{"id":28128,"date":"2021-12-31T06:00:00","date_gmt":"2021-12-31T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28128"},"modified":"2022-01-01T11:50:27","modified_gmt":"2022-01-01T19:50:27","slug":"how-to-develop-a-long-term-aiops-strategy","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/12\/31\/how-to-develop-a-long-term-aiops-strategy\/","title":{"rendered":"How to Develop a Long-Term AIOps Strategy"},"content":{"rendered":"\n

Artificial intelligence for IT operations, or AIOps, has increased in demand over the last few years as enterprise IT teams battle complex data coming into their systems. Not only does AIOps automate mundane tasks, but it processes vast amounts of data. Networks, systems, and applications pump out tremendous amounts of data that has to be analyzed for patterns and correlation. AI and machine learning are perfect for rapidly looking for trends, patterns, and anomalies in massive amounts of data streams. Humans are overwhelmingly visual beings, and we are not designed to wade through massive amounts of textual data, hence the need for AIOps.<\/p>\n\n\n\n

Many companies may view AIOps as a tool. While there certainly are AIOps \u201cplatforms,\u201d  \u2014 the value AIOps brings should be viewed as more of a long-term strategy <\/em>for enterprises. Through an AIOps strategy, teams increase performance, predict outages, automate repeatable tasks, develop actionable reports, and improve overall risk management while setting their entire organization and its customers up for success. As enterprise leaders determine when and how to invest in AIOps, it\u2019s essential they view the investment and commitment as a strategy rather than a single solution. Here are three steps to kickstart a long-term AIOps strategy.<\/p>\n\n\n\n

Determine your level of AIOps maturity<\/strong><\/p>\n\n\n\n

There are five levels of AIOps maturity: reactive, integrated, analytical, prescriptive and automated. Before business leaders set out on their AIOps journey, they must determine which level they\u2019re currently in to better understand how AIOps aligns with their business needs.<\/p>\n\n\n\n

In the reactive stage, teams face siloed operations and collect events and logs for reactive purposes. They\u2019re in constant fire-fighting mode and there\u2019s no communication with other parts of the business. The integrated stage provides data sources integrated into a unified architecture, offers improved ITSM processes and communication slowly, but surely, begins to improve with the business.<\/p>\n\n\n\n

As teams move to the analytical and prescriptive stages, transparency of data increases, machine learning and automation come into play, and comparative analytics measure improvements and business value. The final stage \u2014 the automated stage \u2014 achieves full automation with no human interaction, and teams make proactive decisions based on business value.<\/p>\n\n\n\n

No matter which stage you\u2019re currently in, it is possible to move to the next, but it takes time, patience, and a long-term commitment.<\/p>\n\n\n\n

Review tools and capabilities<\/strong><\/p>\n\n\n\n

Another important piece of a long-term strategy is to review your current tools to see where AIOps would be most beneficial to your organization. Determine where you have gaps that AIOps can fill, too. Understanding where and how AIOps can be the most beneficial among your other tools is essential when viewing this as a strategy versus a siloed tool.<\/p>\n\n\n\n

As you review these tools and capabilities, you may realize you have many tools that overlap essentially do the same thing, otherwise known as tool sprawl. As you\u2019re evaluating your current toolset and how AIOps fits into it, consider a tools rationalization process to evaluate overlapping tools and capabilities and determine which ones can be cut out. Companies that take time to achieve a tools rationalization process can save millions of dollars each year by eliminating unnecessary tools.<\/p>\n\n\n\n

Identify use cases and best practices within your organization<\/strong><\/p>\n\n\n\n

Ultimately, business leaders must be able to identify why they need an AIOps strategy. Because it\u2019s a long-term commitment, it isn\u2019t as simple as slapping on another tool. You must think critically about how it will help your organization in both the short-term and long-term.<\/p>\n\n\n\n

A recent report from EMA<\/a> lists the top three use cases for machine learning and AIOps as incident, performance, and availability management; change impact and capacity optimization; and business impact and IT-to-business alignment. Are these gaps you\u2019re trying to fill within your organization? While these are the most common, it\u2019s important to understand what specific use cases your business is hoping to solve with AIOps and how it can support you long-term.<\/p>\n\n\n\n

An AIOps journey can be intimidating, but it is possible with the right guide. As with any successful strategy, a long-term AIOps strategy requires leadership, vision and commitment from everyone across the entire business. Simply put, the right strategy can set the stage for future success, innovation and growth for enterprises, today, tomorrow and for years to come.<\/p>\n\n\n\n

—<\/p>\n\n\n\n

Sean McDermott<\/a> is Founder and CEO of Windward Consulting<\/a> and RedMonocle.<\/a> He also acts as Lead Researcher at Helix Market Research<\/a>. Sean previously acted as Founder and CEO of RealOps, Inc., the pioneer in enterprise management Run Book Automation solutions which was acquired by BMC. Sean\u2019s curiosity for advancing technology began at his first job as a network engineer\/architect installing and managing the first private internet for the U.S. Department of Justice. At a time when the internet was just taking off, Sean was at the forefront and has continued to be on cutting edge of technology with the development of Windward and RedMonocle. Sean is an advocate for business leadership strategies and shares how other entrepreneurs can align passion and action on his blog, Wheels up World.<\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Sean McDermott is Founder and CEO of Windward Consulting<\/a> and RedMonocle.<\/a> He also acts as Lead Researcher at Helix Market Research<\/a>. Sean previously acted as Founder and CEO of RealOps, Inc., the pioneer in enterprise management Run Book Automation solutions which was acquired by BMC. Sean\u2019s curiosity for advancing technology began at his first job as a network engineer\/architect installing and managing the first private internet for the U.S. Department of Justice. At a time when the internet was just taking off, Sean was at the forefront and has continued to be on cutting edge of technology with the development of Windward and RedMonocle. Sean is an advocate for business leadership strategies and shares how other entrepreneurs can align passion and action on his blog, Wheels up World. <\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, Sean McDermott, CEO and founder of Windward Consulting Group and RedMonocle, discusses how AIOps isn’t just a solution, it’s a long-term strategy for increasing performance, predicting outages, automating mundane tasks and improving risk management. These ultimately allow IT teams to add business value, spend more time innovating and less time fixing, and improve the overall customer experience. <\/p>\n","protected":false},"author":10513,"featured_media":21162,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,87,180,56,97,1],"tags":[754,95],"acf":[],"yoast_head":"\nHow to Develop a Long-Term AIOps Strategy - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/12\/31\/how-to-develop-a-long-term-aiops-strategy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Develop a Long-Term AIOps Strategy - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, Sean McDermott, CEO and founder of Windward Consulting Group and RedMonocle, discusses how AIOps isn't just a solution, it's a long-term strategy for increasing performance, predicting outages, automating mundane tasks and improving risk management. 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