For a service-centric world, ServiceNow launches Intelligent Automation Engine
Enterprise service management company ServiceNow has used its annual ‘Knowledge’ conference to launch a new product it calls Intelligent Automation Engine. This cloud-delivered software is positioned as a tool to bring machine learning to many of the devices and IT architectures that populate the IoT – as well as the services that feed to and from them.
Incidents, outages & benchmarks
Focused on analyzing business process operational data, ServiceNow’s latest product release is intended to create a consumable data layer that defines, describes and details the state of connected business entities, equipment and electronic services.
With every aspect of digitally run business (from simple documents to video capture to networked telephone systems and even air conditioning) now potentially definable as a ‘service’, the need to bring monitoring control to those services is clearly very real.
As explained here, a service is a way of defining a particular portion of software code (and wider application architecture) devoted to the execution of functions of one kind or another. Every service has its own data, performance, uptime, memory and processing requirements, and all the other elements of compute power we associate with cloud (such as Input/Output, networking connectivity and so on). It thus becomes a manageable data ‘thing’ inside the IoT.
Post-apocalyptic cloud realities
Executives from ServiceNow argue that software tools, processes and work patterns of the past are hampering our move to the new post-cloud era. Now, inside the ‘as-a-service’ cloud revolution, they further assert that the volume of back and forth work across every department, for common tasks like resetting of passwords or onboarding new employees, is straining the system.
So what is Intelligent Automation Engine? Essentially, this is machine learning intelligence applied to four of the biggest use cases that IT has today. They are:
- Preventing outages;
- Automatically categorizing & routing work;
- Predicting future performance;
- Benchmarking performance against peer systems.
“Intelligent automation heralds a new era in workplace productivity,” said Dave Wright, chief strategy officer, ServiceNow. “With this [product] we have embedded intelligence across our own software platform, trained with each customer’s own data, so that ServiceNow is enabling customers to achieve a quantum leap in the speed and economics of their business.”
- Anomaly detection to prevent outages —ServiceNow has bolstered its ability to help predict and prevent service outages with anomaly detection. An algorithm identifies patterns and outlying occurrences that are likely to lead to an outage. Combined with new dynamic threshold measures, the system learns the normal range of behavior and then flags outliers that might indicate future errors or malfunctions. Initially delivered in Operational Intelligence for IT, the anomaly detection capabilities can correlate past events that led to outages and initiate workflows to pre-empt future problems when the same preceding events are observed again.
- Intelligence to categorize and route work – ServiceNow will make available machine-learning algorithms to each customer’s unique data set. By learning from past patterns, the Intelligent Automation Engine can predict future outcomes, including determining risks, assigning owners, and categorizing work. Learned models set the category of the IT request and assign the task to the right team, as well as calculate associated risk of action or inaction.
- Performance predictions to drive improvements — The Intelligent Automation Engine powers new algorithms in its real-time Performance Analytics application, in order to help determine when IT performance goals are met. System managers set a performance objective and based on the data profile, Performance Analytics uses the optimal algorithm to predict when they will reach the objective.
- Benchmarks to evaluate performance against peers – ServiceNow Benchmarks enables users to compare their service efficiency to peers; for example, similarly sized organizations or those working in the same industry. In this way, companies not only know how they are performing against their own goals, but also how their performance compares against similar organizations – and, in many cases, competitors.
Cloud composability and micro-compartmentalization
The Intelligent Automation Engine is part of the ServiceNow trademarked Now Platform. As the notion of cloud computing and the wider structure of services-based delivery impacts more of our daily lives (from Uber and onward to other online ‘shared’ services), consumers and business are starting to realize the number of products and services that they can buy in smaller, service-based chunks.
Some call this ‘cloud composability’ as we can now ‘compose’ our lives, based on a more micro-compartmentalized approach to consumption. Some just call this thing ‘services’. Others just accept that this is the way things are now as the ubiquity of this model takes hold. Whatever you call it, someone has to automate and manage it at the back end.
Disclosure: Adrian Bridgwater has previously written a limited number of blogs on ServiceNow’s own website under his own name.
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