Crafting an Effective Data Quality Plan
Data is ubiquitous and rarely centralized in one location. Whether it resides in your ERP, CRM, IoT systems, core operations software, or a combination of these, data quality can no longer be treated as a one-time fix. You need a structured, repeatable, and scalable data quality plan that ensures accuracy, consistency, and trust across the entire organization.
Let’s break down what a strong data quality plan looks like and how to make it work across modern architectures.
Why You Need a Data Quality Plan
Data quality issues don’t just impact reports; they affect decisions, forecasts, compliance, customer experience, and automation.
A well-executed data quality plan allows your organization to:
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Maintain consistency across departments and business units
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Reduce errors in reporting, analytics, and AI models
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Improve regulatory compliance
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Increase confidence in data-driven decision-making
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Enable interoperability between cloud platforms and legacy systems
But how do you build one that works at scale?
1. Define the Scope: What Systems Are Involved?
A scalable plan starts with mapping your data landscape. Most organizations work with hybrid environments that include:
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ERP systems like Oracle Cloud Fusion
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CRM tools like Salesforce or HubSpot
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Custom-built operational software
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IoT platforms collect real-time sensor data
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Point of Sale (POS) and transactional systems
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Data Lakehouses like Databricks for centralized analytics
Each source has its structure, format, and velocity. Your data quality plan should treat each source as part of a single interconnected ecosystem.
2. Align on Data Quality Dimensions
Once you’ve mapped the landscape, identify which data quality dimensions matter most for your organization. Common ones include:
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Completeness – Are all required fields populated?
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Accuracy – Does the data reflect reality?
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Timeliness – How up-to-date is the information?
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Consistency – Are formats and definitions aligned across systems?
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Uniqueness – Are there duplicates?
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Validity – Does the data follow the required rules or formats?
Your plan should define KPIs for each dimension and apply them contextually across different systems and departments.
3. Centralize Observability and Governance
One of the biggest challenges with multi-platform data environments is visibility. You can’t fix what you can’t see.
A successful data quality plan includes:
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Metadata management and lineage tracking
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Real-time monitoring of quality metrics
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Alerts for anomalies or threshold breaches
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Data profiling at the ingestion and transformation stages
This is where Arkon Data Platform plays a key role, connecting disparate platforms while preserving metadata and control logic so your governance efforts scale across your architecture.
4. Make It Actionable: Root Cause and Remediation
Finding a bad record is only half the battle. Your plan should also define remediation workflows:
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Who gets notified?
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How are issues escalated?
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Can the system fix them automatically?
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How are fixes logged and audited?
Empowering your data stewards and analysts with tools to take action is crucial for keeping data quality efforts from stalling.
5. Continuous Improvement and Automation
Data quality is not a project; it’s a capability. Your plan should include:
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Automated tests for new data pipelines
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Dashboards that show quality trends over time
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Feedback loops to improve source systems
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Integration with AI/ML pipelines to prevent garbage in/garbage out
Platforms like Arkon act as the layer that brings together monitoring, lineage, and automation across systems, without locking you into a single vendor or cloud.
Final Thoughts
A modern data quality plan needs to be:
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Platform-agnostic
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Scalable across use cases
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Tightly integrated with governance and analytics
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Automated and measurable
Arkon Data Platform helps you do just that, whether your data comes from Oracle, IoT devices, POS systems, or custom-built software.
Make Data Quality a Built-in Capability, Not an Afterthought
Arkon Data Platform (ADP) empowers your team to enforce and monitor data quality across every stage of your data lifecycle, without rebuilding your architecture or duplicating efforts.
Here’s how ADP enables scalable, multi-source data quality management:
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Connect any source, preserve structure: Extract structured data and metadata from complex systems like Oracle Fusion, custom applications, IoT, POS, and more, without breaking schemas or relationships.
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Enable end-to-end lineage and traceability: Track data movement and transformations from source to dashboard, giving full visibility into what’s being used, where, and how.
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Centralize rules and standards: Unify your quality controls across tools by connecting to governance frameworks like Unity Catalog, so your rules live in one place, but apply everywhere.
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Automate profiling and anomaly detection: Surface quality issues early with automated data profiling and built-in validations, whether in batch or streaming pipelines.
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Standardize formats and definitions: Harmonize naming conventions, units, and critical business logic across teams and domains, using flexible mapping layers.
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Validate in motion and at rest: Apply quality checks both as data lands and as it flows through transformations, so you can act before errors propagate.
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Integrate with your existing stack: ADP isn’t a replacement; it works with your lakehouse, cloud warehouse, or analytics platform to embed quality directly into operations.
With ADP, your data quality plan doesn’t stay on paper; it becomes part of the fabric of how your systems talk to each other.
FAQs: Making Your Data Quality Plan Real
1. What’s the biggest mistake companies make when creating a data quality plan?
Many teams treat data quality as a one-time project rather than a continuous, embedded process. They focus on documentation and theoretical rules but don’t operationalize those controls in the systems where data lives and moves. ADP helps by embedding rules directly into your architecture, so quality isn’t just a governance checklist; it’s part of execution.
2. How do I enforce data quality across sources I don’t fully control, like vendor systems or legacy apps?
This is a common challenge. ADP allows you to extract and standardize data (including metadata) even from black-box systems like Oracle Cloud Fusion or third-party software. It doesn’t require invasive changes; you can define quality layers on top of the existing flows.
3. We already use Databricks / Snowflake / Informatica. Where does ADP fit in?
ADP is not a replacement. It acts as the connective tissue between your operational systems and analytics environments. It ensures data comes in clean, traceable, and mapped, so your existing platforms can operate with consistent, high-quality inputs.
4. How can I track whether my data quality plan is working?
Success should be measurable. You can set KPIs like data freshness, completeness, schema stability, and validation pass rates, then monitor them through lineage-aware dashboards. This gives visibility not only to data teams but also to business users relying on that data.
5. What’s the first step if I don’t have a data quality strategy yet?
Start by identifying your most critical data domains (e.g., finance, customer, operations) and the systems feeding into them. ADP can help you catalog those assets, surface quality gaps, and establish an initial set of rules and validations that grow over time.