Top 5 Benefits of Data Fabric

Today’s data challenges 

Data is an integral part of a company’s digital transformation. But when companies try to use the data, they run into problems. Various data sources, types, structures, environment and platform. The status of this multi-dimensional data gets even more complex as organizations adopt hybrid and multi-cloud architectures. Operational data is important to many companies today. Most remain isolated and hidden, resulting in a huge crowd of dark data.

What is Data Fabric?

Data assets are generated in silos and hidden in a hybrid mix of infrastructure environments. Data preparation cycles are long, and users need a wide range of data management capabilities to overcome the limitations faced by complex multi-vendor, multi-cloud, and evolving data environments. The data fabric architecture is designed to address the challenges faced by complex hybrid data landscapes.

Data fabric can basically be described as an integrated platform that supports diverse data management needs to provide the right IT service levels for all different data sources and infrastructure types. It acts as an integrated framework for managing, moving, and protecting data across multiple isolated, heterogeneous data center deployments. As a result, organizations can invest in infrastructure solutions that meet their business needs without worrying about data service levels, access, and security.

Wondering how Data Fabric can help your business? Here’s how:

Data with flexible data architecture

In most organizations, important data remains idle, unintegrated, and inaccessible to those who need it most. The data can be in different source systems and in different formats. There are many ways to solve this problem. A less clever way is to put all your data in one central location, like a data lake, and use it further. However, this requires a lot of data movement and cannot be done in a large organization. In addition to batch data, you can also use streaming data to make the topic more complex.

What is required is a flexible data architecture that not only allows you to process your data at the source, but also integrates your data across the cloud and in your on-premises environment. Flexible data architecture is one of the key characteristics of high-performance data structures. Data Fabric software enables a flexible data architecture and provides a powerful data structure. Use pushdown processing through an intelligent query structure to help you bring all your data together or use it where you are. It also helps you manage costs with inexpensive object storage and ingest data in real time.

Enables data democratization by automating data detection

A few years ago, the self-service BI movement brought analytics into the hands of business users. However, data preparation and curation were still the responsibility of IT. We are now in the era of data democratization. In this era, business users can directly access data, process, and consume it. Accelerating data democratization is also driven by the movement of data meshes to promote self-service data platforms. A key factor in democratizing data is the automation of the data detection process. Intelligent and automated data detection makes it easy for business users to find the data they are looking for.

Intelligent data exploration using industry data models and knowledge graphs

If you’re accessing the data and looking at its contents, you’re just warming up. You may also be overwhelmed by the sheer volume of data discovered. To start using your data productively, you need to look at it and turn it into useful business insights.

A powerful Data Fabric makes it easy to search for data intelligently. A smart way is to integrate your data with known industry best practices. By doing so, you can be confident that your data exploration follows a pattern that has been proven to provide business value, rather than a random combination of data sources. Data Fabric implementation companies provide a proven way to integrate the data needed to run your business. The industry data model provides a way to accelerate data integration through semantic mapping. The knowledge graph, on the other hand, provides a way to integrate data by analyzing data consumption patterns. The knowledge graph can show the relationship between a retail store and all the events where the store is located. This ability to connect data and enable intelligent data exploration is the value that comes from powerful data fabrics.

Faster value creation through data industrialization

Data Fabric does more than just detect and explore data. It also needs to have industrialization capabilities so that the data-to-value process can operate in a robust and scalable way. The way to do this is to industrialize the data. This helps streamline and automate the process of extracting data from the source system, organizing the data in a structured data environment, and making it available to business end users on the platform. This reduces the time it takes to assess the value of your data to business insights. Get in touch with companies that offer a variety of services, including Data-Ops, that enable the industrialization of data to make the most out of Data Fabric and its business benefits.

Accelerate AI initiatives with faster data preparation and large-scale AI operations

Recently, AI has become the most important strategic initiative by enterprises. Despite the large investment in AI, many companies have not yet fully realized the potential of AI. One of the biggest challenges they face is the data preparation and the large-scale operation of AI. Data Fabric also needs to address these challenges as AI is at the core of its digital transformation initiative. Data Fabric implementation successfully addresses these challenges with advanced analytics in a robust database that supports large-scale data preparation. Since these are functions in the database, all data preparation functions are performed directly in the database. This avoids unnecessary data movement and speeds up the data preparation required for AI. AI operationalization is another area where Data Fabric implementation excels. To commercialize AI, it is necessary to integrate the predicted values ​​of the AI ​​model and operational data. For example, if you have a model that predicts a manufacturing asset failure, running an AI model means finding where the asset is and the manufacturing components that could be affected by the predicted failure. The commercialization of the AI model is not limited to mathematical formulas. Rather, use AI in the context of operational data. Data Fabric should facilitate the integration of AI models and operational data.

Moving Forward

Data Fabric offers the industry’s best value with an adaptive cost-based optimizer, workload management with a flexible pricing model, and deployment options. With Data Fabric, you also have a high degree of security that has been proven to comply with data regulations. Digital transformation is the value provided by a powerful Data Fabric, from unlocking all data for business value, reducing time to value realization, and expanding capabilities in the cloud era. Data Fabric is the key to success.