The field of Machine Learning (ML) originated amidst the growing urge to process data into knowledge and boosted by the last thirty years of remarkable computer advancements. Simply put, ML relies on inference to extract patterns from correlated data, trying to model the process that generates a certain ground truth.
As progress in this filed is ramping up, let’s recall five key reasons why you should use Machine Learning:
Business experts would be the first to tell you that their decades of experience in the craft would never be beaten by an automated tool. Truth be told, some empirical knowledge is either too unstructured or diffuse to be properly and swiftly encoded into machine code.
Yet, the human capabilities fall often overwhelmed when having to cross-reference just a handful of variables. Then, business beliefs and human bias kick in, leading to severe distortions in the outcome. ML algorithms, however, gather from intricate interactions in the data to create a truthful model of reality within seconds.
Accuracy of outcomes, however, may not even be this technology’s greatest selling point. Besides structuring the approach to a problem, an automated or semi-automated analytical pipeline can dramatically shorten the lead time to generate novel scenarios and decrease or drop human intervention, freeing up resources that can be deployed for higher value-added activities.
ML is no longer just a trendy subject for academics to boast about. It’s a proven technology that has been down the beaten path as many have before, showing its robustness every step of the way. It has been filtering into the business world as far back as the last century.
With a large community backing it up, thousands of projects accessible at a moment’s notice and several open-source libraries, the democratization of state-of-art techniques is nearly upon us.
Vouching for ML algorithms is also their ability to thrive when heaps of data are poured onto them. Conventional approaches, such as statistical ones, offer poor scaling performance, a fact that renders them useless in today’s streaming data world.
It’s not seldom we see ML models labelled as “black boxes”. That label is somewhat misleading: the models are surely complex beyond human grasp, but researchers and practitioners alike have developed a plethora of strategies to shed light on their inner workings, rendering such black boxes far less opaque and more informative to the decision-maker.
Hopefully, by now it becomes clear that ML’s flagship improvements on hard analytical metrics, like accuracy, and the often-overlooked procedural benefits have a combined impact that may be transformational to the organizations. And still, progress on this field is ramping up.
Do not let preconceptions of unyielding complexity, which are often unkind at best, and plain wrong, at worst, make you an advocate against kicking your business into a new gear.