The growing adoption of data-intensive technologies like Artificial Intelligence, cloud systems, edge computing, and IoT means enterprises are generating even more data than ever. While more data means better insights and improvement in decision-making, it also results in greater complexity of data management systems for enterprises that make use of them.

This has necessitated the unification of data environments to overcome the challenges that come with handling more data. Data fabric is one of the strategies data management teams are employing to address the challenges of data management.

It is an emerging technology that allows for the synchronization and sharing of data between disparate systems. Technology like this is crucial because it allows businesses to streamline and enhance their data processing and paves the way for a seamless data layer, beginning with the sourcing of data and continuing through analysis, insight generation, and implementation.

Data fabric has been identified as one of the top 10 data analytics trends to watch out for in 2023 to aid organizations in proper data and application management. This article will explain what data fabric is, the concept of the data fabric architecture, and the best practices for implementing data fabric for your enterprise.

What Is A Data Fabric?

The term “data fabric” refers to a unified framework for managing data. The concept of Data fabric was developed to simplify data management for enterprises. It is characterized by intelligent and automated systems that allow the end-to-end integration of different data pipelines and cloud data environments.

Data fabric makes it easier to access and share data in a distributed data environment. The goal is to solve complex problems created by advanced data use cases by eliminating data silos, improving data governance, strengthening data security, and making data more accessible to business users.

Why Use A Data Fabric?

Data fabric is a step up from traditional data integration, which is deficient in meeting the demands of modern data-centric businesses in terms of real-time access to data, self-servicing, universal data transformation, and automation. Data should never be locked in separate silos in different locations or behind protected firewalls where they’re difficult to access. Instead, business users need to be able to access data whenever they need it in a unified, efficient, and secure environment, which is what data fabric provides. The following are some of the reasons to use a data fabric:

Data Protection – Given the holistic nature of the ecosystem in which data operates, data fabric can enhance security, data governance, and regulatory compliance. The risk of sensitive data being exposed is reduced because the size of the threat vector is reduced when data is not dispersed across multiple systems.

Smart Integration – Through the use of advanced data integration technologies such as semantic knowledge graphs, metadata management, and machine learning, you can develop a data fabric that consolidates information from a wide variety of sources and endpoints. Not only does it help you group related datasets together, but it also simplifies the process of integrating new data into your unified data environment as they’re generated. This eliminates data silos. A unified system also makes data governance easier and improves overall efficiency in data management.

Business Process Efficiency – Using data fabrics shortens the time it takes to gain insights from data and make decisions. This helps to prevent micro-segmentation, customer churn reduction, alerts on operational hazards, and individualized customer service by providing a real-time, 360-degree view of your business that is accessible to all corporate entities.

Unite Databases Spread Over A Large Area – Using data fabrics, you can ensure that users are not hindered by physical proximity to servers. It standardizes data access APIs, making it easier to build applications. They can be used to ensure that one program gets the most out of its data without limiting the use of that data by another app.

What Is Data Fabric Architecture?

• Data Sources: these are the systems generating the information to be processed
• Analytics and knowledge graphs: These systems transform the integrated data sets into a coherent format.
• AI/ML algorithms: integrated artificial intelligence and machine learning algorithms allow you to monitor data continuously and generate insights in real time.
• APIs and SDKs: the data fabric infrastructure uses Application Programming Interfaces (APIs) and software development kits (SDKs) to integrate with front-end user interfaces in order to display data insights.
• A data consumption and data transportation layer: the data transport layer moves data across the fabric while data consumption layers like analytics tools, chatbots, and virtual assistants help to explore data.
• The hosting environment: this can be on-premise or on the cloud. Data fabric can also be hosted on multi-cloud and hybrid-cloud environments.

Given the complexity of data fabric systems, no existing standalone solution can facilitate the deployment of a complete data fabric architecture. Organizations often have to implement a combination of custom-built solutions and purchased turnkey solutions. For instance, you can sign up for a data management platform to handle about 70% of your data fabric architecture needs while you supplement the remaining 30% with a custom solution.

Best Practices For Implementing And Managing Data Fabric For Enterprises

As the data fabric market continues to grow and more companies implement a data fabric architecture for their organization, there’s a need to follow standard practices for the best results. A data fabric architecture that follows standard practices ensures that you’re optimizing how you utilize and manage your enterprise data resources. Some of the best practices to follow in implementing a data fabric architecture are highlighted below:

Use a DataOps Process Model

Data ops and data fabric are two completely different concepts. However, applying the basic principles of the DataOps process model can prove valuable for organizations looking to implement a data fabric. Fundamentally, the DataOps process model is concerned with how users leverage the tools available to them and apply the insights they gain from data to optimize operations. It is possible to apply this same model to the data fabric architecture. Adopting this mindset is the only way users can use their data fabric optimally.

Build A Data Fabric, Not A Data Lake

Many organizations trying to build a data fabric often end up with a centralized repository that only serves the purpose of storing data with no major functional benefits. This is just another data lake instead of a data fabric. It’s easy to get them mixed up. A data lake has many of the basic components of a data fabric architecture, such as data sources, analytics, data consumption, and data transport system. All it lacks is an integration layer that ensures interoperability between these different components. These are the Application Programming Interfaces and Software Development Kits that deliver insights to front-end user interfaces-making them truly useful for your organization. In building a data fabric, you need to prioritize these integration layers.

Understand Data Compliance Requirements

One of the top benefits of a data fabric architecture is that it helps to reduce the risk of data exposure at different touchpoints by unifying them into a single framework. However, since each of the disparate data systems often has different compliance and regulatory requirements, one has to be careful in implementing a single environment. A crucial step in planning your data fabric architecture is to automate the implementation of compliance policies that apply applicable laws as your data is transformed across different systems.

Use Graph-Based Analytics Instead Of Relational Databases

In implementing data fabrics, one way to connect and explain the relationship between different data points is by using relational databases, which provide additional information using text strings. However, a better approach to show the correlations between metadata and data is by using graph analytics. It helps organizations visualize data as interconnected nodes so they can quickly identify patterns, trends, and outliers that may not be immediately obvious with traditional analytics methods.

Use Open-Source Solutions

Enterprises can leverage open-source technology to create a data fabric that integrates and unifies data across multiple systems efficiently. One of the benefits of favoring open source over vendor software is that they’re easier to extend or integrate with other tools. Since building a data fabric often requires a robust infrastructure, you’ll do better with an affordable solution that also provides real-time data streaming and decentralized data storage and processing capabilities.

Data Fabric Use Cases

The most popular use case of data fabric is as a centralized business management tool. A data fabric architecture makes it possible to aggregate data assets scattered across various geographical locations in order to simplify access and analysis. This makes it particularly valuable for larger organizations with a wide regional and international spread. For such organizations that prioritize central management, the following are some of the possible practical use cases of a data fabric.

Improved Data Accessibility

Siloed operations make information sharing more challenging for businesses. In today’s digital age, companies are transitioning from this by leveraging data fabrics to revolutionize the fundamental way businesses work with data across their enterprises. Data fabric architecture securely makes data accessible to technical and non-technical users, regardless of location, device, or position within the organization. Data fabric in healthcare, for instance, delivers and permits access to a patient’s structured information to various healthcare departments to swiftly and effectively give recommendations for treatment programs and other data use cases. By reducing barriers to data access, the company can increase productivity and make decisions quickly and more efficiently.

Regulatory Compliance And Security

The management of consumer trust depends on a secure and legal data management system. Data fabric is a single framework that makes it simpler to uphold data quality standards and regulations like GDPR and HIPAA. This supports businesses in ensuring the security, dependability, and use of their data. By imposing role-based access controls and protecting sensitive data both in transit and at rest, data fabric solutions reduce the risks associated with data sharing by making it far more difficult for unauthorized parties to access the stored data.

User Satisfaction And Better Business Operations

Data fabrics integration stores information from customer engagements across several channels, including point-of-sale databases, third-party APIs, and CRM systems. Customers’ activities are collected and analyzed to show businesses how their customers interact with and use their services. Businesses can improve customer experience by using the aggregated data to create customized client personas that enable various teams within their organization to better understand their target audience and enhance customer relationships and service delivery. Similarly, having a unified profile means data collected from one department can be useful to the operation of other arms of your business. For instance, data collected by your marketing teams may be useful to customer service teams when resolving user issues and vice versa.

Preventive Maintenance Analysis

Data fabric technology offers cutting-edge analytics tools and algorithms for preventative maintenance analysis, assisting in the reduction of downtime by recognizing patterns, trigger actions, and trends in significant quantities of collected and unified data. Real-time monitoring and tool configuration is made available to businesses, helping them to foresee potential equipment breakdowns, plan maintenance ahead of time, and offer improved customer experiences. These use cases cover audit compliance, security traceability, and revenue-generating activities like cart abandonment action, ad optimization, and client retention.

Machine Learning And Artificial Intelligence

AI engineers and Machine Learning experts can benefit from using a robust data fabric with a broad view of business data. The large data sets integrated with a data fabric can be used to train machine learning models to boost their accuracy and usability. Leveraging data fabrics thus provides a new path to innovation for organizations serious about using machine learning models to their advantage.

Knowledge graphs, which are one of the most important components of a data fabric, help to draw connections between metadata stored in disparate data sources. This reduces the amount of time spent preparing data. This way, you can build reliable machine-learning models that are usable across different cloud platforms as quickly as possible.

Merger And Acquisitions

When two companies combine, either due to a merger or an acquisition on either end, there’s often a need to create a unified data model for the new company formed by the forged partnership. In cases like this, using a data fabric to connect the database and unify the data management policies of the two organizations is the best solution. It creates a unified view that makes it easier to unify the two previously independent organizations into a single entity without jeopardizing their operational efficiency.