At the same time, healthcare organizations face considerable obstacles as they strive to govern the massive amounts of data under their control. In the first place, the World Economic Forum estimates that the average hospital generates over 50 petabytes of data every year, most of it unstructured.

Secondly, because of its sensitive nature, healthcare data is heavily regulated. Laws such as HIPAA impose strict requirements on how organizations store, use, and share data. And with protected health information (PHI) living in many different formats, departments, and platforms, compliance proves complicated. Frequent security threats add further complexity.

To address these challenges, healthcare organizations must adopt a proactive and strategic approach to data governance. This process will involve numerous moving parts and does not happen overnight. However, the following tips will help set things off on the right track.

Start with Initial Data Assessment and Cleanup

You cannot govern data unless you know what data you have and where it lives. Therefore, start with a thorough inventory to discover what PHI the organization collects and stores, where it lives, who owns it, and how it is used. Also determine who has access to the data. This information will aid in building a risk profile and determining next steps.

Once you have a view of the organization’s data, classify the data based on sensitivity and prioritize it for protection. This initial data assessment period also presents a good time to perform some data cleanup. This will include resolving duplicate records and archiving or removing data no longer needed. Always refer to regulatory requirements.

Review and Update Access Privileges

Because the patient record plays a pivotal role in care delivery, healthcare organizations need to carefully control access to this information. Access management involves defining who has access to what data and under what circumstances. Work toward a state of least privileged access, in which users have just the authorization they need to do their job and no more.

Controlling access to PHI may also include updating authentication processes to definitively verify the identity of anyone attempting to access sensitive information. Multi-factor authentication (MFI) and other modern authentication methods will prove essential.

Leverage Technology Wisely

Technology can streamline the process of healthcare data governance and reduce errors. For example, biometric scanners reduce the chance of misidentification, and software using AI-powered referential matching can prove effective in reducing duplicate records. Likewise, digital compliance solutions provide critical compliance monitoring and simplify data classification.

When choosing and implementing technology, look for solutions made to scale easily as data sets continue to grow. Also prioritize tools that integrate with existing clinical systems. And utilize robust security systems that use encryption and other protective measures to secure data during transmission and storage.

Build a Culture of Data Governance

No amount of technology, however, will take the place of building a corporate culture of data governance. Engage with stakeholders such as providers and patients as you develop a data governance framework. An essential part of that framework will include defining data governance roles and responsibilities.

Then promote data literacy by providing training for all employees on data governance policies and best practices. For instance, standardized procedures around collecting and updating patient data will assist in preventing errors and duplicates. Formal training and just-in-time reminders will help.

Take Data Governance to the Next Level

Effective data governance takes time and requires careful planning. The eMazzanti team of data experts brings decades of experience in effective data governance and data security consulting and solutions. We can help you design and implement a comprehensives strategy designed to protect data assets and drive both efficiency and innovation.

The post Data Governance in the Healthcare Sector Critical to Improve Health Outcomes and Compliance appeared first on eGovernance.

Data governance truly involves a cyclical process, and organizations must revisit the stages periodically as data sources and business processes change. While various regulatory environments and business needs involve unique approaches, this post outlines the stages of a standard data governance lifecycle.

1. Define the Purpose and Goals

Any successful data governance program starts with an assessment of the current data and regulatory landscape. While technologies and trends support data governance, strategic planning should focus on identifying existing risks and business priorities.

For instance, for many organizations regulatory compliance needs provide the initial motivation for gaining control of data assets. Another key driver might include improving data quality to support AI initiatives or other innovations.

Whatever the motivation, an initial step will involve identifying risks and practices that present obstacles to attaining business goals. For example, insufficient data security will compromise regulatory compliance. And poor data quality will reduce the effectiveness of decision making and AI initiatives.

2. Build a Roadmap

With an understanding of the reasons and goals for data governance in the organization, it is time to put together a detailed plan. Begin by identifying the data elements throughout the organization. Look at the types of data collected, the data sources and data owners, and how the organization uses the data.

Next, assess the quality of the organization’s data. Several factors affect data quality. For example, outdated or redundant data will reduce the accuracy of data-driven decision making. Likewise, incomplete data will diminish the reliability of data analytics. And inconsistencies across systems and processes can lead to confusion and errors.

Once you have a detailed picture of the data landscape and the challenges involved, you can construct a data governance plan. This strategic plan will include identifying the steps needed to implement data governance, the key stakeholders, necessary resources, and priorities.

3. Design the Data Governance Strategy

Primary goals of the data governance framework involve establishing standards for data management. They also involve ensuring data security and empowering employees throughout the organization to find and use data effectively.

First, the data governance framework will include determining data governance roles and responsibilities. Properly managing data involves input from stakeholders throughout the organization. Some common roles include data governance sponsors and council, data owners, data custodians, data stewards, and data users.

Next, comprehensive policies and procedures around data access, usage, management, and security form the backbone of a data governance framework. For instance, regulations like PCI DSS and HIPAA impose strict rules around data retention. And numerous privacy laws necessitate security policies to protect sensitive data.

4. Implement the Data Governance Program

With policies and procedures outlined and strategies documented, the real work begins. Technology can play a crucial role in this process. For example, automation can significantly reduce the time involved in labeling and tracking sensitive data. Likewise, automating data retention and destruction policies improves accuracy and defensibility.

Technology solutions can both reduce risk and save time. These solutions will include proactive data security, data compliance monitoring, and comprehensive records management, along with eDiscovery solutions that provide essential visibility into unstructured data.

Additionally, the data governance program must address the human factor, building a culture of data governance from top to bottom. Thus, the program necessarily includes educating and training stakeholders about data governance policies and procedures and their role in maintaining data integrity.

5. Continuously Monitor

Data governance requires continuous monitoring to ensure enforcement of data governance policies and proactively identify risks. This includes both regular audits and automated data compliance monitoring.

6. Review and Adapt

The final stage involves evaluating the effectiveness of data governance initiatives and refining processes and policies accordingly. As new data sets and security risks emerge, and as processes within the organization change, the data governance framework will need to change, as well.

Conduct regular reviews with senior executives and key stakeholders. Track progress against key objectives, measure results, and make any needed adjustments.

Leverage Expert Resources Throughout the Stages of a Data Governance Lifecycle

Many organizations effectively utilize information governance solutions to streamline the data governance process. Working with organizations like eMazzanti Technologies and Messaging Architects provides access to both extensive expertise and cutting-edge technologies.

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In essence, the data steward acts as the guardian of data assets, ensuring their accuracy, consistency, accessibility, and usability. Data stewards generally work in IT but collaborate closely with departments across the organization. Thus, they act as a bridge between the technical and business sides, requiring a unique skillset.

Data Stewardship Puts Data Governance into Practice

Data stewardship plays a pivotal role in effective data governance, translating policies into practice. A data governance framework includes the policies and guidelines for data handling, quality, and security. Data stewards have responsibility for ensuring the implementation and enforcement of these policies.

For example, to enforce data quality standards and ensure trustworthy data for decision making, a data steward might conduct regular data audits to identify inaccuracies or inconsistencies. They could then implement data cleansing processes to eliminate duplicate entries, correct errors, and update outdated information.

Or, to ensure that data access falls within regulatory compliance restrictions, the data steward might design and oversee role-based user access controls. This helps the company achieve a fine balance between data democratization and critical data security.

In implementing data governance policies, the data steward collaborates closely with other data governance roles. For instance, data stewards work closely with data custodians, who provide technical support and maintenance related to data. They also work in tandem with data owners in various departments, as well as the data scientists who retrieve and analyze data.

Responsibilities of a Data Steward

Data stewards oversee all activities related to data management. This multi-faceted role includes several main categories of responsibility:

• Data quality assurance – This involves monitoring data for errors or inconsistencies, as well as implementing processes to maintain data accuracy.
• Data security and compliance – Data stewards ensure the protection of data from unauthorized access. They implement security measures, make sure data usage complies with legal and regulatory requirements, and raise awareness about data security best practices.
• Data accessibility and usability – Stewards balance strict data security standards with making sure that authorized users can find and use data. This involves building a data inventory, maintaining data lineage, managing metadata, and so forth.
• Data advocacy – Data stewards promote data value by educating stakeholders on data quality principles, encouraging responsible data usage, and promoting strategic uses of data, such as data-driven decision making.

Businesses May Have Multiple Data Stewards

This responsibility list looks daunting and often proves more than one data steward can reasonably accomplish. Consequently, a company may assign multiple stewards to handle various aspects of data stewardship.

For example, the organization may assign separate data stewards to work with specific types of data, such as customer data. They may also assign stewards according to business function, such as sales or R&D. Alternatively, a data steward may have responsibility for one or more IT systems, such as email.

Skills to Look for in a Data Steward

Because data stewards cover both technical and culture-focused activities, they need a balance of various types of skills. Without a doubt, the job requires technical skills, including data modeling, databases, and database management systems. And in fact, data stewards often report up through IT.

But data stewards also need interpersonal skills and business acumen. Because they act as a liaison between IT and business functions, in addition to promoting good data practices throughout the company, they must be able to communicate and collaborate effectively. Additionally, they often act as subject matter experts in their area of responsibility.

Who are Data Stewards and Why are They Important for Your Business? Explore Key Benefits

Data stewards play a central role in implementing data governance and ensuring a healthy data ecosystem. They not only protect and manage data, but they also help the company navigate a complex data landscape and leverage data for strategic advantage. And as data plays an increasingly vital part in decision making and innovation, this role has become mission critical.

To set the stage for success, hire the right people and give them the tools they need to thrive. eGovernance solutions for information governance offer powerful, cloud-based solutions to help organizations use their information effectively and drive data value.

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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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Information governance encompasses the overall strategy for information across the organization. This includes identifying, categorizing, and storing data. It also involves managing information lifecycles, ensuring data security and supporting eDiscovery and regulatory compliance.

IT governance, on the other hand, ensures the technical foundation and support for information governance. By implementing information governance and IT governance in coordination, organizations optimize their use of information assets. At the same time, they improve risk management, ensure regulatory compliance and drive innovation.

Data Management

Data holds tremendous potential to provide insights, improve decision making, and inform strategy. At the same time, the sheer velocity of data poses challenges. Organizations ingest huge quantities of data in various formats from a wide range of sources. But if people cannot find and access quality data when they need it, it holds little value.

IT governance supports data management by providing the tools, policies, and processes to ensure data quality and availability. This includes supporting the classification and storage of data as well as controlling information access and sharing. It also involves enabling effective and responsible archiving and destruction of data.

For example, IT governance establishes the data architecture necessary to ensure that all team members can access accurate information, regardless of their location. It also provides data analytics and visualization tools to enable data exploration and reporting.

Risk Management

Any business activity entails risk that can negatively impact the company’s performance, reputation, and even its bottom line. Insufficient cyber security measures introduce the risk of data breach, for instance. Likewise, natural disasters and ransomware can interrupt business continuity and result in crippling data loss.

Risk management requires IT governance to provide the necessary resources and processes to enable risk identification, analysis, and response. For example, effective IT governance provides for risk assessments to identify potential vulnerabilities of IT systems and processes. It will also include the implementation of strategies and controls to close security gaps moving forward.

Additionally, risk management must include implementing data backup and recovery procedures as part of an overall business continuity strategy. And once the organization has identified risks and initiated mitigation strategies, ongoing IT monitoring enables timely resolution of any issues.

Compliance Management

Effective risk management forms a critical component of overall compliance management. Regulatory compliance plays a critical role in avoiding legal penalties, maintaining trust, and achieving competitive advantage. For many organizations, compliance challenges provide the initial motivation for developing and implementing information governance programs.

Here again, IT governance supports compliance management by providing necessary systems and processes to enable compliance evidence and improvement. This includes policies and procedures around data privacy and security. It also includes a strong reporting component to establish proof of compliance with applicable legislation and industry standards.

Innovation

Data plays an increasingly important role in guiding innovation. For example, by analyzing data from various sources, the company can understand what customers need and value. Data also provides insights into gaps and opportunities in current product offerings. With these insights, the organization can then design and deliver solutions specifically targeted to customer needs.

IT governance supports data-driven innovation by fostering a culture of experimentation and collaboration while providing the necessary resources and guidance. This often includes delivering the guidance and structure to responsibly and successfully leverage emerging technologies such as AI.

Optimize Use of Information Assets with Effective IT Governance

By coordinating IT governance and information governance, organizations increase the value of data assets while reducing risk, achieving compliance, and supporting innovation. This requires ensuring that IT infrastructure and operations are automated and integrated where feasible. It also involves using best practices to enhance information security, quality and availability.

Consider seeking external guidance and support as you develop your governance strategies. For instance, the consultants at eMazzanti and Messaging Architects bring a wealth of experience and tools to help you unlock the power of data for your organization.

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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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Evolving Privacy Regulations

Privacy law continues to drive information governance programs, and 2023 brings regulatory changes to key states. For instance, the California Privacy Rights Act amends the existing California Consumer Protection Act and takes effect on January 1.

The Virginia Consumer Data Protection Act likewise takes effect on January 1, followed by the Colorado Privacy Act on July 1 and Utah’s Consumer Privacy Act on December 31. In general, these laws apply to companies that conduct business in or offer products and services to residents of each state.

In fact, Gartner predicts that by the end of 2024, privacy regulations will cover the personal data of 75 percent of the world’s population. Rather than finding and securing personal data after the fact, organizations need to implement or strengthen privacy programs proactively.

Emphasis On Data Democratization

On the other hand, while a compliance mindset emphasizes restricting access to data, 2023 also brings an increased focus on data democratization. That is, data plays a critical role in decision making at all levels. Therefore, users in a wide variety of business roles, from sales assistants to executives, need ready access to quality data.

For employees to use data effectively, they need knowledge, as well as tools and access. Thus, data strategies must include data literacy education. Users need to know how to ask the right questions about data, where to find information and how to use it responsibly.

They also need the appropriate data tools, such as data warehouses and business intelligence (BI) technology, and knowledge about how to use them. And they need appropriate data access. This will involve approaching access management in a way that delivers the right access to the right people at the right time while ensuring compliance and security.

AI and Automation Will Prove Essential

Without automation, this increasingly complex data environment would prove impossible to manage. Consequently, automation—powered by artificial intelligence (AI) and machine learning (ML)—will prove increasingly essential in 2023 and beyond.

Automation plays a key role in finding and cleansing data, managing metadata, and performing other complex tasks with data. This both allows organizations to manage data at scale and empowers users to manage data on their own. It also helps to ensure regulatory compliance.

Hybrid and Multi-cloud Architecture is Here to Stay

Companies have been moving workloads to the cloud for several years. The pandemic accelerated cloud migration, and the trend shows no signs of slowing down. In fact, many companies spread important data assets across multiple clouds and on premises environments.

Managing data across these various environments can prove challenging and result in data silos. This fragmentation in turn leads to complications with data access and other information governance issues. Organizations will need to invest in technologies designed to find and manage data in a hybrid environment.

Metadata Management Remains Central to Effective Information Governance

Metadata will continue to play a central role in information governance. This “data about data” serves to identify and classify information, laying the groundwork for effective data management. For instance, metadata puts data in context and enables governance policies that determine data usage and lifecycle.

As organizations focus more deliberately on using data as a business asset, metadata takes on a greater role. But with large volumes of data arriving from a wide variety of sources, organizations must leverage technology to manage metadata effectively. For instance, AI can play a significant role in automating metadata management.

Translating 2023 Information Governance Trends into Strategy

Regulatory complexities, in combination with a hybrid work environment and the need for data democratization, will make for information governance challenges in the coming year. These challenges can feel overwhelming. But they become more manageable when organizations take a step-by-step approach.

A good first step to implementing an effective information governance strategy involves a comprehensive data audit. The audit will highlight what data you have and where it lives. Our consultants can also identify potential risks and help you implement a data management strategy designed to reduce risk and support business goals.

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Address the Risks of Unstructured Data

The bulk of the information generated by the average organization consists of unstructured data. Unlike structured data, which lives in tables or databases, unstructured data comes in a wide variety for formats.

For instance, it includes documents stored in file repositories such as SharePoint or OneDrive. It also includes emails, instant messages, videos and more. And because this unstructured data can live on multiple devices and in the cloud, it presents a significant challenge to security and regulatory compliance.

Consider healthcare organizations that must maintain strict adherence to HIPAA. Patients may email their care providers. Additionally, providers dictate notes from patient encounters, and doctors communicate in various methods as they collaborate to provide care. Strict regulations govern how that data is stored, transferred, and shared.

Reduce Risk of Privacy Law Violations

Data compliance monitoring helps organizations address potential privacy law violations early, reducing or eliminating legal issues. With the right tools, companies gain visibility into all data storage locations, both on-premises and in the cloud.

Using both automated and manual data classification, organizations tag sensitive data such as financial data or protected health information (PHI). They then set customized alerts so that compliance personnel receive immediate email notification of possible compliance issues. Companies can also automate certain remediation actions.

For instance, in the healthcare organization mentioned above, the system can be set to automatically tag PHI as sensitive data. If a user attempts to share PHI outside the organization, the system can be set to block the action and immediately alert compliance personnel.

Demonstrate Regulatory Compliance

Privacy regulations such as PCI DSS, HIPAA and GDPR dominate our data environment. Organizations must be able to demonstrate to auditors that they take all the proper steps to ensure that no unauthorized persons can access sensitive information. Additionally, customers demand assurance that companies treat their personal and financial information carefully.

Many regulatory agencies mandate monitoring as part of the criteria for achieving compliance. Monitoring demonstrates that the organization has implemented proper procedures and regularly enforces them. Additionally, if an issue does slip through, monitoring can help in reducing the negative repercussions.

Identify Security Vulnerabilities

In addition to achieving and demonstrating compliance, monitoring provides essential visibility into how data moves within and outside the organization. This allows security personnel and data stewards to pinpoint vulnerabilities and adjust information governance strategies accordingly.

Harness the Power of Automated Data Compliance Monitoring

Organizations manage huge caches of data in numerous formats on hundreds of devices. Such a complex data environment makes manual monitoring all but impossible. Fortunately, advancements in automated monitoring technology can help.

Not only can automation process large amounts of data rapidly, but with AI and machine learning the systems learn to identify risks and alert the right people. In many cases, the monitoring systems can automatically initiate necessary remediation, such as blocking a user from sharing sensitive data.

Automation also helps companies stay on top of the regulatory landscape. Laws and industry regulations continue to evolve, and it can prove challenging for organizations to keep abreast of all the applicable standards. Automated monitoring systems can scan for regulatory changes.

Partner with Data Compliance Monitoring Technology Experts

eGovernance solutions provide organizations with the tools they need to monitor data for compliance with regulations and with internal policies. Compliance personnel can track sensitive data and initiate steps to reduce or eliminate potential threats.

Along with compliance monitoring, customers gain access to consultants with deep expertise in information governance, archiving and eDiscovery.

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