This article is written by Mouli Srivasan, an IoT and Big Data expert.
Data is growing by every second and in total compliance to the big data’s 3V rule – volume, velocity, and value that the world has been witnessing in the past decade. Today, with various methods of data storage like private, public, hybrid and on-premise storage methods, collecting and storing data is no longer a challenging task. But with such massive amounts of data to handle, the ability of enterprises to harness, analyze, and take quick business decisions has become increasingly complex. To bridge this gap between the big data expertise to bigger data readiness, data fabrics are a clear winner.
Data fabric does the conversion of raw data sets to the most appropriate, actionable and worth investing data insights. Many companies have evolved from the traditional methods of data preparation techniques to providing insightful approaches.
One such approach is called the K2View approach. In this approach, a patented micro-DB methodology is used to store data through a digital entity wherein every entity represents a specific business partner. Every time the fabric captures data, the schema processes and distributes it into a micro-DB. While every micro-DB represents a specific digital entity, it is encrypted with a primary key thereby ensuring highly configurable data synchronization. With a focus on making applications smarter, be it domestic or industrial, the data fabric performs end-to-end automation of the data preparation pipeline.
Data Fabric for IIoT: Weaving The Right Architecture for the Industrial Floor
Data is the core of the evolution of predictive models. While capturing & storing more data is just one part of it, distilling and refining it into a valuable asset class is a real challenge. With data fabrics, this data is filtered at an early stage thereby making it easier to prepare the data. This means, collecting, integrating, analyzing, and archiving data are all performed, automatically. Not to miss, the process evolves gradually and as the models understand the raw data; their performance in automating the industrial equipment improves too. According to data fabric analysis, the fabric also helps in the transition from manual monitoring to the self-governed evaluation of detecting abnormalities.
Over a period, these models would mature into prescriptive entities that execute guidelines more accurately and have an impact on the physical world. Next comes the on-demand deployment of predictive models for a wide variety of industrial use cases. Hosted in the cloud, these models would be accessible from anywhere to the business requirements. Ultimately, these models will lay the foundation for enhanced automation wherein industrial processes learn and repair themselves.
Data Fabric for Edge: Optimize Communication With The Core
While we are discussing IoT, Edge deserves a mention too. After all, the technology’s disruptive demand cannot be met without fabrics. Now the edge is bound to grow because it is easier to build sustainable IoT in a location that is geographically closer to its end customers. This reflects the bottom line costs due to the lesser number of sensors and other essential devices. Moreover, it is easier to monitor the distributed computations across the edge clusters and the core.
One of the major issues with edge computing is also now resolved. For many years, edge computing did not get mainstream partially because of insufficiencies in real-time data preparation and partially because of the unforeseen environmental conditions that may vary from edge-to-edge. While fabrics have addressed the data preparation issues, improved quality of the hardware is doing the essential data processing. The high-quality hardware casing ensures uninterrupted operations in different conditions, no matter how extreme they are.
However, there are additional complexities involved in adopting edge.
The ability to stream data continuously between the core and the edge has now become a major concern. The edge-core communication is a universal business requirement and fabrics have a solution for this as well.
Consider the use case of a service that provides continuous & on-demand content to millions of users. Most common examples include video streaming platforms (Netflix etc.), social media or e-learning platforms. Now, to maximize uptime, Edge computing could help in eliminating latency by providing streaming nearest to the end consumers. However, without analytics, the very objective of automated digital services is incomplete. The problem with most Edge solutions is the inability to compute and stream analytics data (customer consumption, preferences, etc.) back to the core and ultimately the business CRM landscape.
Using a distributed data fabric the complexity can be reduced to revolutionary levels. This is a simple and secure approach to provide on-demand data from the edge to the system landscape and ultimately to the sales, marketing & support teams.
Conclusion
It is safe to say that fabrics and IoT‘s growth are interoperable with each other. To make smarter apps & processes, we need to send/receive filtered data at the moment across a network of devices. Automated data preparation pipelines are the potential solution to exchange high-quality data.
