Data has quickly become one of a company's most valuable assets. 

But it’s useless if it’s dirty, redundant, and missing essential information. Yet, when establishing data reliability, many businesses often feel overwhelmed and uncertain where to begin. The vast and complex nature of data ecosystems, coupled with rapidly evolving technologies and stringent regulatory requirements, can make the task daunting. This is even more true when the project of cleaning and correcting vast amounts of data falls on employees who are already at 100% capacity.

We have identified seven key steps you need to include in your data reliability strategy so you can use your company's most valuable asset—data—to its fullest potential.

Step 1: Define Data Governance

Data governance is the framework of practices, policies, standards, and processes that ensure an organization's effective management and use of data. 

It involves overseeing data quality, privacy, security, reliability, and compliance, ensuring that data is accurate, consistent, and accessible to those who need it while protecting it from unauthorized access and breaches. 

To establish a robust data governance framework your business and team can count on, start by following these key steps:

1. Define Data Governance Policies: Develop comprehensive policies that outline how data should be handled, stored, and protected. These policies should align with regulatory requirements and industry standards, ensuring data is managed legally and ethically.
2. Appoint Data Stewards: Assign data stewards responsible for maintaining data quality and ensuring adherence to data governance policies. Data stewards act as data guardians, monitoring its accuracy, consistency, and security.
3. Create Data Standards: Establish clear data entry, processing, and storage standards. These standards ensure that all data handled within the organization meets the same quality benchmarks, reducing inconsistencies and errors.
4. Implement Access Controls: Set up robust access controls to protect sensitive data. Ensure that only authorized personnel have access to specific data sets, and implement procedures for granting and revoking access as needed.
5. Conduct Regular Audits: Perform periodic data audits to evaluate the effectiveness of your data governance framework. Audits help identify areas for improvement and ensure that data governance policies are followed consistently.
6. Foster a Data-Centric Culture: Promote a culture of data awareness and responsibility within the organization. Educate employees about the importance of data governance and encourage them to follow best practices for data management.
7. Monitor and Report: Implement continuous monitoring and reporting mechanisms to track data quality metrics and compliance with governance policies. Regular reports help keep data governance top of mind and facilitate timely interventions when issues arise.

Step 2: Implement Advanced Data Quality Measures

While basic quality checks like accuracy and completeness are key, advanced data quality measures take a more comprehensive approach to ensure data reliability. 

These advanced measures include:

• Validity: Ensuring data adheres to defined formats and business rules.
• Consistency: Ensuring data is uniform across different systems and datasets.
• Timeliness: Ensuring data is up-to-date and available when needed.

Organizations should leverage the right tools and methodologies to implement these advanced data quality measures effectively. 

One such tool is data profiling, which analyzes data sources to understand their structure, content, and quality. Data profiling helps identify anomalies, redundancies, and inconsistencies and provides valuable insights for improvement.

Regular data audits and reviews are another critical practice. Scheduling audits to review data quality metrics and processes ensures that data remains valid, consistent, and timely, providing opportunities for continuous improvement. 

Finally, everyone across the company, from the CEO to the newest administrative assistant, should be data stewards. If they see something wrong in the system, they should quickly correct it, communicate to the appropriate team, or submit a ticket to correct the data. That way, everyone will share the responsibility of maintaining data quality.

Step 3: Tune Your Platforms

As you strive to create highly reliable data, we recommend automatically tuning your platforms to enforce data standards. 

This approach requires robust, flexible, and user-friendly platforms, allowing for seamless integration and enforcement of data quality standards without the need for extensive technical expertise. 

Choose a platform that can adapt to your specific data governance needs, offering the flexibility to accommodate various requirements. The ideal platform should empower business personnel, not just IT experts, to define and implement data quality standards. Look for features such as predefined templates, drag-and-drop functionalities, or natural language processing capabilities that make it easy to write, review, and implement data quality rules.

An intuitive interface simplifies the process, allowing data quality standards to be enforced more efficiently. 

By reducing technical complexity, your data governance efforts become more agile and responsive. This ensures that data quality standards are consistently met across the organization and that your data maintains its reliability as it flows through your company applications.

Step 4: Setup Real-Time Data Monitoring

Real-time monitoring ensures that data is continuously tracked and validated as it's used and accessed through various systems, allowing for immediate detection and correction of issues.

Tools such as real-time dashboards provide continuous insights into data quality and performance. These tools can be structured to automatically identify anomalies, inconsistencies, and other reliability issues, alerting users to take corrective actions swiftly. 

Machine learning-based analytics can further enhance this process by predicting potential data issues before they occur, allowing for preemptive measures to maintain data integrity.

Step 5: Choose Scalable Technology

As data volumes increase, scalable systems ensure that data quality remains consistent and reliable without degradation in performance. But first, you have to invest in the right technologies. 

Cloud-based solutions offer the flexibility and scalability needed to accommodate growth and provide on-demand resources that can be adjusted as data needs evolve. 

Plus, these solutions are continually upgraded as the software evolves, which means no more “migrations” from an old version of a platform to a newer one!

Step 6: Schedule Regular Training and Awareness

By keeping employees up-to-date with the latest developments and techniques, businesses can prevent data quality issues and create a data-centric culture.

Training programs should cover the latest data management technologies, best practices for data handling, and the importance of maintaining data integrity. 

They should also review the data's value to everyone within the organization. This involves promoting the importance of data quality and reliability at all levels of the organization and encouraging employees to take responsibility for the data they handle.

When everyone in the organization understands the importance of data and is committed to maintaining it, the overall data reliability will improve. 

Step 7: Enforce Robust Security and Compliance Measures

Without robust security measures, dirty data will quickly spiral out of control. Access controls and authentication mechanisms, such as multi-factor authentication or limiting who can change data or enter data in the system, will help protect the quality of your data. 

But security measures don’t stop there. Regulations like GDPR, CCPA, and other data protection laws set data handling, storage, and security standards. 

Compliance with these regulations helps avoid legal penalties and builds trust with customers and stakeholders. Implementing compliance measures such as data protection impact assessments (DPIAs), maintaining detailed records of data processing activities, and ensuring data subject rights are respected are integral to a robust data reliability strategy.

By prioritizing data security and regulatory compliance, businesses can protect their data from breaches and ensure it remains reliable and trustworthy.

Want To Implement These Steps?

At DataGence, we specialize in helping organizations audit, enhance, and implement effective data reliability strategies. Our expertise can guide you in creating a robust data framework that meets today’s challenges and future-proofs your data.