by Vice President – Technology and Innovation at SLK softwarean automation company with three decades of experience in IT transformation.
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My last article was the start of a series on eliminating noise in existing operations. In this paper, a simple rule of thumb proposed is to detect noise in operations that do not require human intervention. In this series of articles, I will focus on what contributes to this noise in the form of the machine (environment), method (workflow and processes) and model (people and skills) and how to develop artificial intelligence (AI ) and machine learning (ML) techniques to detect and remove noise. This article covers engine noise.
Before we get into that, I’d like to draw your attention to the core functions of AI for IT services (or, for that matter, any kind of service), which is to learn how problems are currently being solved, to identify flaws in current problem solving method and building intelligence on how to solve problems differently to achieve top business performance. Here, looking at how problems are solved is paramount, and in future articles, I’ll look at how environment, process and people together produce the noise we aim to eliminate.
Let’s now focus on the noise produced by the engine.
One of the lowest opportunities for a technology-driven transformation was end-of-life products. While upgrades and replacements are common sense, it is of utmost importance to first establish the connection between the end-of-life machine and the noise in AI-based IT services. We also need to quantify how much of their end-of-life machine noise is noise in the method and model. CXOs should be able to identify how end-of-life machine noise, reflected in the method and model, impacts business performance. This relationship in the form of behaviors and patterns is available in historical data as mentioned in my previous article.
Common technology-driven transformations that struggle to make a business case because of a lack of intelligence about how much machine noise can be avoided and how much it affects business performance include cloud and software-defined “anything”. These technology platforms are promising and are being adopted by many businesses, especially startups, but when a traditional business explores this journey, it often never gets past the proof-of-concept stage. In some cases, CXOs appear to have challenges that justify the benefits they provide to the bottom line of the business. Some companies in recent years have been forced to reverse their digital migration due to a lack of justification of the benefits, financial and otherwise.
Thus, identifying and eliminating noise become very important steps in transformation initiatives that are otherwise just technology changes with unfulfilled promises. A recent analysis we did of a customer’s historical ticket data showed a lack of flexibility at scale, which arguably made a good case for cloud adoption. When the data were examined for noise, we found that much of this lack of agility was contributed by the method and the model. The first step suggested to this business was to remove the noise and then take a look at the flexibility requirements. In doing so, they were able to change the method and model to be more conducive to public cloud adoption, thereby demonstrating the benefits to the line of business.
Machines don’t just refer to infrastructure – what we tend to loosely define as computing, networking and storage. It can also include business applications, both custom and off-the-shelf, so it will be important for CXOs to also look at application-driven noise to see how it affects the method and model.
In conclusion, I’d like to leave you with a few takeaways. Look at engine noise at all levels of the machine—infrastructure, application and data platforms—and target transformation programs to remove that noise. While doing this, you should be able to establish the connection to the method and model and make any necessary process and organizational changes. Most importantly, don’t attempt transformation without removing the noise first.
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