Data fabric has recently entered the data management and analytics buzzwords dictionary. In 2021 and beyond, data fabrics will be critical to data and analytics transformation. Data has evolved into a critical component of delivering business value. As a result, an increasing number of businesses are looking to implement a data fabric architecture. Now, you might wonder what data fabric is and how it can lead to greater business value. Let us understand it in the simplest terms.
What Is Data Fabric?
In its most basic form, a data fabric is a single environment comprised of a unified architecture and services or technologies that run on that architecture to assist organizations in managing their data. Data fabric aims to maximize the value of your data while also accelerating digital transformation. It creates a network of information to power apps and artificial intelligence (AI).
A data fabric supports the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms, by performing continuous analytics on existing, discoverable, and inferred metadata assets.
Data fabric uses both human and machine capabilities to access data while it is still in place or to support its consolidation where appropriate. It constantly identifies and connects data from disparate applications to discover unique, business-relevant relationships among the available data points. The insight facilitates reengineered decision-making by providing more value through faster access and comprehension than traditional data management practices.
The Five Pillars Of Data Fabric Hybrid Cloud Are As Follows:
- Data fabric must collect and analyze all types of metadata. There should be a mechanism that allows data fabric to identify, connect, and analyze all types of metadata, including technical, business, operational, and social metadata.
- Data fabric must be capable of creating and curating knowledge graphs and enabling analytics on knowledge graph metadata.
- Data fabric must convert passive metadata to AI/ML-assisted active metadata for metadata activation and recommendation. To ensure frictionless data sharing, enterprises must activate metadata.
- A strong, AI-powered data integration backbone is required for data fabric.
- The data fabric must be able to automate data orchestration with the help of AI/ML.
Data fabric is more than just a mix of traditional and modern technologies; it is a design concept that focuses on human and machine workloads. The design improves data management by automating repetitive tasks like dataset profiling, aligning and designing schema to new data sources, and, at its latest, healing failed data integration jobs.
The Purpose Of Data Fabric
The complexities of modern data management are rapidly increasing as new technologies, data types, and platforms are introduced. Each technological shift is difficult and disruptive to change and strengthen data management methods. As a result, the traditional approach to data management has become unsustainable as technological innovation accelerates.
A data fabric can reduce disruption by implementing a highly adaptable data management strategy that includes augmented data integration and management.
A data fabric is unconcerned about deployment platforms, data processing methods, data delivery methods, locations, or architectural approaches. By abstracting complexity, it facilitates data as a strategic asset. A data fabric ensures that any data on any platform from any location can be efficiently and effectively combined, accessed, shared, and governed.
Benefits Of Data Fabric:
A data fabric warrants speed, resilience, and efficiency, which leads to lower costs, increased productivity, and a shorter time to value. It is also helpful in fueling digital business transformation. Here are some examples:
- Whisk self-service data discovery and analytics make trusted data readily available to all data consumers.
- It automates data engineering tasks and augments data integration to provide real-time insights.
- Utilizes active metadata to automate data governance and protection for data quality improvements, data curation, data classification, policy enforcement, and more.
- Workload orchestration is automated, as are elastic scaling, self-tuning, and self-healing, preparing your jobs for any environment and data volume.
- Automates the process of connecting discovered data assets and enriching them with knowledge and semantics, allowing consumers to find and understand the data.
Enterprise data fabric connects multiple locations, types, and sources of data. As data moves through the data fabric, it can be processed, managed, and stored. Internal and external applications can also access or share the data for a wide range of analytical and operational use cases across all organizations, including advanced analytics for forecasting, product development, and sales and marketing optimization.
The objectives are numerous, including increasing customer engagement through more advanced mobile apps and interactions, complying with data regulations, and optimizing supply chains.
How Data Fabric Can Increase Business Value
Every business relies on data to gain a competitive advantage. We live in an era of unprecedented change, including changes in the momentum of business and innovation. Therefore, data drives competitive advantage for every business, and organizations must quickly deliver data to serve business and customer needs.
It is not easy to succeed in this environment and become a data-driven organization. There are numerous impediments to becoming a digital leader. As businesses use more applications, their data becomes increasingly archived and inaccessible beyond its initial scope.
Today’s typical business has data in multiple on-premises locations and multiple public and private clouds. Furthermore, data is both structured and unstructured, and it is stored in a variety of formats, including file systems, relational databases, and so on.
With the increase in data integration tools among businesses, it becomes extremely difficult for organizations to be agile and quickly ingest, integrate, analyze, and share their data and incorporate new data sources. The problem will only worsen as the amount and variety of data available grow. As a result, data professionals spend 75% of their time doing things other than data analysis.
Not only does this significantly impair organizations’ ability to get the most out of their data promptly, but it is also a wasteful and inefficient use of your data professionals’ time. Aside from the obstacles preventing organizations from having quick access to data, many issues make the data untrustworthy. For example, nearly half of all enterprise data has integrity problems. And it is ten times more expensive to complete any work that relies on data if the underlying data is flawed.
This is where data fabric comes to the rescue. Using a data fabric to manage data collection, governance, integration, and sharing can assist organizations in meeting these challenges and becoming digital leaders.
A data fabric is not a one-time fix for particular data integration or management issue. Instead, it is a long-term and scalable solution for managing all of your data in a single location.
Finally, implementing data fabric trends can assist a company in meeting its data management challenges and becoming a digital leader, like the IBM data fabric.
- It provides a single environment to access and collect all data, regardless of where it is located or how it is stored.
- Eliminates multiple tools and provides quick access to healthier, more reliable data to enable simpler and unified data management, including data integration, quality, governance, and sharing.
- Provides increased scalability that can adapt to increasing data volumes, data sources, and application requirements.
- It makes it easier to leverage the cloud by supporting on-premises, hybrid, and multi-cloud fabric data center environments and allowing for faster migration between these environments.
- Lowering reliance on legacy infrastructure and solutions
- Future-proofing the data management infrastructure by incorporating new data sources, endpoints, and technologies into the data fabric without interfering with existing connections or deployments.
Because of the increasingly diverse, distributed, and complex business environment, having an agile approach to data management is a top priority for organizations today. Although some analysts believe that data fabrics requirements are the future of data management, this potential cannot be fully realized without the proper technological infrastructure, particularly integration technology.
With the modern integration of technology, enterprises can lay a solid technological foundation for a cohesive data fabric architecture that connects the entire organization, no matter how dispersed its business ecosystem is. Data fabric solutions enable flexible and cohesive data management, maximizing the value of your enterprise’s data and adding significant value and ROI around data fabric for partners, customers, and other stakeholders.
