Traditionally, eDiscovery has been primarily associated with legal proceedings. However, as the role of data and the regulations surrounding data storage and use have evolved, the use of eDiscovery has also expanded. eDiscovery tools and processes can play a pivotal role in the data lifecycle by enabling organizations to locate and access quality data swiftly.

Consider the following eDiscovery practices that support powerful data governance.

Ensure Effective Data Identification and Classification

Data involved in eDiscovery must be searchable and accessible. Additionally, a clear chain of custody for all data ensures the preservation of data integrity, a fundamental goal in eDiscovery.

Achieving these goals requires proper indexing and classification of data through effective metadata management. This in turn makes it easier to manage the data lifecycle and retrieve relevant information when needed.

Today’s platforms offer many different locations and service providers to store information, including localized to computers, internal computer networks, and the cloud. Understanding and controlling the location of information by type is critical to avoid deduplication of effort and having conflicting or out of date documents and information. Data classification is nearly as important as where data is stored.

Prioritize Data Security and Confidentiality

To ensure regulatory compliance and prevent spoliation of data for litigation purposes, companies must focus on data security and privacy. Best practices include encrypting sensitive data and implementing access controls. Organizations should also regularly audit security protocols to prevent data breaches.

Data security and privacy also prove essential for effective data governance. Consequently, this focus on protecting sensitive data and controlling data access accomplishes two critical goals at once.

Automate Where Possible

As the data environment grows increasingly complex, legal teams can find themselves bogged down with identifying and collecting data. Automating many routine and time-consuming tasks frees up legal counsel to focus on doing what they do best. It also allows teams to establish case strategy more quickly while improving accuracy.

For example, automation streamlines tasks such as data identification and classification, deduplication, redaction, review, and analysis. This saves money in eDiscovery while simplifying data governance.

Understand the Benefits and Dangers Posed by AI

AI figures prominently in automating data tasks. It also improves speed and accuracy by quickly combing through huge datasets to identify certain types of information such as account numbers or protected health information. And by identifying trends and anomalies that human reviewers might miss, it helps to uncover hidden insights.

At the same time, however, AI introduces several significant risks. For instance, the accuracy of data used to train AI systems will affect the quality of the data classification the system produces. AI may also introduce unintended bias and privacy concerns. Consequently, while AI may prove essential, business leaders must understand the risks and use the technology wisely.

Leverage eDiscovery Technology

Utilizing eDiscovery tools not only streamlines the eDiscovery process but also reinforces data governance. For example, cloud-based and SaaS eDiscovery solutions provide increased accessibility, scalability, and flexibility. Also, by design, they handle vast amounts of data more efficiently and facilitate seamless collaboration across multiple teams and locations.

When choosing technology to support eDiscovery and data governance, organizations should look for solutions that provide robust security while scaling to handle large and complex data volumes. And to ensure these solutions deliver value, choose tools that are intuitive, easy to learn and use.

Choose Your eDiscovery Vendor Carefully

Finally, as both the data environment and the legal landscape continue to evolve, organizations will benefit from partnering with an experienced eDiscovery vendor. But take time to vet potential vendors carefully, as the decision can significantly impact both legal outcomes and data quality.

Ask detailed questions. For instance, you should prioritize vendors that can demonstrate specific experience in your industry. Also look for providers that have implemented stringent security measures to protect sensitive data and that can ensure compliance with applicable regulations.

Implement eDiscovery Best Practices for Effective Data Governance

eGovernance provides cloud-based solutions 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. We provide fully managed solutions with access to subject matter experts in the fields of Information Governance, records retention policies and eDiscovery.

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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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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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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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