When Target expanded into Canada, its failed inventory system migration became a costly business lesson in data quality. Errors in the data migration process caused huge mismatches between inventory records and actual stock levels, leaving shelves empty in some stores and warehouses overstocked in others. These data issues were a major factor in operational failures that eventually led to the closure of all 133 Target Canada stores and losses exceeding CAD 2 billion . Poor migration planning and incomplete data preparation turned what should have been a modern retail rollout into a high-profile example of how messy data can sink a project.
Approximately 80% of data migration projects exceed their budgets or miss deadlines. This happens when companies invest heavily in new platforms while overlooking the condition of their existing data. Without proper data cleaning before migration, even the most advanced systems are built on unreliable foundations, leading to integration issues, inaccurate reporting, compliance risks, and repeated rework.
This comprehensive guide explores why data cleaning is essential before migration, the risks of neglecting data quality, and practical strategies to ensure your digital transformation succeeds.
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Key Takeaways Data migration success depends far more on data quality than on the technology being adopted. Migrating dirty data leads to cost overruns, delays, unreliable analytics, compliance risks, and operational failures.Data cleaning before migration reduces rework, improves accuracy, and increases user trust and system adoption. A structured approach covering profiling, standardization, deduplication, validation, and testing is critical for migration readiness. Advanced tools like automation, AI, and cloud platforms make large-scale data cleaning faster, more accurate, and repeatable. Long-term success requires ongoing governance, monitoring, and a strong data quality culture beyond migration.
Why Data Quality Matters Most When Planning Data Migrations Moving to a new system represents more than a technical upgrade. It fundamentally reshapes how businesses operate, serve customers, and make strategic decisions. At the center of this transformation lies data—every customer record, transaction, and product detail must be accurate, consistent, and ready for its new environment.
Consider this analogy: you wouldn’t construct a skyscraper on a cracked foundation. Similarly, you shouldn’t migrate critical business operations onto compromised data. The investment in new platforms, from enterprise resource planning (ERP) systems to customer relationship management (CRM) tools and cloud infrastructure, demands high-quality data that can leverage their full capabilities.
Research from Gartner reveals that poor data quality costs organizations an average of $15 million annually. This expense manifests through multiple channels:
Wasted marketing spend targeting incorrect customer profiles Missed sales opportunities from incomplete prospect information Regulatory compliance fines and legal penalties Employee inefficiency from constantly verifying or correcting information Impaired decision-making from unreliable analytics Undermined competitive positioning and customer relationships
Beyond financial impact, dirty data impairs decision-making, undermines competitive positioning, and damages customer relationships.
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Real Risks You Face Migrating Dirty Data Without Proper Preparation Organizations that skip data cleaning face significant challenges that ripple throughout their entire operation. Understanding these risks helps justify the investment in proper data preparation, making it clear why data quality should never be treated as an afterthought.
1. Project Delays and Cost Overruns That Cascade Into Budget Disasters Imagine discovering thousands of duplicate customer records after migration. Development teams scramble to fix issues, timelines extend, and costs escalate rapidly. Post-migration remediation is exponentially more expensive and complex than proactive data cleaning. According to IBM Consulting, the complexity of untangling data issues in a live production environment can increase costs by 300% compared to addressing them pre-migration. What starts as a minor oversight quickly transforms into a major financial burden that affects the entire organization.
2. Inaccurate Reports and Analytics You Simply Cannot Trust When source data contains errors, the new system generates reports based on those same errors. This creates a dangerous cycle where executives make strategic decisions based on faulty business insights , leading to poor outcomes and loss of confidence in data assets. Consequently, leaders may revert to intuition-based decisions rather than trusting data-driven insights, completely negating the purpose of the digital transformation . The ripple effects touch every department that relies on accurate information for planning and execution.
3. Data Privacy and Compliance Nightmares Including Regulatory Penalties Regulations such as GDPR, CCPA, and HIPAA require accurate, well-governed data. Migrating inconsistent or incomplete personal information can result in serious data privacy breaches, regulatory non-compliance, substantial financial penalties, and lasting reputational damage. GDPR violations can result in fines up to €20 million or 4% of annual global turnover, whichever is higher. These aren’t empty threats—regulators have shown they’re willing to enforce these penalties when organizations fail to protect data integrity .
4. Operational Chaos From System Breakdowns and Process Failures Dirty data breaks business processes in ways that immediately impact daily operations:
Supply chain systems are unable to match product codes accurately Billing systems are generating incorrect invoices due to inconsistent address formats Inventory management showing phantom stock levelsCustomer service teams are struggling to locate accurate customer information
These aren’t minor glitches—they halt operations, damage customer trust, and result in lost revenue that compounds over time.
5. User Resistance That Kills ROI and System Adoption Rates If the new system’s data proves unreliable, users quickly lose faith. They revert to old methods, create workarounds, or simply stop using the system effectively. This resistance negates the entire investment and purpose of the migration. Industry experts emphasize that the biggest risk to any data migration isn’t the technology itself, but the quality of the data being moved. Organizations often underestimate the volume of errors lurking in legacy systems, only discovering the extent of the problem when it’s too late to address efficiently.
What Makes Data Cleaning Challenging: Common Obstacles Enterprises Face Even with good intentions, organizations encounter significant hurdles when preparing data for migration. Recognizing these challenges helps you plan more effectively and allocate appropriate resources to overcome them.
1. Managing Massive Data Volumes Across Enterprise Systems Modern enterprises generate data at an astonishing pace. Handling terabytes or even petabytes of information from various sources makes manual data cleaning virtually impossible. Organizations need automated tools and systematic processes to manage this scale effectively, yet many lack the infrastructure or expertise to implement these solutions properly.
2. Integrating Data From Disconnected and Legacy Systems Data often resides in disconnected systems across legacy databases, spreadsheets, and cloud applications, each with its own format, naming conventions, and quality standards. Merging this heterogeneous information requires careful planning and specialized tools. Without clear rules, responsibilities, and processes for data quality, issues proliferate. Data entry errors, inconsistent updates, and missing information become systemic problems rather than isolated incidents.
Older systems might lack robust data validation , making it difficult to extract clean data. Business logic might be embedded directly into applications rather than documented as explicit rules, complicating data extraction and transformation. Additionally, dedicated data quality teams, specialized tools, and expertise are often scarce. Organizations may be forced to compromise on data cleansing efforts due to time or budget pressures, creating a false economy that costs significantly more in the long run.
4. Lack of Clear Data Ownership and Governance In many enterprises, it is unclear who owns which data sets or who is accountable for their accuracy. When multiple teams create, modify, and consume data without shared governance policies, inconsistencies multiply. Without defined ownership, data cleaning decisions are delayed, standards vary across departments, and critical issues often go unresolved until late in the migration process.
5. Balancing Data Quality Improvements With Business Continuity Data cleaning initiatives often compete with ongoing business operations for time, resources, and attention. Enterprises struggle to improve data quality without disrupting reporting, applications, or customer-facing services that rely on existing data. Tight migration timelines can force teams to prioritize speed over thorough cleansing, increasing the risk of transferring flawed data into new systems.
7 Essential Steps to Clean Data Before Your Migration Project Effective data cleaning follows a structured, methodical approach. These seven steps provide a comprehensive framework to ensure your data is migration-ready, transforming raw information into a strategic asset.
Step 1: Build Your Data Quality Strategy and Governance Framework Before touching any data, establish a clear strategy aligned with business objectives. This foundational step sets expectations, accountability, and direction for all downstream activities.
Key actions include:
Defining what “clean” data means based on business and regulatory requirements Identifying critical data attributes and setting acceptable accuracy thresholds Assigning ownership through clearly defined roles for data stewards, analysts, and technical teams Establishing escalation paths, timelines, and resource plans covering tools, training, and personnel
This upfront planning prevents reactive firefighting, reduces ambiguity, and keeps migration efforts aligned with enterprise priorities.
Step 2: Profile Your Data to Understand Current Structure and Quality You cannot fix what you don’t understand. Data profiling creates visibility into the current state of your data and highlights areas requiring remediation.
This assessment typically includes:
Statistical analysis to measure completeness, consistency, and value distributionsFormat validation to detect inconsistencies in dates, addresses, identifiers, and structured fields Advanced discovery such as anomaly detection , relationship mapping, lineage tracking, and missing data assessment
The result is a comprehensive “data health report” that prioritizes issues by business impact and informs all subsequent cleansing steps. Modern profiling tools accelerate this process and surface patterns that manual analysis often misses.
Step 3: Standardize Formats and Normalize Values Across All Systems Enterprise data is rarely consistent across sources. Variations in naming conventions, date formats, addresses, and codes introduce errors and confusion during migration.
Standardization focuses on:
Establishing uniform formats for dates, currencies, addresses, and phone numbers Normalizing multiple representations of the same entity into agreed-upon values Applying business rules, reference tables, and lookup validations for critical fields
All transformation rules should be documented for transparency and auditability. While automation handles scale efficiently, human oversight remains essential for validating edge cases and ensuring business intent is preserved.
Step 4: Tackle Missing Data With Intelligent Imputation Techniques Missing data can undermine analytics, operations, and customer experiences if not handled carefully. The goal is strategic remediation, not indiscriminate population.
Effective approaches include:
Identifying and quantifying missing fields based on business impact Applying statistical, rule-based, or machine-learning-driven imputation where appropriate Deriving values from related records and clearly flagging unresolved gaps
Rather than filling every field, prioritize high-impact data elements and ensure remaining gaps are transparent and understood by business users.
Step 5: Eliminate Duplicates to Create Authoritative Single Sources of Truth Duplicate records inflate volumes, distort insights, and create operational inefficiencies. Removing them is critical for reliable reporting and decision-making.
Deduplication typically involves:
Exact matching using unique identifiers or primary keys Fuzzy and probabilistic matching to account for variations, misspellings, and incomplete data Applying survivorship rules to determine which records to retain or merge
Clear documentation of matching logic and survivorship criteria ensures consistency, auditability, and long-term trust in master records.
Step 6: Correct Errors Systematically and Prevent Recurrence Error correction should address root causes, not just symptoms. Sustainable data quality depends on preventing issues from reappearing.
A balanced correction strategy includes:
Automated rule-based and machine-learning-driven fixes for common issues Manual review workflows for ambiguous or high-risk records External validation against authoritative sources such as registries or industry standards
Beyond corrections, organizations must implement validation rules, user training, and ongoing monitoring to maintain data integrity over time.
Step 7: Verify Readiness Through Rigorous Testing and Validation Before migration, data readiness must be proven through structured testing and stakeholder validation.
Final verification includes:
Automated quality audits measuring adherence to defined metrics and business rules Trial migrations to test compatibility, relationships, and referential integrity Business impact assessment, stakeholder sign-off, and documentation of known limitations
This final checkpoint provides confidence that data is truly migration-ready, significantly reducing post-migration risks and operational disruptions.
Data Quality Comparison: Before vs. After Migration Success Aspect Dirty Data Migration Clean Data Migration Project Timeline Significant delays, missed deadlines, extended remediation phases On-time completion, faster go-live, minimal post-migration fixes Budget Impact Cost overruns of 200-300%, expensive emergency fixes Stays within budget, optimal resource utilization, predictable costs Data Accuracy Rate 60-70% accuracy typical, constant error discovery 95-99% accuracy achievable, minimal error rates User Adoption Rates Low trust, resistance to change, continued use of old systems High confidence, enthusiastic adoption, immediate productivity gains Reporting Quality Unreliable insights, questioning of all analytics Trusted reports, confident decision-making, strategic clarity Compliance Status High risk of violations, potential fines, audit failures Full regulatory compliance , passed audits, reduced legal risk Operational Impact Frequent disruptions, manual workarounds, frustrated staff Smooth operations, automated processes , satisfied employees Business Value Realization Eroded trust, lost opportunities, competitive disadvantage Competitive advantage, improved ROI, enhanced customer experience
Leveraging Advanced Technology for Efficient Data Cleaning Operations While manual processes still play a role, modern data cleaning relies heavily on advanced technologies to manage scale, speed, and complexity. The right tools can compress months of effort into weeks while delivering higher accuracy, consistency, and repeatability across enterprise data environments.
Automated data profiling tools rapidly analyze massive datasets to uncover patterns, anomalies, and quality issues that manual methods often miss. They generate statistical summaries, identify data distributions, detect outliers, and highlight inconsistencies through visual dashboards. By delivering a comprehensive, real-time view of data health, automated profiling enables teams to prioritize remediation efforts efficiently and make informed decisions early in the migration lifecycle.
2. AI and Machine Learning Capabilities AI and machine learning enhance data cleaning with intelligent, adaptive techniques. Predictive imputation models infer missing values based on historical patterns, while advanced matching algorithms significantly improve duplicate detection accuracy. Self-learning systems continuously refine their logic as corrections are applied, reducing the need for manual intervention over time. AI-driven classification and tagging further streamline data organization, accelerating migration preparation and analytics.
3. Cloud-Based Infrastructure and Scalability Cloud platforms provide the elasticity required for large-scale data cleaning initiatives. They support dynamic resource scaling, parallel processing for faster execution, and cost-efficient, pay-as-you-use consumption models. Cloud-native data quality solutions also offer extensive connectivity through pre-built connectors, API-based integrations, and support for diverse data formats, enabling both real-time and batch processing across distributed enterprise environments.
Enterprise data governance platforms establish centralized oversight while maintaining visibility across decentralized systems. Core capabilities include data cataloging to inventory enterprise assets, lineage tracking to trace data movement and transformations, and metadata management to preserve context and definitions. Workflow automation routes quality issues to the right stakeholders, while role-based access controls ensure accountability and secure handling of sensitive information.
Modern data cleaning solutions integrate seamlessly with business intelligence and analytics platforms, allowing organizations to realize value immediately after migration. Cleaned data feeds directly into automated reports, advanced analytics , and real-time dashboards that monitor quality metrics continuously. Predictive analytics further enables teams to anticipate emerging data quality issues, shifting data management from reactive remediation to proactive optimization.
How to Maintain Data Quality After Your Migration Succeeds Data cleaning isn’t just about preparing for migration; it’s about establishing practices that maintain data quality indefinitely. Successful organizations implement comprehensive data governance frameworks that ensure data remains a strategic asset rather than becoming a liability over time.
1. Establish Data Governance and Stewardship: Effective data governance begins with clear data ownership and stewardship, assigning specific responsibility for data quality, defining roles and accountability, empowering data stewards with authority and resources, and creating cross-functional governance teams. Establish data policies and standards that define rules for data entry, usage, and storage while creating standardized procedures for common operations and documenting exceptions and approval processes.
2. Implement Continuous Monitoring and Quality Checks: Implement continuous monitoring through ongoing data quality checks, tracking metrics and trends over time, setting up alerts for quality degradation, and conducting regular audits and reviews. Maintain comprehensive data audit trails that log all data changes, track who made changes and when, document reasons for modifications, and enable rollback capabilities when needed.
3. Ensure Data Privacy and Regulatory Compliance: In today’s regulatory landscape, data privacy and compliance are non-negotiable. Organizations must classify sensitive data to identify personally identifiable information (PII), tag protected health information (PHI), mark financial and confidential data, and apply appropriate security controls. Implement robust access controls that restrict access by role and need, monitor data access patterns, encrypt sensitive data, and apply anonymization where appropriate.
Maintain compliance documentation through audit trails for regulatory reporting, document data processing activities, maintain data lineage information, and prepare for compliance audits. Enforce data retention policies that define appropriate retention periods, implement automated deletion processes, balance legal requirements with business needs , and manage data lifecycle comprehensively.
4. Building a Data Quality Culture Within Your Organization: Beyond technology and processes, successful organizations cultivate a strong data quality culture where all employees understand the importance of accurate, well-maintained data. This involves establishing clear communication about data quality expectations, recognizing and rewarding employees who contribute to data quality improvements , and providing ongoing training to help users understand proper data management practices. Additionally, implementing feedback mechanisms that allow data quality issues to be reported promptly enables organizations to address problems quickly before they compound.
Organizations should develop comprehensive key performance indicators that track overall data health and progress toward quality goals. These metrics should measure accuracy rates by system and department, track completion rates for critical data fields, monitor the average time required to resolve data quality issues, and quantify the business impact of data quality improvements. Furthermore, creating executive dashboards that visualize these metrics helps leadership understand the value of ongoing data quality investments and justifies continued resource allocation to data governance initiatives.
Understanding theoretical approaches is valuable, but seeing practical applications brings concepts to life. These scenarios illustrate the transformative impact of proper data cleaning on complex migration projects.
Global Retail ERP Migration: Multi-System Consolidation Success Challenge: A multinational retailer needed to consolidate 12 legacy ERP systems into a single cloud-based platform. Data was fragmented across regions, with inconsistent product descriptions, non-standardized customer addresses, and heavily duplicated supplier records. The scale, thousands of SKUs, and millions of customer records—made manual data cleaning impractical, with initial estimates projecting a year-long effort that threatened to delay the migration.
Solution: The organization adopted automated data profiling to uncover quality issues across systems. Standardization tools normalized product data globally, while advanced matching algorithms eliminated customer and supplier duplicates. Migration accelerators streamlined mapping and transformation, supported by continuous validation to maintain data quality throughout execution.
Results: Data cleansing timeline reduced by 60%, completed in under five months Data accuracy increased to over 98% Smoother ERP go-live with minimal operational disruptions Faster user adoption and immediate reporting confidence Saved millions in potential rework and operational issues
Healthcare Patient Data Consolidation: HIPAA-Compliant Integration Challenge: After acquiring multiple clinics, a healthcare provider needed to consolidate patient records from five EHR systems into a central platform. Data inconsistencies, missing patient information, duplicate patient IDs, and non-standard medical codes posed risks to patient safety and billing accuracy. All activities also had to maintain strict HIPAA compliance.
Solution: Secure data profiling identified duplicates and missing data without compromising patient privacy. Probabilistic matching accurately resolved duplicate patient records, guided by medically defined survivorship rules. Standardization of ICD-10 and CPT codes ensured consistency, while detailed audit trails preserved regulatory compliance throughout the consolidation.
Results: Achieved 99% accuracy in patient data consolidation Minimized risk of misdiagnosis or billing errors Completed project within budget and ahead of schedule Maintained full HIPAA compliance throughout the process Ensured seamless patient care continuity and enhanced clinical analytics
5 Best Practices That Separate Successful Migrations From Failed Projects Beyond the core steps, several best practices can significantly improve your data cleaning outcomes and ensure long-term success.
1. Start Early and Involve Business Stakeholders Begin data assessment and cleaning as soon as migration planning starts, not weeks before go-live. Early engagement provides the time needed to identify complex issues, test assumptions, and avoid last-minute surprises. While IT teams can address technical inconsistencies, business stakeholders understand how data is created, interpreted, and used in daily operations. Involving them in defining quality standards and validating outputs ensures the cleaned data truly supports business processes, reporting, and decision-making.
2. Document Everything for Future Reference Comprehensive documentation is essential for sustainability. Record data quality issues identified, transformation and standardization rules applied, business decisions taken, exceptions handled, and unresolved limitations. This documentation supports faster troubleshooting after go-live, enables knowledge transfer across teams, and provides a strong foundation for future migrations, audits, and data governance initiatives.
3. Conduct Multiple Test Migrations Never assume that clean data will automatically migrate without issues. Perform multiple test migrations using progressively larger and more representative datasets. These dry runs help uncover hidden compatibility issues, performance bottlenecks, and data-mapping errors well before the production migration.
4. Plan for Iterative Improvement Perfect data quality is rarely achieved in a single cycle. Plan for iterative rounds of data cleaning, validation, and refinement. Each cycle improves accuracy, completeness, and consistency while incorporating feedback from testing and business users. This approach reduces risk and leads to more reliable outcomes over time.
5. Be Realistic About Constraints Be honest about what can be achieved within the available time, budget, and resources. Not every issue can be resolved before migration. In many cases, consistently managed and well-documented data that meets business-critical needs is more valuable than pursuing perfection at the cost of missed deadlines and delayed value realization.
How to Measure Data Cleaning Success With Key Metrics Establish clear metrics and track them throughout the process to ensure your data cleaning efforts are actually effective. Key data quality metrics include:
Accuracy Rate: Percentage of data records that are factually correct and validCompleteness Rate: Percentage of required fields that contain valuesConsistency Rate: Percentage of data following standardized formats and business rulesUniqueness Rate: Percentage of records without duplicatesTimeliness: How current the data is relative to real-world changesValidity: Percentage of data conforming to defined formats and ranges
Before cleaning begins, measure baseline data quality across these dimensions. Then set realistic target metrics based on business requirements. Not every field needs 100% accuracy; prioritize based on business impact. Establish dashboards that track data quality metrics in real-time. This enables quick identification and resolution of emerging issues before they compound into major problems.
Choosing the Right Partner for Complex Data Migration Projects While some organizations have the resources and expertise to handle data cleaning internally, many benefit from partnering with specialists who bring proven methodologies and advanced tools to complex challenges.
Look for partners with structured, repeatable processes that cover every aspect of data quality management from assessment through post-migration governance. The best partners leverage AI-powered automation to reduce manual effort, accelerate timelines, and improve accuracy. Their platforms should offer intelligent data profiling, automated cleansing, and sophisticated matching algorithms that handle the complexity and scale of enterprise data.
Different industries face unique data challenges . Healthcare data differs significantly from retail or manufacturing data in structure, sensitivity, and regulatory requirements. Choose partners with relevant experience in your sector who understand industry-specific challenges and compliance requirements. The ideal partner handles everything from initial assessment through post-migration governance, providing continuity and consistent quality throughout the entire process.
Kanerika’s Comprehensive Approach to Enterprise Data Migration For organizations seeking a complete data migration solution, Kanerika offers an end-to-end framework backed by its proprietary FLIP (Flexible, Intelligent, and Portable) platform designed specifically for complex enterprise migrations. This AI-powered system transforms complex data operations through:
Core Capabilities:
Automated data extraction, profiling, and loading across diverse source systemsIntelligent data mapping suggesting optimal transformations between source and target systems Rule-based transformation engines applying complex business logic at scale Self-learning capabilities that improve accuracy over time Cloud-native architecture providing enterprise-grade scalability
Kanerika’s methodology combines deep expertise in data management with cutting-edge technology to deliver measurable results. Their team brings extensive experience in industry-specific data challenges, ensuring that solutions align with both technical requirements and business objectives. What distinguishes their approach is the commitment to data governance and compliance , helping clients establish robust frameworks that maintain data quality long after migration.
Organizations that have partnered with Kanerika report significant improvements in project timelines, data accuracy, and overall migration success rates, while maintaining strict regulatory compliance throughout the process. The combination of proven accelerators, expert guidance, and advanced technology positions Kanerika as a strategic partner for organizations serious about migration success.
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Conclusion: Data Quality Is the Foundation of Migration Success Data migration represents a significant opportunity to transform your business, but success hinges on the quality of your data. Organizations that invest in thorough pre-migration data cleaning avoid costly delays, reduce operational risks, and unlock the full potential of their new systems.
The process requires strategic planning, systematic execution, and the right combination of expertise and technology. By following the comprehensive framework outlined in this guide from initial assessment through post-migration governance, you position your organization for a smooth transition and sustained competitive advantage.
Remember that data cleaning isn’t merely a technical checkbox on your project plan. It’s a strategic investment in your organization’s future, creating a foundation for accurate analytics, informed decision-making, and operational excellence. Whether you tackle data cleaning internally or partner with specialists, the key is to start early, follow proven methodologies, and maintain focus on data quality throughout the migration lifecycle.
The question isn’t whether to clean your data before migration, it’s how thoroughly you’ll prepare for success. Make data quality a priority, and your migration project will deliver the transformative results you envision.
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FAQs 1. Why is data cleaning important before migration? Data cleaning is critical because migrating poor quality data only transfers existing problems into the new system. Inaccurate, duplicate, or incomplete data can break reports, disrupt operations, and reduce user trust after go live. Cleaning data beforehand ensures the target system performs as expected and supports reliable decision making.
2. What types of data issues should be fixed before migration? Common issues include duplicate records, missing values, outdated entries, inconsistent formats, and incorrect data relationships. Organizations should also address invalid values, broken references, and unused legacy data. Fixing these issues early prevents errors during migration and improves overall data usability.
3. When should data cleaning start in the migration process? Data cleaning should begin as soon as migration planning starts, not right before deployment. Early cleaning allows teams to identify data gaps, involve business users for validation, and avoid rushed fixes later. Starting early also reduces migration delays and unexpected costs.
4. Who should be involved in the data cleaning process? Both technical teams and business stakeholders should be involved. IT teams handle profiling, validation, and transformation rules, while business users confirm data accuracy and relevance. Collaboration ensures the cleaned data aligns with real business needs and usage.
5. What happens if data is not cleaned before migration? Skipping data cleaning often leads to system failures, inaccurate reporting, and user frustration. Post migration fixes are more expensive and time consuming than pre migration cleaning. In regulated industries, poor data quality can also lead to compliance risks and penalties.
