One of the key drivers which has crafted the IT landscape for many years is the business requirement for organizational agility. IT is no longer just a back office function, it is an enabler of business success and is quite rightly perceived as a catalyst for generating innovation. Today, systems are expected to be adaptable and ready to react to the competition. So, what has been the impact on IT systems?
Firstly, IT systems have become more modular and independent. For developers, a modular system is easier to change and hence agility is more achievable. The re-architecting of IT systems has been conducted on a massive scale as the agility business philosophy sits firmly at the heart of all IT strategies.
However, when you have components acting independently it becomes harder to monitor and analyze performance. If you want to truly understand what a system is up to, it has to be observed end-to-end. With traditional, monolithic systems, IT Operations teams could use snapshots to determine how a system is performing. This is not the case with modularity. Businesses now require more data if they are going to get a true picture of what is occurring under the hood.
However, it’s not just a question of increased volumes of data, it’s also the behavior of individual data sets which has become harder to interpret. Therefore, in many respects, the drive for modularity has led to the emergence of AIOps, but it’s not the only factor. Modularity has also led to the greater distribution of IT systems. Due to the geographical spread of IT systems, they are no longer confined to the data center.
If you are running blind, profits are likely to fall. Now, if that isn’t an incentive to deploy AIOps I don’t know what is.
Visibility 101: AIOps Is a Prerequisite
In addition, a growing percentage of modules have become more short-lived, such as containers and microservices, so the final nail in the coffin for operations teams is that the system they’re trying to manage and monitor is constantly changing and evolving.
The business demand of increased IT agility has increased the difficulty in managing and supporting IT systems. Agility has come at a price. At Moogsoft we talk about an agility tax and it’s fair to say that the tax has been severe. Given that these IT systems are not going to change, we must assume that these challenges are only going to get worse. Given all this, how can you assure the reliability of IT systems?
IT Operations teams need to invest in AIOps — not to predict the future, or to run a fully autonomic self-healing data center or to reduce meantime to discovery to almost zero. They need it so they have eyes on how the system is performing. Sadly, the alternative is to be blind and to pretend that the team is in control. In any digital business, AIOps is now a prerequisite for basic visibility.
The Need for More Dynamic and Collaborative Decision Making
If you embrace AIOps, do you need to change the culture? I would say there are two changes. Firstly, because we are dealing with rapidly changing IT systems, the way in which teams observe and try to resolve problems changes. Previously, top down, deterministic, almost mechanistic approaches have been used. Due to the speed of change in IT systems, this approach no longer works.
Decision making now needs to be more dynamic and collaborative as everything is more distributed. Look at the way agile methodologies have changed application development — a similar wave of agile thinking is sweeping over IT Operations. AIOps is part of a tool kit which enables IT Ops teams to become more dynamic and agile. So, the way in which people work will change, as decision making becomes more democratic and distributed.
One important footnote here is how teams process incident management and problem management. Traditionally, incident and problem management were viewed as distinct processes. That completely dissolves in a modern IT environment as things are changing so rapidly that you cannot distinguish between incidents and problems. What AIOps brings to the table is to present incidents within the context of the problems that they manifest, so you move directly from the observation of the incident to problem management.
Increased Demand for Data Science Skills
Secondly, IT Operations teams are at some point going to have to acquire data science skills. You buy technologies like AIOps to perform complex inferences, which means decisions need to be made based on the results these technologies present. To make these decisions you need some understanding on why you make the decision in the first place. These are decisions that impact both IT and the business in general. No matter how the results are obtained, there is a degree of responsibility on the team or individual to be able to interpret the data.
Within IT Operations there is a need for more data science skills to deal with the new complexities IT systems are producing. Data science skills are required, but you don’t need the title of a data science professional to do your job. In summary, by using AIOps, IT Ops teams will become more collaborative, democratic, less rigid, more dynamic, and more entrepreneurial. Ultimately, this is the real consequence of the business wanting to become more agile.
Consequences of Operating Blind
It’s no longer a question of do we really need AIOps, it is now a prerequisite if operations teams want to resolve issues and fully understand how their systems are performing. Remember, optimized IT systems are revenue generators and play a significant role in organizational growth. If you are running blind, profits are likely to fall. Now, if that isn’t an incentive to deploy AIOps I don’t know what is.
About the author
Will studied math and philosophy at university, has been involved in the IT industry for over 30 years, and for most of his professional life has focused on both AI and IT operations management technology and practises. As an analyst at Gartner he is widely credited for having been the first to define the AIOps market and has recently joined Moogsoft as CTO, EMEA and VP of Product Strategy. In his spare time, he dabbles in ancient languages.