White Paper IT Operations Management The State of Analytics in IT Operations IT operations analytics Introduction holds considerable promise for making day-to-day If you lived through the AI (artificial intelligence) hype of the 1990s or earlier, you might be skeptical about IT Ops work easier. seeing the term in such frequent use these days. AI can mean many different things, andalways has. It has also changed names a few times, from IT Operations Analytics (ITOA) to Algorithmic IT Ops, AIOps, and Cognitive Operations. But over the past two years, with influential analyst firms like Gartner and Forrester getting on board with the term, AI is getting more respect, and it’s getting more practical. According to a recent Forrester survey, ITOA is the number one application of AI technology that busi- nesses are considering. Also high on Forrester’s list are business insight and security, all of which are related to ITOA at a fundamental level. IT Ops Analytics all starts with data collection or monitoring data. Analytics is dependent on data and lots of it, often called Big Data. Data is the food that fuels analytics, without it analytics has nothing to look at to find patterns or anomalies that provide us insight. IT Operations Analytics holds considerable promise for making day-to-day IT Ops work easier. But what does this mean for IT Ops specialists who aren’t trained in analytics? Do they now need to take classes in data science and machine learning, and learn to write the algorithms that lie at the heart of analytics capabilities? No. But it does mean that IT Ops specialists should be at least familiar with the kinds of analytics be- ing used, increasingly, in their industry. They should take advantage of whatever analytic capabilities are embedded in their tools, and they should know when to seek guidance from other teams in the organiza- tion – security, big data, business intelligence teams, for example—when they have questions or want to improve their analytics skills. There’s a lot to consider. Here is an overview of what’s happening today in the ITOA space, along with some expert advice. IT Ops Teams: Don’t Panic Over Analytics Compared to specialists in security or big data, where analytics is a core part of the job description, the analytic skills within an IT Ops organization tend to be relatively low, which is to be expected. The technology and the field itself is fairly new within IT Ops. Besides, “analytics within IT Ops isn’t usually something that demands a data scientist,” says Michele Goetz, principal analyst with Forrester Research, who specializes in business insights, artificial intelligence, information management, architecture, and strategy. www.microfocus.com 1 White Paper The State of Analytics in IT Operations “IT Ops teams in mid to large size orgs often tap into the analytic know-how within other teams, if possible,” says Goetz. This might include a security, or business intelligence, or big data team that can provide basic “But what I see is that IT Ops specialist tend to rely help or training for those just getting their feet wet with analytics. on the analytics capabilities within the platforms and “But what I see is that IT Ops specialist tend to rely on the analytics capabilities within the platforms and solutions acquired for solutions acquired for the IT Ops organization,” says Goetz. “These are not the same capabilities you find the IT Ops organization,” in security analytics tools, which represent some of the most sophisticated capabilities on the market.” says says Michele Goetz, Instead, these are tools to help performance, monitor and predict spending, what Goetz calls “the block- principal analyst with Forrester Research. and-tackle job of running and maintaining the platform, keeping the lights on, being agile to support busi- ness needs. These are the things that require operational analytics.” Some teams are running models that give them a better understanding about cost to performance, they’re managing resources with tools that can give them more detail than the higher-level performance metrics they might have used in the past. “This is not big, sophisticated predictive and prescriptive modelling, that you might see in other parts of the business. The best IT Ops teams are looking to mine a little deeper into the system data that comes from their infrastructure or learning about the types of queries they can run against the system and figure out better styles of workload management,” Goetz says. So, don’t panic if you don’t have the skills to be more sophisticated with analytics. But you may want to begin exploring machine learning, “at least take a look at what your own solutions offer for embedded machine learning in the operations,” says Goetz. “This will bring you up a level over the coming year.” Controlling The Spend: The Number One Target For IT Ops Analytics Analytics, in addition to all its other users, has a focus on the spending side of IT Ops. You’re trying to lower you cost to performance. What is the total cost of ownership for your technologies? How do you right size your resources, and what’s happening with your outsourcing and contracting? The goal is to get smarter about the resources you need, the investments you need to make. CIOs are constantly under pressure to contain their budgets, which means justifying expenditures against the business value. And much of the analytics that can help with this comes out of the box for many tools. 2 “Take automated data warehouses,” Goetz continues. “Based on years of understanding how data centers Good analytics leverages run in the cloud, what those workloads are, all that understanding is built into the tools.” information across a number of different sources to help meet service level Rather than requiring users to figure out their particular environment, the machine smarts can guide you agreements (SLAs). via patterns that come preconfigured. Meanwhile, “vendors of IT Ops technology continue to learn how different types of workloads and administrative tasks are informing how you’re managing and optimizing those environments as well as managing it toward your cost to performance models,” Goetz says. “This guidance is not as targeted, of course, as if a Google or Facebook data scientist were opening the box and tweaking the model. But this is going to be much more of the norm than needing a data scientist in-house. The pretrained environments are usually sufficient.” IT Service Management: Analytics Drives Issue Resolution An efficient, authoritative IT service desk can be a business’s best line of defense when customers call with critical software problems. It can put the customer at ease, with faster ticket resolution, and even faster resolution when the problem involves a known issue can be resolved via smart self-service based on analytics. “Good analytics leverages information across a number of different sources to help a worker opening up a ticket,” says Jeff Jamieson, CEO of Whitlock Infrastructure Solutions. “A problem is described, and the analytics engine underneath can tell you, essentially, ‘wait... we just had 15 other people log this same problem.’ We’re seeing our customers adopting machine learning as a way to drive down the time and cost of tickets.” (More on machine learning below.) As ITSM teams monitor business services they provide to customers, capabilities like smart search, smart ticket, virtual agents for 24x7 support, and social collaboration, all based on machine learning and analytics, help meeting related service level agreements (SLAs). These issues can cover a broad range of areas and may have to do with business processes like order-to-cash or infrastructure services like email. But the biggest ITSM payoff for analytics may lie in understanding how long it takes to resolve tickets. Identifying root causes—and providing solutions—before they become widely reported problems im- proves your business’s reputation, reduces labor costs, and leads to better services. www.microfocus.com 3 White Paper The State of Analytics in IT Operations Anomaly Detection and Resolution via ChatOps “But the beauty of analytics- driven anomaly detection As IT Ops teams use tools to define baselines for normal operations, they’re setting the stage for anomaly is that you don’t have to detection – the ability to find what’s out of spec or overloaded, conditions that something is out of bounds. know everything that might go wrong. While there are “When there’s an outage or a failure, there’s a common reaction on the business side: ‘Hey, we pay all this millions of log files that money for monitoring tools; why didn’t you catch this problem?’” says Jamieson. have captured what’s going on in your environment, “Typically, you can only catch things that you anticipate. The things that drive our customers crazy are analytics can point you to events that they can’t even imagine—events, for example, based on a piece of infrastructure that no one 3, 4, or 6 areas that seem to be most relevant to has a clue was there. your problem, based on data. This is a new type “But the beauty of analytics-driven anomaly detection is that you don’t have to know everything that might of opportunity,” says Jeff go wrong. While there are millions of log files that have captured what’s going on in your environment, Jamieson, CEO of Whitlock analytics can point you to 3, 4, or 6 areas that seem to be most relevant to your problem, based on data. Infrastructure Solutions This is a new type of opportunity.” With analytics built into performance monitoring tools, IT Ops teams may have the ability to review timelines for performance on specific servers and see where and when performance took a hit.