How Data Management Frameworks Can Enhance Data Security

As cloud technology becomes a centerpiece of business operations across all industries, the challenge of managing vast amounts of organizational data is escalating drastically.

Companies are embracing cloud solutions to the point of ubiquity, and the sheer volume and complexity of data that needs to be managed, shared, and protected have grown exponentially. This surge in data brings with it the responsibility of ensuring effective data management — which means organizations must implement robust data discovery processes and comprehensive risk assessments.

Especially for companies subject to stringent regulatory standards, the requirement to meticulously manage access to confidential and sensitive data has never been more critical. This extends beyond everyday operations to include key business processes such as mergers and acquisitions, which require establishing secure and efficient data management protocols.

One of the key challenges here is that critical sensitive data is often concealed in less obvious, hard-to-navigate locations, such as deep within financial analysis spreadsheets or embedded in complex business forecast reports.

So, how can organizations effectively navigate, analyze, and orchestrate their data to ensure it is accessible but also secure and compliant with various regulations?

That’s where data management comes in – a strategic approach essential for navigating the complexities of today’s data-rich business environment. Effective data management is crucial for organizations to not only make informed decisions and maintain operational efficiency, but to stay competitive.

This blog explores the essentials of data management frameworks, explains how they compare to data governance, and highlights how Concentric AI fits into the secure data management picture.

Understanding Data Management Frameworks

The core concept of data management frameworks is simple: a comprehensive system designed to handle an organization’s data effectively and efficiently. These frameworks are more than a collection of practices; they provide a strategic approach to managing one of the most critical assets of any organization: its data.

Here are the key components:

Data governance: This is the most strategic layer, setting the vision for how data should be managed. Governance involves defining roles, responsibilities, and processes to ensure data accuracy, availability, and security. It’s about creating a culture where data is recognized as a valuable asset.

Data quality: Central to any data management framework, quality is about measures that establish the accuracy, completeness, and reliability of data. This includes establishing data standards, implementing validation processes, and continuously monitoring data quality.

Data integration is about creating a unified view of data from disparate sources. Typically, it involves techniques and tools for data extraction, transformation, and loading (ETL), ensuring that no matter where the data comes from, it is compatible and can be used cohesively.

Data security: As the threat landscape escalates, protecting data from unauthorized access and breaches is table stakes. Examples include data identification and classification, access control, encryption, and regular security audits.

Data privacy: With regulations to adhere to, like GDPR and CCPA, ensuring personal data is handled legally and ethically is crucial, and requires an understanding of the legal landscape and implementing processes to comply with these laws.

Data retention: Retention is making sure data is stored for the appropriate amount of time, balancing the need for information with legal and regulatory requirements.

Data architecture: Architecture involves designing the structures that hold data, ensuring they support the organization’s needs. Optimally, models should be created that are scalable, flexible, and efficient.

Data analytics: Typically, the key objective of data management is to extract insights. This involves tools and processes for data warehousing, mining, and visualization — turning data into actionable intelligence.

Data Management vs. Data Governance

While data management and data governance are similar, an effective data strategy requires an understanding of what makes them different.

Data management is all about the execution: a practical application of policies and procedures to handle data effectively across its lifecycle. Data management encompasses the technical and procedural aspects, from how data is stored and archived to how it is transformed and analyzed. It’s a broad field that includes data quality, integration, security, and more.

Data governance, on the other hand, is more about oversight and strategy, providing the framework that guides data management activities. Governance involves setting policies, standards, and procedures for data management and ensures that data is used ethically, compliant, and aligns with business objectives. Governance also establishes whether data management activities align with the organization’s broader goals.

Ultimately, while data management is about the ‘how’ of handling data, data governance is about the ‘why’ and ‘what’.

Governance sets the rules and objectives; management ensures those rules are followed and objectives met.

How Concentric AI helps organizations leverage data management frameworks

You may be aware of Concentric AI’s ability to help with Data Access Governance (DAG), but did you know that our Semantic Intelligence solution can play a significant role in helping companies leverage data management frameworks?

Here’s how.

Data governance and compliance: Concentric AI aligns with data governance by ensuring that data access policies are defined and enforced easily and autonomously.

Data quality assurance: By providing tools for comprehensive data discovery and classification, Concentric AI helps to ensure the quality of data.

Data integration and accessibility: Concentric AI enhances data integration by providing better access to relevant data across various repositories — in the cloud, on-premises, structured or unstructured.

Data security and risk management: A core strength of Concentric AI is its ability to enhance data security. By autonomously monitoring data access and identifying risks, Semantic Intelligence helps prevent unauthorized access and potential breaches, protecting data across its lifecycle.

Data privacy and protection: Concentric AI’s solution contributes to data privacy by managing who has access to sensitive data and ensuring that this access is compliant with privacy laws and regulations.

Boosting data analytics: By ensuring that only quality, relevant, and secure data is accessible, Concentric AI indirectly supports data analytics initiatives.

Leveraging Concentric AI’s advanced capabilities, organizations can be confident that their data management frameworks are not only comprehensive, but also equipped to handle the complexities of modern data environments.

Want to see for yourself, with your own data, how Concentric AI can help with data management frameworks?

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