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A Methodical Approach to Measuring ROI on Incident Response Systems

Often MSMEs operating small-scale machine workshops struggle to map and prioritize their technology platform adoption needs, specifically, when they learn about broad concepts such as IT-OT integration, secure and seamless data extraction, and availability in cloud platforms for visualization and decision making. They get the concept but lack the analytical toolsets to quantify what it all means for them. 

The perceived benefits through data visualization ( the “dashboarding” or “digital/virtual cockpit views”) and corresponding task-driven process automation involving connectors and middleware that promises to connect assets, machines, tooling, and sensors, should be quantified considering the on-field realities to justify funding.

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Is It Worth It? ROI of Recommender Systems

Netflix recommender system

Recommender systems promise to reduce churn and increase sales. But how do you measure their actual success? What is it that you should measure? And what challenges should you look out for when you’re building your recommendation engine? In this article, I’ll discuss some challenges of recommendation engines, the ROI, and standard metrics to help evaluate their performance.

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Challenges of Recommender Systems

Most articles about recommendation engines focus on all the bright sides of recommendations: personalized customer experience, lower churn, increase in sales, and more revenue. While all of that is true, as we can see looking at the examples of numerous companies including Amazon, adopting a new technology requires a strategic approach — so you should be realistic and well-prepared and not only optimistic about the future outcomes. There are some challenges that you have to be aware of.

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Cloud-First is Often a Mistake. Here’s Why.

For some enterprises, a “cloud-first” policy can seem like a no-brainer, especially when compared to the quagmire of traditional data center infrastructure. Yet new software-defined infrastructure solutions like hyperconverged infrastructure (HCI) also offer IT agility, as well as greater security and control than what’s available in a public cloud. Perhaps surprisingly, many actually cite cost as the key incentive for using public cloud, despite the fact that, in most cases, it is significantly more expensive than on-premises HCI solutions like Enterprise Cloud.

IDC published a study that found predictable workloads, which account for the majority of all enterprise workloads, on average were about twice as expensive to run in the public cloud as compared to running on-premises on Nutanix. And a 2018 IDC survey entitled Cloud Repatriation Accelerates in a Multicloud World reported that 80 percent of organizations had repatriated applications out of the public cloud back to on-premises, and that 50 percent of all public cloud applications installed today will move back on-premises over the next two years.