For any company that relies on managed service providers (MSPs) to automate IT services and streamline operations, there are no two words perhaps more important to their future than machine learning. Machine Learning is the intelligent interpretation of high volumes of data by machines to make improvements without being explicitly programmed.
Although machine learning is not a new concept to data scientists, the term has taken the business world by storm in the last several years, thanks to its increasingly wide range of applications. In fact, a Forrester brief states that machine learning investments should increase by more than 300% this year compared to 2016. As machine learning becomes more common for MSPs and used within more applications, it will become more fundamental to the success of any emerging business.
Traditionally, managed services collect large volumes of data from their monitoring services. The data is collected, reviewed, and acted upon by the service provider. As the volume of data scales up, it becomes increasingly challenging, if not impossible, to have eyes reviewing all the data being generated. This is where automated workflows can be highly effective in improving existing managed service solutions. Those quick to adapt are already seeing that machine learning serves to accelerate these improvements — but how?
Machine learning 101
Like other innovations, such as the computer and the steam engine, machine learning is known as a foundational technology — that is, one whose applications grow both horizontally and vertically beyond its initial intended use. As the Entrepreneur article points out, those applications include self-driving cars, music recommendations, and personalized ads.
Some of these processes may seem like the work of pure magic; however, in reality, they’re highly advanced and complex. This technology operates via a facet called deep learning. In short, a computer uses unstructured data — such as pictures, sounds, and behaviors — and draws conclusions based on rules of its own design. This differs from the logic found in traditional programs in that a machine learning program operates autonomously and intuitively.
Machine learning helps to quickly analyze the data and then recommends a set of actions. For example, existing data collected from monitoring is analyzed for trends, patterns, or issues previously faced to identify actions that can be implemented. The ability to use existing customer data to generate a prediction model and identify an optimal algorithm can, in turn, improve a MSP user’s ability to locate and attract new customers. Where users once had to sift through millions of rows the algorithm goes through based on patterns and shortlist data elements, they can now look at a very rapid pace and drive actions.
Currently, most MSPs use automated workflows that they create themselves and program into the systems, but machine learning would eliminate the need for a person to do that. Thus, this high-performing interpretation helps to automate IT operations on a greater scale than ever before.
Smarter data integrity
Forward-thinking and the adaptation of such technologies is important to both MSPs and their clients. Incorporating a more advanced level of predictive analysis and automation can enhance proactive monitoring and reduce the level of effort required of consultants. While MSPs help to streamline a business through managing IT complexities, machine learning optimizes the functions of these complex systems, creating a win-win.
For Value Global clients, machine learning means improved data analytics, which translate to better levels of service and a more efficient organization. The intricate data sets these systems build ultimately allow users to gain new insights and improve existing services. And the ability to fast-track the evolution of managed services can save both time and money, while also giving users a competitive advantage.
As machine learning becomes more common in technology and, more importantly, in business, clients will continue to demand more intensive and intelligent IT services. Thankfully, the organic, foundational nature of machine learning offers an optimistic image for the future of managed services. All it takes is the desire to innovate — and a little clever programming.