Rapid advancement in technology has led to large volumes of data from a multitude of sources that has become one of the main challenges of modern companies. Large amounts of data are a challenge to maintain and making sense of the most complex of them, even more so. Businesses and consumers today demand a lot from data, and they expect capabilities that will transcend traditional use cases and enable digital transformation. Developments in AI, machine learning, and other cognitive computing capabilities have taken business use cases from low-latency storage systems to real-time analytics, which helped accelerate application and product development.
To leverage the power of real-time analytics, businesses have been rethinking their approach to the gathering and operationalizing of data. The data fabric is one of those approaches that shows great potential of becoming a mainstream solution. Data fabrics allow you to “visualize” your data in a way that data lakes don’t. Seeing your data in motion allows you to keep track of data and make it simpler to migrate them to another platform.
What is a Data Fabric?
A data fabric simplifies integration of data management and processing across on-premise, cloud, and hybrid systems. Even if data is stored in multiple locations and always in motion, business applications can access data securely through a consolidated data management platform. Data fabrics allow integrated computing components to both work independently of each other when necessary and allows for complete data visibility. Thi, in turn, helps provide actionable insights, ensures data security and integrity, and makes overall data control more efficient.
As data volume grows and the technologies that harness it evolve, the data fabric will become an indispensable tool in enabling the digital transformation of an organization. The data needs and the ways a business changes because of these needs will vary, but data management will be less complex with the help of the insights provided by a data fabric. It’s highly adaptable and an ideal solution to addressing the ever-changing computing needs of forward-thinking companies. It helps enable digital transformation by making data accessible to end-users whether data is stored on the premises, in the cloud, or within a hybrid environment.
Using in-memory computing technology, a data fabric is able to offer the following features:
- Single-point access to all data regardless of data structure and deployment platform
- A common process for all data and deployment platforms via a centralized service-level management system
- Consolidated protection for all data, including data backup, security, and disaster recovery
- Unified data management through a single framework
- Cloud mobility for fast migration between cloud platforms
From Data Fabric to Data Mesh
Larger companies mean larger volumes of data. As such, these companies resort to multiple applications to gather and process the data they need. The challenge here is making sense of all the structured and unstructured data gathered and processed in silos from disparate sources, including cloud storage, transactional databases, data warehouses, and the like. A data fabric helps stitch together current and historical data from these silos without the need to replicate all the data into another repository. Through a combination of data virtualization, management, and integration, a unified semantic data layer is created, which helps in accelerating data processing and other business intelligence processes.
A static data infrastructure can only do so much. As the data fabric becomes more dynamic, it has developed into what is referred to as a data mesh. A data mesh is a distributed data architecture that’s supported by machine learning capabilities and follows a metadata-driven approach. It distributes ownership of domain data across business units so data is owned at the domain level. These domain datasets are then made available to the different teams within an organization. A data mesh provides dynamic data pipelines, reusable services, and a centralized policy of data governance.
For end-users, the most beneficial aspect of a data mesh is its data catalog, a centralized discovery system that’s available for all users. It allows multiple teams to access a single data catalog to obtain critical information that’s indexed in this centralized repository for quick discoverability. Data consistency is also maintained across domains by allowing for interoperability and delivering standards for addressability between domain datasets.
The Business Case for Data Fabrics
High-growth firms have seen an increase in data requests in recent years, with market data, social media data, and marketing and CRM data at the top. This is followed closely by financial data, risk and compliance data, operational data, and macroeconomic data. This trend shows an increasing reliance on data and analytics to gain fundamental insights that can be used to create new strategies and business models. Companies strive to gain better business outcomes through modern technology, but most of them fail to anticipate the technical feasibility and constraints that a digital transformation entails. This is why investing in in-memory analytics solutions like a data fabric is a sound business investment—one that will more than pay off in the long run.
Today’s business applications fall under three main categories based on how they produce and consume data:
- Legacy applications that serve a single function and are often on-premises
- Data warehouses that act as a middleware repository for frequently accessed data
- Cloud-based platforms and integrations that require the most data to serve a host of use cases
The road to being a data-driven business begins with establishing an efficient and secure connection between these three layers. This is where the data fabric comes in. A data fabric is designed to provide an architecture for better data governance between interconnected systems. Whether an organization uses an on-premises database, cloud-native platform, or a hybrid analytics platform, a data fabric eliminates the need for moving data from one location to the other. This ensures that data remains intact and minimizes bottlenecks by reducing data movement to and from disk and within the network.
Conclusion
Digital transformation used to be a term for tech companies that relied on modern technology to ensure business success. This definition, however, has become the typical definition for all companies that are trying to make it in this constantly evolving data-driven landscape. Big data has become “bigger data,” and it shows no signs of slowing down.
As data is gathered, regardless of platform, it will be decentralized and distributed. Without a unified data management strategy, collected data will be useless, its potential reduced to meaningless ones and zeroes. A data fabric helps navigate the complexity of data processing and management by providing a clear visualization or“big picture” view of an organization’s data in real time. There’s no need for complex codes with a data fabric’s pre-packaged connectors that allow connection to any data source without the assistance of developers and data analysts. This brings data closer to the user and the costs down to a very reasonable amount.
Understanding the architecture of data systems is vital in the digital transformation of a business, but it should be something that makes the process easier instead of harder. The integration of a data fabric in your data systems and strategy will do just that.
