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Data Fabric — A New Level of Data Management


May 11, 2022 - 5 minute read

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Małgorzata Caban Senior Content Marketing Specialist

She specialises in translation, writing and knowledge management. In her work, she combines her passion for languages with an interest in technology. Privately, she was part of a team of volunteers responsible for the Polish translation of “Baldur’s Gate: Siege of Dragonspear” video game.

See all Małgorzata's posts

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In today’s world, data is the fundament of any business. Companies collect ever-growing amounts of information, and data management methods have to keep up not only with the increasing volumes but also with changing market demands. The right understanding and usage of data are necessary to run your business efficiently and identify further improvement and growth opportunities. It becomes imperative to seamlessly connect and integrate all your data to achieve long-term benefits. This is where the data fabric comes into the picture as a concept of introducing a connective layer for all data assets and databases across the enterprise.

What Is a Data Fabric?

Gartner considers the data fabric one of the top strategic technology trends for 2022 and predicts that “by 2024, data fabric deployments will quadruple efficiency in data utilisation, while cutting human-driven data management tasks in half”. A data fabric is an emerging data architecture concept for unified data collection and optimised business usage. This allows organisations to achieve flexible, reusable, and augmented data management.

Data fabric architecture relies heavily on metadata, or “data in context”, to enable automated data discovery and access across the organisation. Data fabrics break from the status quo by leveraging knowledge graphs and applying artificial intelligence (AI) and machine learning (ML) to optimise integrations. The data fabric concept presents businesses with the potential to reduce manual data integration and management efforts and support the utilisation of integrated data layer across an organisation’s environment. Data fabric architecture is platform-agnostic, not location-specific, and doesn’t rely on particular use cases and data processes. Ultimately, it offers organisations the possibility to access, share, and govern their data efficiently and cost-effectively.

How is data fabric different from the traditional monolithic approach to data warehousing? One of the main issues in traditional data warehousing is that data is centralised to create a single source of truth upon which insights can be built. This approach is most effective in use cases where there is little change. In others, as data sources change and data volumes grow, the centralised approach to data warehousing can prove to be a bottleneck as the solution struggles to provide the right data at the right time. Moreover, it often requires data movement, which generates huge costs in analytical solutions.

A data fabric creates a unified view of organisational data assets. Data is still distributed in the analytical and operational data sets, but with enhanced metadata and data virtualisation, a data fabric improves information access and enables business units to turn raw data into actionable insights without complex ETL processes. Essentially, the traditional warehouse architecture is a reactive approach to data management, while data fabric architecture is designed to be a proactive one.

How Can You Benefit From a Data Fabric?

  1. De-silo your data
    Data fabrics help eliminate data silos, making previously isolated data accessible to other user groups within the enterprise. The unified data management framework improves transparency and removes inefficiencies as data can be easily found and shared with all users.
  2. Enable self-service capabilities
    Self-service data consumption options increase your overall business agility as business users across the organisation find relevant data faster and are empowered to draw valuable insights. At the same time, timely and reliable AI-powered recommendations improve the users’ trust in the data they consume.
  3. Automate governance and data protection
    A data fabric utilises active metadata to improve the quality of your data and enforce regulatory policies. AI-based automation accelerates the implementation of new governance policies and ensures the integrity of your data.
  4. Augment data integration
    With a data fabric in place, you’ll be able to optimise your data integration processes, remove the inefficient, manual tasks, and deliver real-time insights based on automated data analysis. According to Gartner, a data fabric can recover up to 70% of data discovery, analysis, and implementation tasks from a selected delivery team.
  5. Accelerate and inform decision making
    The improved transparency and reduced time-to-insights translate to a truly informed decision-making process. When you can collect the right data at the right time, you can ensure your company’s business resilience and growth.
  6. Adapt at your own pace
    In principle, data fabric can be adapted to your existing data integration, quality, and governance practices without forcing a full replacement. You can implement the concept gradually, over time, and in harmony with your current investments.

Planning Your Data Fabric Implementation

When planning the design of a data fabric, it’s worth remembering that there isn’t a single solution that will fit every enterprise. Data fabrics consist of several technical components whose maturity levels vary in every organisation. Similarly, the needs of each organisation differ significantly depending on what they want to achieve and their planned investments. Data fabric designs should therefore be customised to address your specific objectives and challenges.

Keeping this in mind, Gartner proposes a few practical steps to be taken when you begin designing your data fabric.

  • Assess your existing data management stack against the technology pillars of the data fabric design. Once you know your maturity level in each component, you’ll be able to plan your design accordingly and choose a technology partner that can best support your technical needs.
  • Perform a “metadata discovery” to identify patterns and connections among users, data, storage locations, transitions, and usage frequency. Understanding those patterns will help you lay the ground for the introduction of a data fabric.
  • Start with the foundational path of your data fabric, which will cover the known data and use cases, and the technical components of higher maturity. Later, you can move onto a more advanced path where you’ll augment your data management infrastructure design and data delivery.

Final Thoughts

mplementing a data fabric enables speed and efficiency, which, in turn, can lead to cost optimisation, better productivity, and improved decision-making. It can be a significant step on your digital transformation journey as it maximises the value of your data.

When exploring the concept, you should consider your organisation’s specific needs and technical maturity and tailor your design to address them. As the data fabric is still an emerging trend in data management, it’s advisable to analyse its benefits against your company’s business objectives. You can track the return on investment in areas such as accelerated time-to-market or a better understanding of usage patterns to make sure you’re exploiting the full value of your data fabric.


  1. Top Strategic Technology Trends for 2022: Data Fabric, Gartner, 2021
  2. Quick Answer: What Is Data Fabric Design?, Gartner, 2022
  3. Emerging Technologies: Critical Insights on Data Fabric, Gartner, 2022
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Małgorzata Caban Senior Content Marketing Specialist

She specialises in translation, writing and knowledge management. In her work, she combines her passion for languages with an interest in technology. Privately, she was part of a team of volunteers responsible for the Polish translation of “Baldur’s Gate: Siege of Dragonspear” video game.

See all Małgorzata's posts

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