quality, security, and usability.
• Upper boundaries (sanity checks): Limits to avoid overprocessing, overspending, or overcomplicating your data strategy.
To put it into perspective, we'll go through some key questions and answers.
1. Access Control:
Who can access what data and under what conditions?
A: Sensitive customer information must only be accessed by authorized personnel.
2. Retention Policies:
How long should data be stored?
A: Keep financial records for 7 years; delete obsolete operational data after 2 years.
3. Data Quality Requirements:
What are the minimum standards for accuracy and completeness?
A: No report is valid if data completeness falls below 95%.
What if I have a manufacturing company?
Simple.
Manufacturers handlemassive datasets—inventory levels, supplier performance, and production rates. Using standards, they ensure that every supplier’s data follows the same format (e.g., CSV).
But applying policies, they can:
• Define the minimum accuracy threshold for supplier data.
• Limit processing to datasets with significant impact, avoiding wasted resources.
• Retain inventory data only for the last 12 months to reduce storage costs.
This dual approach keeps operations efficient and focused.
How to balance data Standards and Policies
1. Identifying Core Datasets:
What data is critical to your operations?
2. Applying Standards Strategically:
Focus on areas where standards provide the most value.
3. Setting Practical Policies
Define boundaries that match your organization’s goals.
Why Decision-Makers Should Care
Without clear policies and standards:
Data becomes inconsistent and unreliable.
Resources are wasted on unnecessary activities.
Security and compliance risks increase.
With them, you can:
✔️ Ensure data supports business objectives.
✔️ Avoid inefficiencies and risks.
✔️ Build trust in data-driven decisions.
The data governance could be simple for you with a click here.
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