Streaming Analytics for Manufacturers

DHG's Chief Data Officer Amit Arya discusses how streaming analytics can be a useful tool for manufacturers by utilizing real-time data for proactive insights and decision-making.

Transcript

Introduction

[00:00:09] JL: Welcome to today’s edition of DHG’s GrowthCast. I’m your host, John Locke, and at DHG, our strength lies in our technical knowledge, our industry intelligence and our future focus. We understand business needs and are laser-focused on company goals. In this ever-changing world, DHG’s Growthcast provides insights and thought-provoking conversations on topics and trends that address growth opportunities and challenges in the current and future marketplace.

Thanks for joining us as we discuss tomorrow’s need today.

[00:00:42] ANNOUNCER: Views and concepts expressed by today’s panelists are their own and not those of Dickson Hughes Goodman LLP. Always consult the advice of your legal and financial professional before taking any action.

Interview

[00:00:58] JL: Today, I will be talking with Amit Arya, Chief Data Officer of DHG. Amit oversees the firm's data and analytic strategy, and provides guidance on data centric decisions in how to enhance firmwide operations. Thanks for joining me today, Amit.

[00:01:15] AA: Happy to be here, John.

[00:01:16] JL: So for starters, Amit. What is streaming analytics?

[00:01:22] AA: Well John, in a gist, streaming analytics refers to civil machines and processes, talking to each other through connected senses and allowing a firm to minutely monitor every aspect of their operation that they’re able to put the sensor zone. Now, this mechanism can provide companies with real-time data from a number of different sources. And what this means for the companies is that they can have insights, in fact real-time insights into their operations, maintenance and security all simultaneously. So having access to real-time data can really translate into improved overall operating efficiency and helping the firm make important decisions with more insight and the speed if you will.

[00:02:19] JL: Well Amit, we hear a lot about cloud-based programs, and it seems like the more content being utilized, the slower the performances is. Is that a concern for streaming analytics?

[00:02:32] AA: Well John, there was a day and time when speed was a concern when examining, and storing and creating massive amount of data. But that is not the case these days. They have really come a very long way from a traditional data marts and warehouses. When we store large volume of data and storing massive amount of data to the deep tradition and data access and querying time.

Today, we have the ability to ingest and analyze massive amounts of data, what we call big data, which is categorized by three Vs, the volume, the velocity and the variety of data that you can capture. To be specific about streaming analytics, this analytical engine is optimized to perform real-time analysis of extremely large data, which can be scaled for any size of company. So this platform can adapt and handle extremely large volumes of data without compromising performance.

[00:03:39] JJ: What is the benefit of using streaming analytics over traditional analyzing processes, Amit?

[00:03:48] AA: Well John, the basic difference between streaming analytics and what we might call traditional analytics is, where exactly is the data being analyzed? In a traditional analytical architecture, data is analyzed at the end of an event, which is being started. We usually record the data, store it and then analyze it. The primary difference in streaming analytics platform is that the analysis occurs as the event is occurring. So in essence, the analysis of course in real time. This gives a firm a real-time picture of what is occurring in the here and the now, versus coming back to the data or the event a day, a week or a month later, which is usually the case in most analytical processes.

This way, we’re able to assess information. Over a period of time with a constant flow of information and we are able to give continuous insights. This real-time flow of information allows decision-makers to be more proactive rather than have a reactive status to decision making.

[00:05:02] JJ: Well, being proactive versus reactive sounds like a good thing, but how specifically does having a real-time flow of information truly benefit the company?

[00:05:13] AA: Well, the use cases are immense within the industry. Let’s take manufacturers for instance. Streaming analytics has the capability to automate and potentially risk operating cost by installing adequate amount of sensors in the manufacturing process, where you have dependency on one process in your manufacturing chain. A breakdown in one process can be immediately identified and remediation measures are taken place as opposed to valuable timing spent on understanding where the downtime might be coming from. This also reduces defects in manufacturing, increases uptime. In other words, having access to up-to-date data can really help make company's decision in real time things like utility costs, imagery on hand.  So there's a wide area of use cases.

Initially, manufacturers can use analytics to identify issues in the supply chain outside of their four walls of production. Ensuring the production lines are producing quality products is essential to the success of a business, so maintaining their efficiency should be the top priority for all manufacturing companies.

[00:06:39] JJ: Great. Well, one final question, Amit. With more people working from home than ever before, cyber security must be a big concern for a lot of companies. Would analytics be able to help mitigate some of those concerns?

[00:06:54] AA: Absolutely, John. In fact, we have been living in a virtual world for the last six months due to COVID-19. One of the great benefits of streaming analytics is that, it can absolutely identify potential threats again in real time or even suspicious behavior as it is happening based on the set of predetermined criteria. Cyber attackers can be stopped before they even become a threat, leveraging this real-time analytics technology. This is another great use case of how streaming analytics platforms can be leveraged within the industry to make businesses safer, more resilient and providing the decision-makers real-time information for the decision making.

[00:07:43] JJ: Great. Good information and Amit, thank you so much for helping us have a better handle on the impact of streaming analytics today. I appreciate your time.

[00:07:54] AA: Glad to be here, John. Thanks.

End of Interview

[00:07:57] JL: Thank you for joining us today on DHG GrowthCast. You’ve been listening to DHG’s Amit Arya, Chief Data Officer. We hope that you now have a better understanding of the differences between traditional and streaming analytics.

I'm your host, John Locke, and I look forward to reconnecting with you again soon on another episode of DHG GrowthCast.

About DHG's GrowthCast

At DHG, our strength lies in our technical knowledge, our industry intelligence and our future focus. We understand business needs and are laser focused on company goals. In this ever-changing world, DHG’s Growthcast, provides insights and thought -provoking conversations on topics and trends that address growth opportunities and challenges in the current and future marketplace. Join us in discussing tomorrow’s needs today.

Disclaimer: The views and concepts expressed by today’s guests are their own and not those of Dixon Hughes Goodman LLP. Always consult with your legal and financial professional before taking any action.

CONTRIBUTORS

Amit Arya
Chief Data Officer
amit.arya@dhg.com

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