Rise of FinOps

As cloud workloads continue to climb as a percentage of total workloads, public and private cloud costs are going to climb as well. At the same time, more organizations are shifting to use of multiple cloud vendors, plus on-premises, plus private cloud, all of which increases complexity and therefore cost, while increasing the risk of inefficient purchasing. 

Since different cloud and hybrid services have different pricing and billing models, and costs can change from month to month, there now is a growing emphasis on management of cloud costs. The notion that FinOps solutions are required is therefore not too surprising, from the standpoint of vendors that hope to capitalize on the “new trend” by selling FinOps solutions. 

source: Cloudera, Harvard Business Review 

source: Cloudera, 451 Research

ServiceNow, IBM Team for AIOps

ServiceNow said it will integrate its service management and operational visibility tools with IBM's Watson software that automates information technology operations using artificial intelligence, or AIOps. Available later in 2020, the solution is intended to allow enterprise IT staff to identify, fix and prevent IT issues. 

AIOps value lies in its ability to improve pattern recognition, anomaly detection and determination of causation, Gartner says. AIOps uses a big data platform to aggregate siloed IT operations data in one place, IBM says. 

IBM notes that data can include:

• Historical performance and event data

• Streaming real-time operations events

• System logs and metrics

• Network data, including packet data

• Incident-related data and ticketing

• Related document-based data

Using Watson AIOps, the average time to resolve incidents was reduced by 65 percent, according to one recent initial proof of concept project with a client, ServiceNow says. “AIOps will detect patterns a human would be unlikely to uncover, including those that reveal cause and effect,” Gartner analysts have said. 

Enterprises might use AIOps to support a pattern detection algorithm supporting customer relationship operations. In such cases, software can map the metrics from IT and business data. 

User navigation might be correlated with digital experience data, order data, sentiment data and account activity.

That would enable the building of a composite model of a customer, across all applications they use and different behaviors across multiple modes of a single application such as when they use a web browser versus a mobile device, Gartner says.

Dreamworks uses AIOps to Smooth Out Big Rendering Jobs

“Any sufficiently advanced technology is indistinguishable from magic,” novelist Arthur C. Clarke once quipped. 

But successful advanced technology use cases always are concrete, and add value because real business problems are solved. Consider the way Dreamworks uses AIOps. As a digital content business, Dreamworks arguably has an easier time than most switching to fully-remote work. 

But even so, there are real problems AIOps helps Dreamworks solve. The way the studio schedules workloads if a case in point. One specific problem is ensuring that the internal computing network does not crash when huge rendering jobs are executed, such creating 150,000 animated people in a crowd scene.

Doing all the rendering at once would affect computing performance. 

"We don't want the artists noticing that something's performance has changed," says Skottie Miller, technology fellow and vice president of platform and services architecture at DreamWorks. "We want our synthetic transaction and monitoring framework to tell us before the artists notice that something is trending in a bad direction.”

"It used to be there would be an issue and maybe an engineer noticed because they were looking for it, or maybe the system sent an alert and an engineer would go investigate it," Miller notes. "Now an issue surfaces almost always with a recommendation and, in many cases, a solution before the engineer is in the loop.”

“It lets us run with 24x7 support with fewer sets of eyeballs staring at the systems,” he says. And that is precisely the sort of use case AIOps was envisioned to support: automating the alerting process and preparing a solution without information technology staffs having to do so manually. 

Telecom AIOPs

In the connectivity business, potential applications include network operations monitoring and management, predictive maintenance, fraud mitigation, cybersecurity, customer service, marketing virtual digital assistants, customer relationship management preventive maintenance and battery optimization, for example. 

In the network operations monitoring area, that might include anomaly detection for operations, administration, maintenance and provisioning (OAM&P), performance watching and optimization, alert or alarm suppression, bother price ticket action recommendations, automated resolution of bother tickets, prediction of network faults or congestion prediction, for example. 

Artificial intelligence is a capability, not a product. 

“You don’t focus on ‘I’m going to go do AI,’ says Peter Guerra, North America chief data scientist at Accenture. “You focus on ‘I’m going to do supply chain better, and I’m going to leverage AI to do that.’” Peter Guerra, North America chief data scientist at Accenture.

Hughes Network Systems Adds AIOps to its Managed Services

We are, by most estimates, relatively early in the artificial intelligence life cycle. Most AI-based capabilities tracked by Gartner are five to 10 years away from widespread commercial use. Some might say the same is true of connectivity service provider use of AI to support their own network operations.

But it is coming. Hughes Network Systems, for example, has commercial availability of its artificial intelligence capability for supporting information technology operations (AIOps).

Integrated into the company’s HughesON Managed Network Services, the Hughes AIOps feature is already in use across more than 32,000 managed sites. The technology automatically predicts and preempts—or “self-heals”—undesirable network behavior, preventing service-disrupting symptoms in 70 percent  of cases, HNS says.

Hughes says it s the first managed services provider to deliver a self-healing WAN edge capability to enterprise customers.

source: Gartner 

Of course, AI will have impact in many other ways. At two recent sessions of the PTC Academy, a training course for mid-career telecom professionals, the point was made that artificial intelligence, more automated business processes and competitive pressures on profit margins all would combine to reduce headcount in the industry. That is not a judgment about the morality of the trend, just a prediction of what will happen. 

Nor are such observations in any way denying that new jobs that will almost inevitably be created as the automation, artificial intelligence and “substitute machines for humans” trends unfold. 

Big technology changes have happened before. Much-higher mechanization of agriculture drove most U.S. residents off farms and into urban centers, where those people and their descendants worked in new roles. A shift of value from goods to services likewise has shifted people out of factories and created new jobs elsewhere in the economy, particularly in a wide range of services roles. 

While the shift within the connectivity industry might not be that pronounced, industry headcount has been dipping for some decades, though offset by growth in the mobility segment. In the U.S. market, you can see the slow attrition of fixed network employment since the internet bubble peak and crash. 

The emergence of the mobility business as the industry growth driver was accompanied by job expansion in that segment of the business, stabilizing around 2002 and then falling after 2009. 

source: Bureau of Labor Statistics

To the extent that profit margins continue to be under pressure, and industry revenue growth anemic (less than one percent per year, globally), we should expect more substitution of machine operations for humans. 

AIOps for Dummies

AIOps is among the esoteric disciplines information technology managers encounter these days. Many would argue that is the case because information technology operations these days are complex and decentralized. 

To a large degree, that is because modern IT operations no longer are conducted “in house,” but also integrate cloud computing operations, virtual servers spinning up and down, containers sharing operating systems and providing microservices, mobile device and app support. New Internet of Things apps and devices, cloud storage, software as a service and platform as a service all contribute to complexity. 

Among the issues is that AIOps--like artificial computing generally--is not a product. It is instead a capability that enhances other products and services. “AIOps isn’t a specific off-the-shelf product,” say Tony Branton and Ted Coombs, authors of a ServiceNow edition of “AIOps for Dummies.”

Specifically, AIOps is about applying machine learning to supervision processes. Right now, that mostly means AIOps “sits on top” of existing management tools. “Machine learning is a branch of AI that uses the ability of computers to learn by analyzing data and improving answers to questions posed to it autonomously,” the authors say. 

Typically, that means applying supervised or unsupervised learning routines to security or asset management; root-cause analysis; change management; impact analysis, capacity management; performance and availability management. 

Is AIOps a Capability or a Product?

Some markets are hard to estimate because the definitions matter so much. Consider AIOps. A new research report predicts the global AIOps platform market will reach US $11.1 billion in sales by 2025, growing at about a 34 percent compound annual growth rate. 

Aside from the typical issues--how do we define platform; what is AIOps--we face some additional questions. Is artificial intelligence a capability of existing products or a new product category? If the former, then AIOps is a capability, not a product category. If the latter, is it only new products which count? 

source: Techtarget

“Major growth factors” include the growing demand for AI-based value in  IT operations, Cole Reports says. But how do we separate AI features that are embedded in existing platforms and software? Is it the full value of the retail product, or only the incremental value added by AI? 

To use one example, AIOps is not about automation in a direct sense, some would argue, but is a tool to achieve automation. AIOps essentially requires analytics conducted on big data sets, but is not directly big data. AIOps is normally part of some analytics solution for some IT operations function. 

source: dzone

The Cole Reports study suggests properly that AI will be used in information technology operations, which illustrates the definitional issue. If AI improves operations processes, can we cleanly separate AI from the value AI providers for existing operations management products, both embedded into other software and provided as stand-alone solutions?

It is not an unusual task. When estimating 5G revenue, a proper understanding of incremental revenue growth would also subtract the value of replaced 4G connections. Growing streaming revenue also has to be balanced against lower linear subscription revenue to evaluate actual net market changes. 

AIOps presents some similar issues. Definitions matter. If every analytics platform or operations system includes AIOps features, then AIOps becomes almost a meaningless term.