The service desk acts as the go-to place for all IT-related needs and issues, typically managing incidents or service disruptions, requests, and changes. The service desk scope of work can be enormous and wide-ranging, depending on the nature and size of the organisation in question. As a critical function used by employees across a company, it needs to be managed appropriately.

Technology has upended the way business is done across all industries around the world. At the same time, traditional IT service management (ITSM) solutions have become inefficient in maintaining customer satisfaction levels and meeting increasing customer expectations in a fast-paced digital world.

According to the SolarWinds IT Trends Report 2019: Skills for Tech Pros of Tomorrow, 79 per cent of IT managers werent able to spend sufficient time on value-added business activities or initiatives due to interruptions with day-to-day support-related issues. This resulted in misleading or incorrect manual entries into a problem log, which caused misinformed decision-making. With managers inundated with work, its easy for them to accidentally become the victim of manual or human errors.

With IT environments changing at an accelerating rate, its crucial IT service desks adopt emerging technologies. An explosion of data in recent years has intensified the pressure for IT professionals, but automated processes and machine learning (ML) can alleviate this pressure significantly. Artificial Intelligence (AI) and ML arent just buzzwords anymore. Enterprises worldwide are incorporating these technologies to enhance and improve operational efficiencies.

Whether for their use in predictive analytics, providing business intelligence, performance monitoring of networks, applications and systems, or even for its importance in self-driving cars, AI and ML are transforming the IT space. So, what are the applications of ML when it comes to ITSM? As an essential driver of how a business operates, a service desk solution can employ ML to streamline processes, and reduce manual, time-intensive tasks, which will ultimately free up time for additional projects and training to deliver business-wide transformation.

Incident resolution time has the potential to be cut in half. ML will enable self-resolution of incidents without the involvement of technicians and users will be able to search for solutions by themselves. Chatbots (like Google Assistant, for example) will be able to give information to end users without them having to log a ticket by providing easy access to relevant knowledge base articles based on their queries. Through ML, help desks could learn from past incidents and data to route tickets to the appropriate technician or support group. This can considerably increase efficiencies. Even better, automated help desks can run 24/7, making services available to employees at all hours at their own convenience.

Old IT assets can cause performance degradation for employees who rely on technology assets to do their jobs. In turn, this can result in a sizeable number of incidents in an organisation. Businesses spend a lot of money on hardware and software because of asset management solutions with poor transparency. This can be turned around using asset management solutions with ML technology to help track their performance based on insights from performance levels or incidents associated with a given asset. If incidents about a specific technology asset come into the system frequently or en masse, ML can recognise these as being associated and therefore indicative of a broader problem to be addressed.

ML can consume large datasets of past performance data to enable an analysis of incidents to predict future problems. Predictive capabilities can help save time, money, and effort for the entire organisation as steps can be taken before the severity or impact of the incident increases.

When end users submit a ticket, automation rules rely heavily on data like categories and subcategories to ensure accurate routing. ML helps facilitate this process by providing end users with suggestions for the most relevant categories and subcategories for a given ticket.

Service desk reporting can show trends about seasonality. Predictive models, however, take into consideration rate of change, frequency of problems, and other key factors helping predict service degradation and likely resulting in increased incident flows. This can help determine when more coverage is needed to maintain service levels.

ML, while being versatile as-is, demonstrates some critical applications when it comes to ITSM. Increasingly, organisations are taking leaps and bounds in their digital journeys, and it is only right their IT services evolve with them.

Now is a critical time for the Information Technology service management industry. The market is growing at a double-digit figure each year and is forecasted by analyst house IDC to reach over $8.5 billion by 2023.

Today, organisations need to re-examine how they can use new IT management software incorporating machine learning capabilities. Only this can change the course of IT service management which has historically been a cumbersome function of every business IT department.

Just as with huge transformative initiatives, software and machine learning can help streamline processes and increase employee productivity to drive better business outcomes. Service desk software will let IT pros consolidate asset information from multiple sources and provide real-time asset intelligence, thus improving service delivery while enhancing flexibility for collecting and managing data. By removing the manual burden of tasks like ticketing and tracking of assets and their performance, this will enable IT professionals to focus on critical projects and business transformation.

Steve Stover, Vice President of Product and Strategy, SolarWinds

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The role of machine learning in IT service management - ITProPortal

Because people are the most important resources in any organization, human capital management (HCM) is essential in every large enterprise. Changes in technology -- from mobile devices to AI -- are having a profound impact on how people in business work and interact.

Also:More on machine learning

To glimpse the future of HCM technology and the role of machine learning, I spoke with Cristina Goldt, vice president for HCM product management and Strategy at Workday. Cristina is a prominent HCM technology leader who is helping to shape human capital management. Our conversation took place at Workday Rising 2019, the company's annual customer event, held this year in Orlando.

Watch the video embedded above to see the future of HCM and read the edited transcript below. I recorded this video as part of the CxOTalk series of conversations with the world's top innovators in business, technology, government, and higher education.

We see technology -- artificial intelligence, machine learning -- changing work, changing jobs, and the relationship between people and machines. We see the world of work and alternative arrangements and agile teams. We see all of that playing into how work gets done.

And, and very importantly, we see skills become a key factor or driver in how people are thinking about their workforce, their talent, executing on their talent strategy.

We need to support them. They're looking for how they support their people in this ever-changing world of HR and world of work. And so for us, it's how do we help them become those enterprises in the future to identify, develop and optimize talent. Scale and speed are what we endeavor to help them with.

I think executing on our account strategy, where we're trying to match talent to talent demand, which is the work. For us, it's how do we take that data foundation, that rich foundation that we have, and build on it. I talked about skills earlier. It really is about building that skills and capability foundation, which we did with using machine learning.

It's a common language of skills across all of our customers. And most importantly, if you think of software as a service, it's skills as a service because it's crowdsourced. It dynamically lives and breathes and grows based on data. We've solved the data challenge of understanding, categorizing and continually keeping your skills updated.

The next thing was to solve the challenge of [knowing] what skills my people have. Taking machine learning to do that, to infer skills, matching people to work.

In the past, it has been very manual and challenging, if not almost impossible, because there wasn't a common language or way to do the matching. The technology wasn't there. Today that technology is there to sort through the huge volumes of data, to understand skills.

Data is the foundation to make this happen. At Workday, we started with that core system of record, which became that core foundation of data. Which now moves to a core system of capabilities. You now have data about your people that you can take action on, make recommendations. Using machine learning to make suggestions and make all of this happen. It doesn't happen without the data. That data foundation gets us to the next step.

The data comes from the billions of transactions and thousands of dimensions of the over 40 million workers in Workday.

Data is an important part of the future of work and a foundation for all the things we're going to do next.

Disclosure: Workday covered most of my travel to Workday Rising.

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Workday talks machine learning and the future of human capital management - ZDNet

Microsofts antivirus and malware division recently opened the bonnet on a malicious mutating cryptocurrency miner. The Washington-based big tech firm revealed how machine learning was crucial in putting a stop to it spreading further.

According to the Microsoft Defender Advanced Threat Protection team, a new malware dubbed Dexphot has been infecting computers since last year, but since June 2019 has been burning out thanks to machine learning.

Dexphot used a number of techniques such as encryption, obfuscation layers, and randomized files names, to disguise itself and hijack legitimate systems. If successful, the malware would run a cryptocurrency miner on the device. Whats more, a re-infection would be triggered if system admins detected it and attempt to uninstall it.

Microsoft says Dexphot always uses a cryptocurrency miner, but doesnt always use the same one. XMRig and JCE Miner were shown to be used over the course of Microsofts research.

At its peak in June this year, 80,000 machines are believed to have displayed malicious behavior after being infected by Dexphot.

Detecting and protecting against malware like Dexphot is challenging as it is polymorphic. This means that the malware can change its identifiable characteristics to sneak past definition-based antivirus software.

While Microsoft claims it was able to prevent infections in most cases, it also says its behavior-based machine learning models acted as a safety net when infections slipped through a systems primary defenses.

In simple terms, the machine learning model works by analyzing the behavior of a potentially infected system rather than scanning it for known infected files a safeguard against polymorphic malware. This means systems can be partly protected against unknown threats that use mechanics similar to other known attacks.

On a very basic level, system behaviors like high CPU usage could be a key indicator that a device has been infected. When this is spotted, antivirus software can take appropriate action to curtail the threat.

In the case of Dexphot, Microsoft says its machine learning-based detections blocked malicious system DLL (dynamic link library) files to prevent the attack in its early stages.

Microsoft has not released any information on how much cryptocurrency was earned as a result of the Dexphot campaign. But thanks to Microsofts machine learning strategy it seems to be putting a lid on it, as infections have dropped by over 80 percent.

It seems as long as there is cryptocurrency, bad actors will attempt to get their hands on it.

Just yesterday, Hard Fork reported that the Stantinko botnet, thats infected 500,000 devices worldwide, has added a cryptocurrency miner to its batch of malicious files.

Published November 27, 2019 09:27 UTC

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Microsoft reveals how it caught mutating Monero mining malware with machine learning - The Next Web