"Machine learning" may sound to some like the overused plot device from a slew of cautionary sci-fi blockbusters, but let us assure you - machines can learn, and it's something we all want them to do, however subconsciously.
Big data businesses especially see immense value in machine learning and how it pertains to IT service management. Between 2013 and 2020, the total amount of data on the planet will grow ten times over, according to a study conducted by EMC and International Data Corporation. That's because by the next decade, human beings will each produce an average of 1.7 megabytes of information every second. For businesses, machine learning is the best chance they have at truly understanding their consumer bases and anticipating how best to serve them.
ITSM has largely been responsive, dabbling in predictive analytics. Machine learning pushes ITSM well into the predictive sphere and potentially beyond. To understand more about how machine learning will transform traditional ITSM, you'll need to enroll in a few introductory "classes" at the School of Intelligent Service Management.
1. Algorithms 101
Let's start by talking about algorithms, the basic building blocks of machine learning.
Enterprise IT professionals build and connect algorithms to predict values based on raw data, and those algorithms combine with others to create models for predictive analytics. As TechTarget points out, we have yet to hone this technology down to an exact science. Rather, most companies employing algorithmic modeling are given "probabilistic correlations, not definitive conclusions." In plain English, these models offer many possibilities, but interpretation is still up to the user.
2. Wood Shop, or Predictive Modeling
If algorithms are the wood and the screws, predictive models are the birdhouses. That said, these models do a lot more, and the impact of machine learning on predictive modeling is really where we start to see effects of this innovative field on ITSM.
An independent algorithm provides low-level predictions, but combining several different algorithms could give users more actionable intelligence. Furthermore, machine learning can help make predictive models more applicable to varied data sets, so whole new models don't have to be built from scratch each time they're needed.
From an ITSM standpoint, machine learning advances the performance of enterprise service tools in help desk, IT service desk, CMDB, and change management. With machine learning, these already automated features no longer simply spell out the consequences of a given configuration change, for example, but offer viable solutions used to interpret and correct issues to enhance customer or end-user service.
3. Unsupervised Play
Everybody, even robots, need to spend a little time on the playground. A machine's version of recess, however, is still integral to the machine learning process and the new era of intelligent service management.
As we mentioned earlier, smarter predictive modeling produces more standardized models that can be applied to different situations. That's only half the picture. In fact, machine learning means predictive models will eventually be constructed by the machines themselves, as opposed to IT professionals.
What are the implications of a self-sufficient fleet of machines learning from enterprise data, building their own predictive models and testing them out as intelligent service management tools, all without any manual interaction? For starters, it ostensibly decreases enterprise IT workloads by taking automation to its next stage of evolution.
Final Word on Intelligent Service Management
There is a greater significance at play here, though. All workplaces have, in the last couple decades, become digital workplaces. That used to mean companies used fax machines, bulky desktop computers and a dial-up internet connections. Today, knowing customer data is to know the customer themselves, and businesses that utilize this information to provide more precise service solutions to bolster customer retention, strengthen internal processes and amplify their products are all the better for it.
Big data has opened a whole new sense businesses can use to be more responsive to users. The promise of machine learning, however, aims to make that sense more precognitive. Moreover, the enhancements machine learning offers ITSM allow it to be easily applied to new platforms as they emerged, like mobile, without learning curves or calibration.
Every business possesses troves of data poised to make IT operations more efficient, like a billion lockers filled with tens of billions of textbooks. Instead of IT professionals wading through all the material and attempting to decipher it or draw conclusions from it manually, machine learning burns through the pages, using the knowledge therein to develop next-level intelligent service management suites and further simplify the jobs of IT service professionals.