The future of data analytics in the IoT world

Challenges in today’s connected world with Data

The world today is connected in many ways that are revolutionizing human lives and transforming technologies towards digital existence. The connected world today is generating millions of terabytes of data from all possible machines human race can depend on, such as airplanes, high-speed trains, cars, robotic arms, manufacturing plants, and many more. These Growing volumes of data are creating numerous challenges for businesses, increasing cost and time to handle them on centralized cloud or on-prem infrastructure.

Big companies and industries are investing billions of dollars to handle data at a faster pace. They aim to derive actionable insights in real-time to make their business decisions agile and data-driven. They want to obtain more significant time-to-value as its fairly told – “Data, which is a second old, is more valuable than data which is minute old.” Complex and heterogeneous data analytics tools have taken over the market. They are performing without harmony, thus making it more challenging for the industries to manage and monitor their data sacs. As the human race evolves, business needs for data security and privacy are under continuous scrutiny. The rules around sensitive data, PII, data residency have been continuously in the limelight, making industries vulnerable.

How can Edge Analytics rescue us?

Moving ahead, the De-centralized approach transforms the way the data is being handled by millions of devices around the world. Gartner defines edge analytics as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”

In simpler words, Edge Analytics brings data management and data storage closer to the devices where the data is generated rather than moving to centralized locations (cloud or on-prem), which are thousands of miles away. This approach will contribute to the performance of IoT applications such as smart factories, autonomous cars, and other IoT devices by analyzing data in real-time, with reduced latency.

Acts performed by Edge Analytics on Edge devices

• Data Filtering and Normalization – Remove dark and noisy data; standardize these data streams for further consumption.
• Data Enhancement – Catalog data streams by adding data tags, metadata and time stamps
• Data Analytics – Analyze data streams using Machine Learning (ML) models to detect anomalies and provide actionable insights and recommendation
• Data residency – Move data to the desired storage or publish the analyzed results to user dashboards or to a centralized location for advance analytics.

IoT technology will continue to get smarter and easier day by day. But the goal is to get value faster out of the generated data from these devices. Edge Analytics will provide a dramatic breakthrough in handling and managing these IoT applications.