Data quality plays an essential role in strategic analytics. And AI requires a large amount of high-quality data to train the large language models (LLM) that are essential to AI systems. When determining data quality, organizations need to address several factors, including:

• Completeness – Identify any gaps or missing elements. For instance, this could include verifying that all vendor records include critical information such as valid phone numbers.
• Uniqueness – On the flip side, duplicate data will also result in skewed results. Data teams must identify and resolve duplicates regularly.
• Validity – Ensure that data conforms to predefined standards such as rules around expected format or data type. For example, check to make certain that email addresses use a valid format.
• Timeliness – Outdated information will result in faulty strategies. For instance, using old sensor data can lead technicians to create ineffectual equipment maintenance strategies.
• Accuracy – Does data reflect real world values? For example, do location-based services use accurate GPS coordinates?
• Consistency – Data teams need to compare and verify data from various sources and systems to ensure coherence. For instance, check for consistent use of product names.

Several key strategies will help streamline data quality checks and ensure that you have the data you need to guide business direction.

Implement Strategic Data Governance

Data quality does not happen by chance. It requires a robust data governance framework that includes clearly defined policies, procedures, and responsibilities. These policies outline data lifecycle management, provide for data security, and ensure regulatory compliance.

With effective data governance, companies gain visibility into all their data, no matter where it lives. They classify data, tying retention and destruction policies, as well as sharing restrictions and encryption rules, to data type. They also enhance data security by strengthening identity and access management, balancing access with security.

An essential element of data governance involves data lineage, a type of metadata that traces the movement of data throughout the organization. This “data about data” tells where the data originated, how it has been used, and how it has transformed throughout its lifecycle.

By illuminating milestones along the data journey, data lineage helps the data team determine data consistency and accuracy. And in the event of an error, it helps investigators trace issues back to the root cause.

Monitor Data Continuously

By constantly monitoring data, organizations can track sensitive data to ensure regulatory compliance. Continuous monitoring also allows the organization to perform data quality checks in real time. This allows for immediate identification and correction of data issues, ensuring that data-driven decisions are based on the most accurate and up-to-date information.

Data monitoring systems should use clearly defined metrics, tracking error rates, identifying missing values, and following data trends.

Embrace Automation

Automation smooths the way for both data governance and data monitoring. Add AI-powered tools to the mix, and managing data at scale becomes much easier and more accurate. For instance, tools such as Microsoft Purview use pattern matching and machine learning to label data much more rapidly and accurately than humans can alone.

AI-powered automation also aids policy enforcement. And it helps the organization discover and interpret new regulations and updates, even suggesting necessary changes to policies and workflows.

Fine-tune the Human Touch

To achieve success, data quality must become integrated into corporate culture at all levels and in all departments. Train both data teams and end users to identify and address data quality issues. End user knowledge of business context will prove invaluable in interpreting data anomalies and ensuring that data reflects the real world.

Best Practices for Data Quality Checks Save Future Headaches

By implementing robust data governance, monitoring data 24×7, leveraging automation, and engaging end users, companies will be able to ensure effective data quality checks. And by improving data quality, they will build a solid foundation for data-driven decision making.

eGovernance solutions for information governance and compliance monitoring arm your organization with state-of-the-art technologies and decades of experience. We will help you harness your data to inform strategy and drive innovation.

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Consider the vast amount of sensitive data held by county governments. Without appropriate information governance, sensitive information related to local citizens and businesses could be vulnerable to breach. And when mismanaged data makes it difficult to fulfill public records requests, governments incur penalties and lose face with the public.

On the other hand, when that same county government manages, secures, and uses data effectively, they achieve critical goals of accountability, transparency and efficiency. Decision makers have reliable access to quality data, and public trust in the governing process increases. These same benefits apply to organizations in all industries.

What is eGovernance?

eGovernance refers to electronic governance, specifically governance of electronic information. As in the county government example above, common goals of eGovernance include improving data access, strengthening data security, driving efficiency and productivity and promoting accountability.

That is, organizations need to know what data they have, where it lives, who owns it and who can access it. They need to ensure that they data they own is high quality and secure from breach. And they need to achieve compliance with legislation and other regulations that govern data.

Information Governance Challenges

Today’s data environment poses significant challenges for information governance. In the first place, organizations collect massive amounts of data every day from many disparate sources. These include Teams documents, customer input from company websites, emails, meeting recordings, chat messages, data from IoT devices and much more.

Because information lives in so many different places, including BYOD devices and remote locations, identifying and securing that data proves challenging. At the same time, the regulatory environment grows more complex every year.

Technology trends such as cloud computing and artificial intelligence offer exciting capabilities. But they also complicate the cyber security environment and introduce new privacy and ethics concerns.

Finally, decision makers and data analytics tools need clean, trustworthy data. This requires comprehensive checks for duplicates, errors, and outdated information. It also requires a balance between facilitating data access and ensuring data protection and privacy.

eGovernance Solution Companies Best Practices Deliver Key Benefits

By following best practices for information governance, organizations do more than ease the path to regulatory compliance. They also reduce risk, ensure appropriate access to high quality data, and improve business planning.

An effective information governance program steps beyond checkbox compliance. For instance, in addition to ensuring the implementation of appropriate data security controls, proactive organizations will conduct penetration testing.

In addition, automating repetitive or redundant tasks such as tagging data and processing permission requests helps to reduce error. In fact, automation proves essential to managing an increasingly complex data environment.

As organizations migrate some or all their data to the cloud, they will need to adjust the tools they use to manage their data. Look for tools designed specifically to find and manage data across multiple clouds and on-premises environments.

Partner with eGovernance Solution Companies to Achieve Business Goals

An eGovernance solution company will help you choose and implement information governance solutions tailored to your business needs. The consultants at eMazzanti Technologies and Messaging Architects deliver a fully managed, cloud-based solution for preserving, discovering, and accessing digital data across your data environments.

Our data experts will begin by assisting you to build a comprehensive data and records management plan to reduce unstructured data and risk. A state-of-the-art archiving solution facilitates policy-based retention and destruction of data, regardless of Microsoft license type. Add our powerful eDiscovery tool and digital compliance solutions to gain the transparency, accountability, and efficiency your organization needs.

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eGovernance Cloud Solutions

eGovernance is a Cloud based solution for preserving, discovering and accessing digital data within your email and document storage systems for compliance, audit, security, eDiscovery and warehousing of critical or older data.

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eDiscovery »

Compliance »

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Compliance involves addressing both cyber security and data privacy. Data retention and destruction policies also play a key role. Consequently, compliance best practices involve improving data visibility through information governance and monitoring. They also include updating data policies and security practices and addressing the human component.

Use AI to Strengthen Information Governance

Because achieving compliance requires that organizations know what data they have, where it lives and who has access to it, information governance plays an important part. For instance, several privacy regulations include the “right to be forgotten.” This means that a company must be able to find and delete an individual’s personal data upon request.

Additionally, rules such as HIPAA and PCI DSS mandate the careful control of sensitive data such as protected health information (PHI) and financial data. These regulations require that organizations locate and tag all sensitive data wherever it lives or travels. With vast amounts of data on multiple platforms, finding and tagging that data represents a monumental task.

Fortunately, AI and machine learning can help. The average company manages hundreds of terabytes of data, with new data created every minute. Humans cannot feasibly find and classify all sensitive data manually. However, using pattern matching and machine learning, automated AI tools can find and classify sensitive data quickly and accurately.

Gain Visibility Through Compliance Monitoring

To identify compliance gaps, organizations should conduct regular compliance and security audits. In addition, continuous compliance monitoring allows data administrators to proactively address any potential compliance issues. Here again, automation plays a critical role.

Much of a company’s most sensitive information hides in unstructured data such as emails, PDF files and instant messages. This data can prove difficult to manage. But automated tools, powered by AI, monitor both structured and unstructured data for compliance violations.

These monitoring systems deliver automated alerts to appropriate personnel while taking precautionary action. For example, if a user attempts to improperly share PHI, the system will block the action and alert compliance officers. In addition to keeping sensitive data safe, monitoring allows the organization to demonstrate compliance in the event of an audit.

Regularly Review Data Policies

Data policies play an essential role in compliance. For instance, policies mandate who can access data and how long data should be retained. They also govern how users can share data and with whom. And they may cover certain security actions, such as the encryption of sensitive data.

An effective electronic communications policy includes not just the written policy, but also the technology to enforce that policy. For example, tools such as Microsoft 365 allow organizations to automatically prohibit sharing or destruction of sensitive data. Data policies require regular review and updates as the regulatory landscape changes and as the company adopts new tools.

Implement Essential Cyber Security Practices

Because compliance requires keeping data safe and secure from unauthorized access, data compliance best practices necessarily include security measures. At a minimum, organizations should use firewalls, keep software up to date, change default passwords and implement both multi-factor authentication and encryption.

In addition, security teams should regularly review access rights and permissions. Apply the principle of least privilege to ensure that users have the minimum amount of access they need. And make sure to remove user accounts and access when no longer needed. Tools such as Microsoft Entra help to automate access and identity management.

Provide Compliance and Security Awareness Training

Regardless of the technology involved, no compliance or security effort will prove successful if it ignores the human component. Take time to engage employees at all levels through regular privacy and security awareness training. Complement the training with phishing simulations and internal events such as privacy awareness month.

Compliance Technology Powers Data Compliance Best Practices

With a rapidly evolving regulatory environment, compliance experts suggest taking a big picture approach to achieving compliance. That is, look for privacy solutions that apply to most privacy laws, rather than applying different rules to different locations in compliance with individual states.

Technology will prove essential to a successful compliance strategy. For instance, intelligent compliance solutions from eGovernance provide insight into all indexed data through a single portal. Automatic reports alert auditors whenever an issue arises, allowing for immediate remediation. Proactive intervention saves time and money while delivering peace of mind.

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