How Data Fabric Helps Address Multi-Cloud Sprawl

The abundance of data facilitates good decision-making, but too much of it can be problematic. Indeed, collecting too much business data can be wasteful, as it’s unlikely to ever be used to derive any useful insights, but it’s also likely to be stored across multiple repositories, creating hassles around data management and data security. Database managers at your business might not even be aware that some of these resources exist.

This phenomenon, referred to as “data sprawl,” is particularly pronounced among organisations that manage multi-cloud environments. This setup comes with the inherent challenge of having to account for information scattered across various cloud platforms. There is a tendency for organisations that use multiple cloud platforms to have visibility issues, inconsistent implementation of security policies, data siloing, and difficulties when it comes to regulatory compliance.

In response to the data sprawl problem, data fabric can provide a good model for a solution. Data fabric is an approach in IT architecture wherein organisations manage their data in different environments to achieve unified access, governance, and integration. It supports organisations as they deal with the challenges of data fragmentation and overload in complex IT environments.

Specifically, data fabric provides the following mechanisms and functions to combat data sprawl across multiple cloud environments.

Enhanced Security

The idea of data fabric focuses on using a set of technologies and tools to establish a data management architecture that efficiently addresses governance, integration, and access across multiple sources. It provides a systematic way to manage data to overcome challenges, especially those brought about by the reliance on multiple platforms.

Marketed as data security fabric (DSF) by security firms, solutions that leverage the concept of data fabric are characterised by their emphasis on providing adequate and appropriate security for multi-cloud environments, scalable data security, and compliance at scale. They offer security features that target the changing security needs of data storage and processing across different platforms.

Adequate and appropriate data security in the context of different data storage topologies requires more complex data encryption practices to address more sophisticated attacks while ensuring efficiency. It needs to deal with cloud provider-specific security features, which can be chaotic to manage through conventional means. Organisations also need automation and continuous monitoring to keep up with the deluge of data. Additionally, it is important to centralise the management of user identities and access requests to comprehensively and efficiently manage data hosted and processed on different platforms.

On the other hand, scalability in securing data is a must for multi-cloud environments. As organisations rapidly expand and onboard more cloud platforms, they inevitably broaden their attack surfaces. Hence, there is a need to efficiently integrate new systems, security tools, and policies.

Moreover, it is important to keep up with data security regulations, especially when expanding into regions that impose different sets of laws or rules on data collection, retention, and protection. Compliance may sound easy, but it can get complicated when working with varying regulatory requirements in different locations.

Data fabric provides the framework to guide organisations in dealing with the threats that emerge as a result of using multiple clouds as well as hybrid environments that include on-premise data storage. It presents the tools and security best practices necessary to anticipate, respond to, and mitigate attacks.

Integration and Unification

Data fabric is notable for being an integration powerhouse. Designed to seamlessly bring together multiple environments, it serves as a robust bridge that connects disparate data sources while allowing data to move with ease between different systems and platforms. 

Data fabric streamlines technological intricacies by simplifying the details required for transferring, altering, and merging data throughout the enterprise. It dismantles the obstacles of data movement. In the process, it allows organisations to manage data more efficiently to support the assimilation of disparate data, break down data silos, and enable comprehensive analysis. 

All relevant data are put to good use, while those that are deemed unnecessary are archived or destroyed to free up space.

Additionally, data fabric creates a virtual data layer for an organisation’s IT infrastructure. It provides a unified way to view and manage all data. It does not matter where the data resides or which cloud platform hosts it. Everything is viewable and manageable through a virtual data layer that simplifies data discovery and ensures secure access for authorised users.

Moreover, this combination of integration and unification facilitates standardised management for all of an organisation’s data. It supports the consistent implementation of data governance policies, ensuring adherence to data quality standards, access controls, and integrity measures. There is a sense of predictability in how data is treated, which makes it easier for teams to find, manage, and use data.

Automation and Machine Learning

Data fabric may also leverage automation and artificial intelligence to counter data sprawl. Various repetitive tasks in multi-cloud data management can benefit from automation. Also, machine learning can significantly improve data handling tasks that would take a long time when done manually.

Cloud database security managers can automate the process of data discovery and cataloguing. These time-consuming and typically error-prone tasks can result in various mistakes that contribute to data sprawl. Also, automation plays a crucial role in data movement and transformation. There are many instances when unnecessarily redundant copies of files are kept because of unplanned or disorganised file movements. 

Data transformations that are not thoughtfully planned out like in the case of business data calculation and reporting can also mess up data management. Organisations can use data fabric to automate data lifecycle management and ensure that data is optimally utilised and carefully disposed of.

Data fabric solutions can take advantage of machine learning for various purposes. For one, AI helps intelligently discover and classify data, especially for organisations that gather, generate, and process vast amounts of data. Artificial intelligence is also useful in anomaly detection and data lineage tracking. 

Additionally, machine learning is important in rapid automated data curation and optimisation, particularly when it comes to data deduplication, archiving, and making decisions on which data to delete. AI also facilitates predictive analytics for proactive data management, allowing admins to automatically get rid of unwanted and unneeded data that take up limited storage space.

Tidying the Sprawl

Given the increasingly rapid generation of data, sprawl is a reality that modern organisations face. A lot of this data is likely to be useful, but unnecessary data proliferation also happens frequently – especially when using disjointed and disorganised data storage and processing platforms. Data fabric provides a highly viable model for how to address data overload and manageability in the age of multi-cloud and hybrid environments.

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Hazel Raoult is a freelance marketing writer and works with PRmention. She has six-plus years of experience in writing about business, entrepreneurship, marketing, and all things SaaS. Hazel loves to split her time between writing, editing, and hanging out with her family.

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