Data Fabric is a fast-emerging technology architecture or framework where all sources of data and their components across an enterprise are made available to all computing entities within that enterprise through a common interface. These entities could either require this data for further processing or be points of generation of new data.
This fabric of data is made available across the enterprise via software services or connectors. A data fabric can be visualized as a unified collection of all data-related assets, databases, and data architectures. The final purpose of the data fabric is to ensure all data across the enterprise is normalized, is of the highest quality, and is accessible to all computing points within the enterprise.
The Function of the Data Fabric
A data fabric is a technology layer that allows applications and services across the enterprise to connect to all data-related entities within the enterprise via a common interface. It is a unified view of all data spread across the enterprise regardless of the type, source, and location of the data. Methods are made available to access this layer so that all applications across the enterprise can access this common data layer.
A data fabric does away with the concept of application silos and their data silos and introduces a common unified view of all data for all applications to access. This helps in the processing of data, its management as well as its storage. Due to the unified view, there is the much-required continuous normalization and optimization of data within the enterprise as well.
Benefits of Having a Data Fabric
Adopting the data fabric architecture eliminates the need for data silos.
Data Management Becomes Unified and Simpler
This unified management ensures data is of a higher quality and considered trustworthy. With the implementation of a data fabric, integration and sharing become simpler, along with much greater scalability. Enterprises can now add new data sources to the fabric, endpoints, and technologies without breaking existing deployments and connectors.
A unified approach to the data fabric leverages the advantage of the cloud as well as ensures that the migration between different cloud architectures becomes easier. Moving away from legacy applications and obsolete architectures becomes less cumbersome as well.
Applications and services can connect to any data source within the enterprise using pretested connectors and functional APIs that are shipped along with the architecture of the data fabric. This allows for ease of integration and data sharing across the enterprise. All applications and services begin to use a single source of data within the data fabric, thus ensuring the normalization of data.
Big Data Migration and Analysis
As the data fabric is inclusive of all types and sources of data across the enterprise, big data migration and analysis of the same becomes easier to manage and process. Analysis dashboards gain access to all sorts of data across the enterprise, thus making reporting and decision-making more effective and accurate.
Since big data access becomes easier with the existence of the data fabric, projects and workflows which are dependent on machine learning and artificial intelligence are easier to implement with faster operational deployment.
Data Governance and Compliances
The data fabric ensures data governance and compliances are faster to implement, monitor, and control since there is a common view or architecture of all data across the enterprise.
All data across the enterprise can be accessed via pre-packaged and pretested connectors and methods in a data fabric. This allows for applications to access managed data across all departments regardless of format, structure, location, and business points.
Overall, business analysis, reporting, forecasting, and operational optimizations become efficient and accurate with the unified data architecture of the data fabric. This allows for better customer engagement, logistics, marketing & sales, as well as corporate communication within the enterprise.
Challenges in Implementing a Data Fabric
The biggest challenge of implementing a data fabric is the initial harmonization of data that is already existing within the organization.
- Data sources, structures, silos, and locations must be identified and unified.
- Various data-related platforms need to be unified such that they can be included in the data fabric.
- Application and data silos must be done away with to implement an efficient data fabric.
Implementation of a data fabric must ensure that all data across the enterprise is included and should be extendable across the whole organization. Having subsets of data not included in the fabric decreases the effectiveness and efficiency of the data fabric.
Once the data has been unified, access to the same must be via common connectors and technologies. These connectors should be operational with existing data APIs or query mechanisms so that existing applications will not stop running. The data fabric not only depends on the harmonization and unification of data but also on the technologies being used to connect to it.
To unify or harmonize data for the data fabric, virtualization technologies are to be used. The applications within the enterprise should be able to use this data which is now location-independent. This means a lot more processing power and bandwidth will be required for an enterprise adopting the data fabric architecture.
The Competitive Advantage
In the era of digital transformation, it is the ease of data access that drives innovation and the pace of business excellence. Data needs to be accessed faster and more effectively to serve business workflows and customer expectations. Data is valuable; it needs to be managed, stored, sanitized, and accessed in the best possible way. To that end, adopting the data fabric should be the path for any organization to grow in the digital era.
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