Enterprise Architecture

  Filed under: Enterprise Architecture

  Filed under: Enterprise Architecture

Enterprises, in their quest to reduce labor costs are applying RPA technologies. Yet they do not have a well-defined set of principles and best practices including how to position RPA with other process tools and initatives. Today it may have become a bit more clear. Pega is the first tech provider, and only BPM market particpant of substance, to purchase an RPA provider (OpenSpan). The combination brings robotics, analytics, and case management together – and that makes sense. Think of Pega’s process/rules capibility firing off a set of RPA scripts.

RPA in many respects is an alternative, some would say the polar opposite of Pega’s current business model that feasts on the transformitive “big IT spend” for BPM, case management, automation, and customer service projects. RPA does not require invasive integration. It is a quick hit for automation, a “low touch” approach for process improvement for brittle legacy systems. The bottom line. Enterprises that employ labor on a large scale for process work, can gain efficiencies by just automating repetitive human tasks for the “as is” process.

OpenSpan is nice pick up for Pega that will help with back office BPM work, but more so with contact center environments where the agent requires human and machine multi-tasking that often spans multiple windows and web applications, few of which are integrated with each other. Cumbersome process flows, rekeying of data and lack of integration add up to lengthy call times, reduced accuracy and an overall increase in customer frustration. Pega/OpenSpan, will give Jacada, and NICE a run for thier money and the future integartion with Pega’s analytics tarcks where the RPA space is heading.

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Suppose you build a house. When it’s finished it’s shiny, new, and clean. Then you live in it for decades. Stacks of old newspapers litter the floors. Sometimes a bunch of these get thrown out. You only do superficial cleaning, … Continue reading →

Day one of the GPU Technology Conference in San Jose and I’m still glowing from watching Steve Wozniak “travel to Mars” through NVIDIA’s photo real virtual reality. Or, holding my stomach as Jen Hsun Huang, CEO of NVIDIA took us soaring over Everest. Or cringing, as I watch the early attempts at a car teaching itself to drive and being reminded of how my 16 year old daughter is learning to drive (there were a few similarities…). Each emotion illustrates what everyone will experience shortly on NVIDIA’s next gen compute platform with announcement for AI, VR, self-driving, SDK and new deep learning appliance.

This is not your traditional or even big data analytic platform. It’s a complete overhaul of the computing architecture. It’s a complete rethink of data management. It will also change how you think about analytics.

Stepping back from what may seem like hype and examples steeped in robotics, VR and infrastructure, the truth is, the announcements today show that deep learning in action is at most a year away, and as soon as now. In addition, the innovation coming out of robotics, VR and infrastructure will allow introduction of new form factors and channels to engage with customers and shape our workforce. In the end, it is a data challenge for the very reason that for every channel we use and add, it always ends up being a data challenge.

The implications for how you manage data are radical. Here is what you need to think about:

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Day one of the GPU Technology Conference in San Jose and I’m still glowing from watching Steve Wozniak “travel to Mars” through NVIDIA’s photo real virtual reality. Or, holding my stomach as Jen Hsun Huang, CEO of NVIDIA took us soaring over Everest. Or cringing, as I watch the early attempts at a car teaching itself to drive and being reminded of how my 16 year old daughter is learning to drive (there were a few similarities…). Each emotion illustrates what everyone will experience shortly on NVIDIA’s next gen compute platform with announcement for AI, VR, self-driving, SDK and new deep learning appliance.

This is not your traditional or even big data analytic platform. It’s a complete overhaul of the computing architecture. It’s a complete rethink of data management. It will also change how you think about analytics.

Stepping back from what may seem like hype and examples steeped in robotics, VR and infrastructure, the truth is, the announcements today show that deep learning in action is at most a year away, and as soon as now. In addition, the innovation coming out of robotics, VR and infrastructure will allow introduction of new form factors and channels to engage with customers and shape our workforce. In the end, it is a data challenge for the very reason that for every channel we use and add, it always ends up being a data challenge.

The implications for how you manage data are radical. Here is what you need to think about:

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Just a short mention today: R&A, Tetradian and Archi are proud to announce a new initiative: UltiMateCloud™, an initiative that takes Big Data and Cloud to its limit — and beyond. Have a look at the website.Filed under: Enterprise Architecture