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Understanding Data Assessment: keys to effective Data Governance
Data Assessment is a critical process that ensures the accuracy, relevance, and security of organizational data. Within the framework of NIST 2.0 and ISO 27001, it plays a pivotal role in safeguarding data integrity and meeting compliance requirements. By identifying potential risks, evaluating data quality, and ensuring secure management, data assessment empowers organizations to proactively address vulnerabilities.
However, Data Assessment can often be a resource-intensive activity, both in terms of time and effort. To accelerate compliance projects and streamline processes, leveraging advanced software platforms that integrate AI for seamless access to both structured and unstructured data becomes a strategic requirement.
The NIST 2.0 approach: NIST Research Data Framework (RDaF)
The NIST 2.0 Research Data Framework (RDaF) Lifecycle: a methodology for effective Data Asset Management
The Research Data Framework (RDaF) Lifecycle Stage Graphic represents a cyclical process for managing research data, developed by NIST. This lifecycle approach consists of six interconnected stages that help guide organizations and researchers through managing research data effectively. The stages include:
Envision: Focuses on setting the strategies and objectives for a research data program, aligning it with broader organizational goals.
Plan: Involves preparing for data acquisition, selecting formats, and planning for storage and sharing.
Generate/Acquire: Deals with producing or gathering research data, either through experimentation or external sources.
Process/Analyze: Pertains to processing and analyzing raw data, often using software tools, to derive meaningful insights.
Share/Use/Reuse: Covers how research data is shared, used, or reused both within and outside the organization, addressing constraints or incentives.
Preserve/Discard: Involves archiving or safely disposing of data once it is no longer needed, ensuring proper records management.
The stages are interrelated, meaning any stage can lead to another, and organizations may address multiple stages simultaneously based on their data management needs​
Transforming Data Assessment: the role of automation in streamlined assessments and compliance
Transforming Data Assessment: accessing Unstructured and Structured Data across platforms
Automating data assessment processes is becoming standard across industries to reduce time and effort in data management, especially when dealing with terabytes of heterogeneous data. By leveraging advanced AI and Machine Learning (ML) tools, organizations can streamline their data discovery, classification, and risk analysis processes.
Automation minimizes manual workloads, enhances accuracy, and enables efficient cross-functional collaboration, all while ensuring compliance with regulatory standards.
E-Venture Business Solution employs state-of-the-art software to automate Data Assessment. These solutions are designed to handle both structured and unstructured data, accessing data sources on-premises and in the cloud without requiring significant hardware or software investments from clients.
This cutting-edge approach allows organizations to efficiently manage their data, remain compliant with regulations such as international Regulators (e.g. Central Banks), the European General Data Protection Regulation (GDPR) and Swiss Federal Act on Data Protection (LPD/DSG), mitigate risks, and foster internal collaboration for a thorough and effective assessment.
Optimizing Data Assessment: key goals and effective processes
A comprehensive automated data assessment not only locates critical data but also ensures that organizations maintain data integrity, meet legal obligations, and foster internal collaboration. Below, we outline the primary objectives of a data assessment and highlight the role of automation in streamlining the process.
Locating Sensitive Data: Identifying sensitive information, such as Personally Identifiable Information (PII), Payment Card Information (PCI), and Protected Health Information (PHI), is critical for compliance with data protection regulations. Automated tools simplify the process by detecting and analyzing these data types across various formats and storage systems.
Risk Prioritization: A well-executed data assessment enables organizations to prioritize their data based on sensitivity and associated risks. By classifying and evaluating data exposure, businesses can focus on protecting their most valuable assets and proactively address potential vulnerabilities.
Supporting Legal and Compliance Obligations: Meeting regulatory requirements—such as those from central banks, GDPR, LPD/DSG, and industry-specific standards—is a legal necessity. Automated data assessment tools generate comprehensive data inventories, ensuring compliance with minimal manual intervention.
Improving Data Governance: A robust assessment process strengthens data governance by continuously monitoring data lifecycles, access controls, and user activity. Automation supports seamless governance, ensuring data integrity across systems without the need for significant IT adjustments.
Enhancing Internal Collaboration: For data assessments to succeed, tasks must be distributed across relevant departments, including IT, Legal, Compliance, and Data Governance. Each department’s expertise contributes to a comprehensive review, covering regulatory and operational risks.
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