How Does Fabric Solve the Challenges of Data Silos?

Realizing the need for digital transformation is just not enough. It’s time to move on; time to embrace change and optimize digital structure at all layers of the enterprise landscape. Of late, businesses have adopted data fabrics in their management practices and created new data layers for workloads. At this rate, the market value could touch USD 4546.9 Mn by 2026, thereby making the technology accessible to everyone. 

As we all know, fabrics address many challenges. They eliminate the manual dependencies and empower data scientists to focus on other core tasks. One of the major issues solved by fabric is the uncertain volume of incoming data from multiple sources in silos. 

Breaking Down Silos Can Offer Multiple Ranges of Benefits for Businesses 

Let’s take a reference to K2View. Their data fabric tool aligns fragmented data sets from multiple systems into relevant business entities such as the customer name/ID, product, location, order, etc. Each of these entities is stored in an exclusive micro-database. The database stores all the data related to a particular business entity, such as transactions and all sorts of master data. 

Since the fabric collects, transforms, and integrates the data into numerous micro-databases in real-time, it directly simplifies the issues arising out of source systems in silos. What does it achieve? The fabric records 60% less DBMS computing power. Not to mention, there’s a 70% cost reduction in progression and regression testing. 

Likewise, there are many data fabric vendors such as IBM, NetApp, Atlan, etc. Each of these could differ in their configuration and extent but share similar objectives: to align volumes of data from multiple sources and address challenges as explained below.

Availability

By using a well-structured data fabric, users can access information regardless of the data source, storage location, or the point of user access. Remember that siloed data is usually restricted by availability. Contrary to this, accessible data can be accessed without any structural restrictions.

Scalability

The data that is standardized automatically when received can be processed at high volumes in comparison to data that is manually formatted, integrated, and standardized. The golden rule of thumb is: the greater the volume of applied data, the better is the business insights derived from it.

Readability

Using a data fabric allows you to improve data reliability in 2 ways. Firstly, data is made available in a reliable way to multiple users regardless of their location. However, in a second way, the data is collected at a single source. This eliminates any confusion arising because of storing data at multiple locations. Ultimately, by using a data fabric you can make business decisions faster by viewing data from different sources on a single platform.

Usability

Another great reason for using a data fabric is that it allows you to improve the usability of different types of data. This is done by using a multi-faceted and layered data analytic program. This way you get all the valuable insights well in time to stay ahead of your competitors and other industry leaders in the market. The aim should be to look for a comprehensive analytics program that includes different data types. This provides deeper insights into your business environment.

Creating Multi-Region Cloud Networks

A conventional multi-regional network usually features a hub in a minimum of at least 2 regions, and these are connected to a public cloud service provider through the fabric. Both of these regions also share the same carrier network.

Enterprises can build multi-region cloud gateways by establishing consistent connectivity between regional hubs and the fabric port. Furthermore, they can use the VPC peering to ensure interconnectivity between multiple fabric ports. Such cloud gateways not only respect the regional boundaries but also lower the network latency rate and extend flexibility to support future challenges in the cloud.

Interconnected Data Centers

As the digital landscape improves by leaps and bounds, corporations now look for network providers who can provide them with a cloud-like experience. This includes spinning up new services in no time rather than having to place an order and wait for it to be fulfilled instead.

Fabric architecture provides agility and flexibility to ensure connectivity with the cloud service provider in a metro where they aren’t located or having customers connect their assets in different locations. If you are looking for data center interconnections you can either opt for the EVPL (Ethernet virtual private line) or the latest EPL (Ethernet private line). Customers can now use EPL to back-up encryption via the MACsec (Media access control security). 

Accessibility

• Carry out all data access modes, support data types and sources, integrate transactional and master data, be it in motion or stationary.

• Absorb and collect from both cloud and in-situ systems, regardless of their format (structured or unstructured).

• A data fabric logical access layer allows for data consumption regardless of the location of the data storage and distribution. This means that no in-depth analysis of any underlying data sources is required. 

Distribution

A data fabric should be deployable in different environments such as on-premise, multi-cloud, or hybrid.

Maintain data governance capabilities along with transactional integrity, as the data fabric must always support a smart data virtualization strategy.

Fabric in Action 

So far, we discussed the various challenges in conventional data management systems. Most of them are related to complexities arising out of data silos and how fabric provides immediate relief. 

Given the ongoing restrictions, the demand for remote operations is inevitable, and this will put immense pressure on services providers to ensure in-the-moment data insights. I believe there couldn’t be a better time for enterprises to embrace the change and adopt advanced practices. 

What’s your story of fabric? Do share with me in the comments.