Bad data costs businesses $12.9 million every year. Every click, purchase, and interaction your customers make creates valuable data. This data holds insights into their needs, preferences, and behaviors. Analyzing this information helps you improve your operations, save on costs, and tailor customer experiences.
However, the quality of your data directly affects how useful these insights are. The better your data, the more reliable and actionable those insights become. Read on to learn of nine practical data quality standards to help ensure you have accurate information.
What Are Data Quality Standards?
A Data Quality Standard defines specific rules for how data should be represented, formatted, and defined. By enforcing these standards, businesses minimize errors, meet regulatory requirements, and improve customer relationships.
It helps to be compliant with internationally-recognized standards that set guidelines for managing data quality across a company, including governance, assessment, and industry-specific applications.
The following are general guidelines on what accounts for quality data:
- Completeness: Data should contain all required information without gaps or missing values.
- Accuracy: Data must be correct and reliable.
- Relevance: Data should directly support the intended use case and provide meaningful insights for decision-making.
- Timeliness: Data needs to be up-to-date and readily available when needed.
Why Is It Important to Ensure Data Quality?
Accurate data reveals customers’ needs, market trends, and areas of improvement for your company. This allows you to personalize experiences and simplify processes by making data-informed decisions. Inaccurate data leads to misguided strategies, flawed marketing campaigns, and wasted resources. Here are some reasons why data quality matters greatly for your organization:
- Aids Decision-Making: High-quality data provides an accurate picture of what’s working well and room for improvement. With quality analytics, it’s easier to pinpoint actions that will benefit your business.
- Simplifies Operations: Clean, consistent data simplifies processes for your team, reducing time spent correcting errors or duplicating efforts.
- Improves Customer Satisfaction: An accurate customer 360 view helps you customize experiences for customer segments to grow order value.
- Supports Compliance and Risk Management: Meeting regulatory requirements is crucial for any business. Reliable data helps ensure compliance with industry standards and secures critical information.
- Eases Digital Transformation: Quality data provides a solid foundation for AI and machine learning algorithms. This helps businesses implement digital solutions with greater accuracy and reliability, making digital transformation easier.
9 Ways to Ensure Data Quality
Ensuring data quality requires allocating time, resources, policies, and tools. A systematic approach helps obtain accurate, reliable, and consistent information. Here are nine essential methods on how to ensure data quality:
1. Establish Clear Data Governance Policies
Clearly define responsibilities for data management and assign roles to oversee that collected data is meeting set standards. Standardizing data entry and maintenance procedures helps ensure consistency and reduce errors. A well-structured data governance framework should specify who enters new data, updates records, approves changes, and monitors quality.
2. Implement Data Validation at Entry Points
Preventing errors at the source is easier than correcting them later. Apply input validation techniques at data entry points using:
- Mandatory field completion
- Format verification (dates, numbers, etc.)
- Reference list validation
- Quality checks during data entry or updates
3. Regularly Cleanse and Remove Duplicate Data
Despite strict quality controls, data redundancy or compromise may occur. Use data cleansing tools to identify errors, misspellings, inconsistencies, and redundant records. Schedule regular maintenance activities (e.g., quarterly) using internal teams or external specialists to maintain data quality as volumes grow.
4. Employ Data Profiling and Monitoring Tools
Data profiling tools identify patterns, outliers, and anomalies, revealing potential quality issues like missing fields, out-of-range values, and format inconsistencies. Monitoring tools help flag quality variations, enabling automated detection and swift resolution before business impact occurs.
5. Standardize Data Formats and Definitions
Create a comprehensive data dictionary defining each data element and required formats (e.g., MM/DD/YYYY for dates). Also, uniform formatting rules across departments, such as standardized name fields with proper capitalization, should be established to ensure consistency.
6. Implement Master Data Management (MDM)
Critical business data, such as customer, product, and supplier records, should have a centralized source of truth. MDM creates a single system of record for core reference information.
Establish processes for your master data to be updated and maintained regularly. For example, you may designate specific employees to review master files weekly for errors or changes.
7. Conduct Regular Data Quality Audits
Performing periodic audits is essential to assess your data health. Automated tools scan for anomalies, while manual reviews validate accuracy. Document any issues found for tracking purposes and ensure findings are addressed, whether fixing errors or improving processes.
8. Train Employees on Data Quality Best Practices
Provide comprehensive training on quality guidelines, explaining the business impact of data quality and individual responsibilities in maintaining high standards. Regular training reinforces policies, governance, and collection procedures, and ensures data quality is preserved throughout the organization.
9. Employ AI and Machine Learning for Data Quality
Advanced AI-driven tools can help analyze large data volumes, using machine learning algorithms to detect patterns and anomalies stemming from compromised data quality. Over time, the tools become more accurate as they are trained on more of your data. Any potential problems can be flagged so your employees can quickly validate and address them.
Download your copy of “Unlocking Gen AI’s Potential By Strengthening Data and Organizational Readiness” to gain insights into building a solid foundation for AI initiatives.
Trust Boomi to Ensure Data Quality
Maintaining high data quality standards is an ongoing process that requires continuously adapting your strategies. Your business must establish transparent governance, implement validation tools, conduct regular audits, and train employees on best practices. Most importantly, quality management must evolve alongside your growing data using the right solution.
Boomi DataHub is a comprehensive, integrated solution that helps establish and maintain high data quality standards across your organization. Its built-in profiling, monitoring, and cleansing capabilities automate routine quality checks, giving you trusted data to support process, analytical, and AI initiatives. The solution has the following features and benefits:
- Intelligent Data Synchronization: Boomi DataHub ensures the bi-directional flow of high-quality data between your business systems, reducing data fragmentation and bringing consistency across your organization.
- Advanced Data Security: The solution implements strong encryption to store sensitive customer information securely, in line with major regulatory requirements for data protection.
- AI-ready Data Foundation: Boomi DataHub prepares and maintains clean, organized data, aiding your AI and machine learning initiatives by providing high-quality input.
- Comprehensive Data Profiling: In-depth analysis and statistics on essential records, such as customers, enable quick identification and resolution of data quality issues. Problems are found before they impact your operations.
- Low-Code Data Modeling: An intuitive visual interface streamlines data modeling tasks. Your teams can work faster and more efficiently on governance activities, and processes are optimized.
- Real-Time Data Quality Enforcement: Boomi’s continuous validation, enrichment, and error checks uphold data integrity as information flows through your systems.
- Flexible System Integration: DataHub provides system-agnostic connectivity, eliminating the need for multiple tools to manage data across different platforms.
- Automated Alerts and Monitoring: Boomi DataHub’s real-time notifications inform your teams of completed processes and records requiring review, enabling proactive data stewardship.
Learn more about optimizing your architecture for AI innovation and data quality to stay ahead of the competition.