What is A Data Fabric and What Is It’s Business Value

What is the business value of a data fabric solution? There's a new buzzword in the world of data analytics, and that is – data fabric. The word, which denotes a well-defined environment comprising the technologies involved in data analytics, has caught on this year.

What is Data Fabric?

A data fabric is a setup that enables the organization to use better the data it has. A data fabric facilitates self-service data consumption, embeds governance, and automates data integration. This way, the organization can optimize the data for faster insights. It also helps reduce data inconsistency and compliance risk and improves data quality.

Data Fabric can also help businesses:

• Reduce costs by utilizing the best and most accurate source of data
• Reduce risk by automating data quality processes
• Accelerate insight delivery with a single view of all relevant information across your enterprise

Still managing fragmented data? See how a data fabric approach helps teams work from a single, trusted view of information

The term data fabric refers to the "physical topology of servers and other hardware components used to store and process data." The idea is that you should be able to move data between components such as physical servers, virtual machines, containers, and storage accounts. This idea has been around for a while, and it's still considered cutting-edge.

Data fabric provides a way for various types of data (e.g., schema, context, history) to be passed among applications that use them. A single instance of any data type can exist in multiple places, but it is essential that each instance points to the same physical resources and that the data types are consistent.

Data can be moved from one component to another as needed. 

Data fabric is also used to reduce management overhead and provide a single point of control for managing resources and settings across multiple physical and virtual resources.

According to one article, Data Fabric provides a catalog of consistent data services across private and public clouds. This explanation says enterprises have grappled with integrating their entire data sets into a single platform. A data fabric is a comprehensive approach to achieving that goal.

Try to imagine a large piece of hypothetical fabric stretched over a theoretical space that joins multiple data points across locations, including the cloud, structured and unstructured data, and methods for accessing and analyzing it. Unlike a piece of cloth, a data fabric does not have a fixed shape, is scalable, and has built-in fluidity that accounts for data processing, management, and storage. It can be accessed or shared by internal and external teams for a wide variety of enterprise analytical and operational use cases.

Explore how Express Analytics helps organizations build scalable, business-ready data fabrics >>>>> Schedule a call

What Data Fabric is not:

1. A fancy word that describes the same old processes or solutions
2. A one-time resolution of a data problem

We must understand that as data analytics gets cost-effective and more democratic, and as the world marches towards becoming a data-centric one, the entire operation needs to get more cohesive and easier to use.

That's where the scalable data fabric comes in, as it not only helps manage the collection, governance, integration, and sharing of data but also solves challenges along the way. And, it prevents data from accumulating in silos.  

Why is Data Fabric Required?

The goals are simple – provide a single environment for accessing data and enable simpler, unified data management. But the overarching aim is to maximize the value of your data. 

Let's first examine the hurdles before an enterprise that is on its way to digital transformation:

1. Data in multiple on-premise and off-premise locations
2. Data cleansing issues
3. Structured and unstructured data
4. Data in a variety of formats
5. Use of different technologies and various data integration tools
6. Lack of scalability  

All of the above uses about 70-80% of a data professional's time for doing non-essential tasks. Clearly, it is in an enterprise's interest to strive for a single environment for accessing, collecting, and analyzing all data.

That's what a data fabric does – making the enterprise extremely agile.

How does Data Architecture Work with Data Fabric?

With a data fabric, you can map data from disparate apps (including data stored in different places) so that they are ready for business analysis.

Insights and decisions can be drawn from existing and new data points with connected data. 

Unlike static reports or dashboards, this is a highly dynamic experience.

Data architecture is an essential consideration in designing your data fabric. Data architecture is at the top of the data life cycle and encompasses many architectural considerations.

A data architecture architect can guide the design of your data fabric and help determine which system to use.

A data architecture describes how you model your data. You can map each data object as a member of a table, as a property of a database, or as an element of a service.

The goal is to optimize your data's structure across its entire lifecycle, including growth, security, performance, reliability, and functionality. 

Typically, an architect starts with a business data model that describes how all relevant business entities are modeled as data tables.

Using this approach, you can then manage your data independently of the systems that contain it. This is the foundation of the Data Fabric design model. 

The basic structure of a data fabric is a set of discrete data centers, often using NSX or SDN technologies, that communicate with one another via secure virtual links.

Each data center is the equivalent of a traditional server and represents a point of entry for data. Each data center can hold several data volumes and supports both local and distributed database workloads.

A data fabric solves several problems, like:

1. Siloed data
2. Lack of reliability in data storage and security
3. Poor scalability
4. Reliance on underperforming legacy systems

How does a Data Fabric Help?

Here are the many ways:

1. It helps with data inputs and integration abilities between data sources and apps
2. Helps with bolstering data quality, data preparation, and data governance capabilities
3. It helps connect with any data source via pre-packaged modules and does not require any coding
4. It helps handle multiple environments, such as cloud, on-premise, and hybrid

What Business Value can be Derived from Data Fabric?

Data fabric is essential to modern IT infrastructure. It can be used for two types of business value: data governance and application integration. Both are independent but work together.

The data fabric lays the foundation for modern applications by architecting the infrastructure needed to manage information assets throughout their lifecycle.

Data governance refers to the ability to track and govern data across environments, applications, and users.

For example, when objects are moved from one environment to another, the data fabric will notify all components that the object has been moved and handle processing accordingly (which processes to run, how they should be run, and the object's new state).

Dynamic data fabrics are built on contextual information. Data fabric should be able to identify, connect, and analyze technical, business, operational, and social metadata (in the form of a well-connected pool of metadata).

The data fabric will provide actionable insights to enable real-time decision-making for business users.

The fabrics are getting smarter with each passing day, redefining the essence of dynamic architecture to meet the needs of the evolving digital world.

Data governance is paramount to the evolution of the data fabric and to its ability to improve and deliver real-time insights continuously.

Data governance, architecture design, development, and integration will provide more strategic and tangible business benefits and make organizations more flexible and agile.

Enterprises must activate metadata to ensure frictionless data sharing. To achieve this, the fabric should:

• Create a graph model by constantly analyzing metadata for key metrics and statistics
• Show the relationships between metadata in a graphical form in a way that is easy to understand

Furthermore, it can improve business-level metadata governance, increase collaboration, and allow companies to speed time-to-value by facilitating 360-degree customer views, fraud detection, and deleting data in silos, among other features. What's more, by leveraging metadata metrics, AI/ML algorithms can learn over time and provide more advanced predictions about data integration and management.

See how a data fabric fits into your existing analytics and data architecture without disruption >>>>> Schedule a call

Conclusion

A single instance of any data type can exist in multiple places, but it is vital that each instance points to the same resources and that the data types are consistent.

Data can be moved from one component to another as needed. A data fabric reduces the amount of data management required and provides a single point of control for managing resources and settings across multiple physical and virtual resources.