I recently caught up with Ashok Reddy, CEO of KX to discuss how his company’s work in the area of real-time analytics, more specifically, using AI and machine learning to provide insights for making better decisions quickly is one of the most exciting spaces in technology today. Increasingly, machine data from sensors and IoT, and AI and ML are influencing this strategic shift we’re seeing toward real-time forecasts and recommendations.
insideBIGDATA: Would you agree that real-time analytics is one of the most exciting spaces in technology today? Why?
Ashok Reddy: Of course. Real-time analytics and, more specifically, using AI and machine learning to provide insights for making better decisions quickly is one of the most exciting spaces in technology today. Increasingly, machine data from sensors and IoT, and AI and ML are influencing this strategic shift we’re seeing toward real-time forecasts and recommendations. Anomaly and pattern detection are also driving the market growth for real-time analytics.
Creating insights from data has been always important; however, for many companies, it’s costly and time-consuming. Most companies spend a great deal of time and effort collecting data, transforming and cleaning it, and then bringing data to analytics tools to make sense of the data. Transferring data from a company’s data silo to its analytics group creates latency. Customers are also starting to realize that insights need to be continuous. A single set of insights has a shelf life and is perishable in the context of many use cases in major industries.
At KX, we help users with decisions and actions that need to happen in micro-seconds. This has become possible with the change in paradigm. Instead of bringing data to analytics, we are bringing code (logic) and analytics (AI/ML) to data, in memory, for the fastest performance for generating real-time insights. It is especially important for analytics to be performed by joining different type of data (time series, relational, historical), without any latency. Then the real magic happens for businesses where they can make better decisions faster and focus on what matters most.
insideBIGDATA: We’re seeing industries outside your core financial services base taking advantage of streaming analytics. Are you seeing the same, looking at vertical industries like manufacturing, automotive and telecom?
Ashok Reddy: Yes, we are seeing significant growth in the adoption of real-time streaming in industries where large volumes of machine data is being generated at high speeds to be used with real-time applications such as fraud and fault detection, predictive asset management, risk management, network management and optimization, global location intelligence, supply chain management and customer engagement.
Key sectors where this is growing include telco (5G networks), healthcare (clinical trials, medical device manufacturing, patient health monitoring), semiconductor fabs, energy and utilities, government (defense and aerospace) and transportation and logistics, in addition to the financial space.
The largest datasets in the telecom arena are being generated by the telcos from the mobile and fixed line telemetry data from their networks. 5G brings a new Open RAN virtualized setup, resulting in an explosion of more data due to the nature of the protocol, more flexibility on getting data out closer to real-time and more analytics happening closer to the edge. This moves those datasets into the petabytes per hour range, and these can and should be acted upon instantly to gain maximum efficiencies.
In energy and utilities, the increasing demand for real-time smart grid visibility and load forecasting by the industry is driving the adoption of streaming analytics in the market.
insideBIGDATA: Do you see an expanded interest for these technologies in the North American market?
Ashok Reddy: North America is the largest market for these technologies due to the emergence of edge/IoT computing, the increasing amount of data across industry verticals (for example, post-COVID, large healthcare data sets have become available) and increasing spends by enterprises in real-time analytics.
insideBIGDATA: Do you see Azure bringing the development of further finance applications?
Ashok Reddy: Yes, as finance is one of the most demanding data environments in the world. In fact, as part of the strategic agreement announced earlier this year, KX and Microsoft will jointly develop applications and services for the financial services sector, utilizing the KX Insights platform for delivery. This will support existing and potential financial services customers with their cloud migration strategies.
insideBIGDATA: You released a report that found that 80% of companies surveyed have seen their revenues increase after implementing real-time analytics. Can you elaborate?
Ashok Reddy: Our recent research on real-time data in conjunction with the Centre for Economics and Business Research showed significant revenue improvements from real-time analytics. Across the different geographies and sectors we surveyed, 80% of companies reported a revenue uplift. And the benefits didn’t necessarily stop there. The findings also showed increased positive customer sentiment (98%), more efficient rollout processes (62%) and better detection of nefarious activity (100%). With the right analytics engine, it’s clear that businesses globally can realize significant value from real time data.
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