How AI/ML will shape our data management and analytics support processes

Updated: Apr 28

Written by Ramki Ranganathan, VP of Data & Analytics (Americas) at Systech


In the last few decades, we have made tremendous progress in the field of Information Technology, with the pace of progress accelerating rapidly. Many fear that these

advancements in the technological space will inevitably render human beings obsolete in the work force, but I am willing to argue that these advancements will ultimately help the human race move to the next stage of evolution. The application of AI powered bots in the managed services field, for example, will be able to eliminate the manual and mundane efforts on the support processes front, altogether.


One day soon, I believe that autonomous technology will not only be a norm, but also a major buffer in shifting our culture for the better.


In the field of data analytics, the successful application of autonomous bots offers limitless potential. An enterprises’ data management and analytics environment will involve data pipeline processes (real-time and batch) and dashboards, visualization, reports, refresh and subscription schedules. The managed services team’s responsibility is to monitor these processes. There are many levels of support teams and SLA’s defined for these processes. For example, a retail chain might have a batch (daily) process that updates the data warehouse with data from POS systems across their stores. As soon as the day comes to a close, the POS from these stores may be sent over to corporate, where the data will be loaded into the data warehouse and transposed into insights, reports and dashboards that help the organization process and understand the success and problem areas within their business.


The managed services team that supports this process typically has three to four layers of support. The objective of the first level of support is to monitor these processes and make sure that nothing fails. In the event of an abnormality (failure or slowing down of processes), they have to notify the second level personnel to troubleshoot why the error occurred and put a resolution ASAP. Sometimes the third level support team needs to be involved and typically these are product or platform specialists to resolve the issue.

If COVID-19 has done anything, it has reinforced the value of maximizing profit while cutting costs. I’m of the belief that AI and ML technology hold the key not only to the future of data analytics, but also to the support processes, as an essential component of this ecosystem.


Through the use of AI/ML, it may one day be possible to eliminate the first layer of support, altogether. Visual recognition software and body automation could quite literally identify failing processes, or even send alerts should something seem incongruous with typical findings. The AI / ML solution will also categorize the error and even suggest possible resolutions to the second level support team by making the solution learn from historical patterns. If we are able to unlock the true potential of AI/ML, we could arguably cut second tier support by 50% and eliminate the first layer of support, entirely. Businesses could focus on advancing their market footprint, instead of how to cut costs and maintain a death grip on the revenue they accrue.


RPA (Robotic Process Automation) bots could also be employed to adjust downstream processes if a delay is predicted on any particular process. The data pipeline and analytics schedule would be made as fluid and flexible as possible, allowing the bots to mimic a human person who would ordinarily be able dynamically monitor them and make decisions on the fly.


The future of AI/ML technology has the ability to shift our culture for the better. For better, the freed-up resources will encourage businesses to become more ambitious and fearless. Thinking philosophically, I have to ponder the societal implication of these advancements. Maybe we will find the J.A.R.V.I.S. to our Iron Man, or the R2-D2 to our Anakin Skywalker. The possibilities are limitless. For now, we only at the precipice of a true AI takeover.


On the same note, while the use of AI/ML is on a more regulated scale, it feels appropriate to question whether or not advancements in AI/ML will at some point jeopardize the intrinsic value of mankind.


If we build robots to think, feel and dream like us, what separates us from them?

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