How to make the most out of your machine learning investments
Machine learning (ML) is disrupting businesses across the world, creating waves within the IT community and fueling total organisational transformations. There’s no doubt that organisations everywhere (probably even yours) are jumping on the bandwagon and working to implement ML capabilities. But have you asked yourself the question, “How successfully is my organisation applying machine-generated insight?”
What would your honest response be?
My prediction is that all too often these insights are uncovered but not applied because business leaders neither understand nor trust the outcomes, or may not have the budget or the skillset needed to make real changes. For others, ML has become a check box—you buy it, you check it off the list, and you never touch it again.
If your organisation has or is considered purchasing ML capabilities, you need to first understand what ML learning actually is, the role big data plays in delivering insight, and what actions you should be taking now to truly transform with ML.
Machine learning, in a nutshell
Do you ever hear people refer to artificial intelligence (AI) and ML as one in the same? The first step in executing a successful ML strategy is to understand that it’s different from AI. Artificial intelligence is technology designed to perform tasks that are typically reserved for humans (for example, voice assistants like Alexa or Siri).
Machine learning falls within AI, but uses algorithms to generate learnings from structured and unstructured data. It sifts through large data sets which might include images, text, voice, video, location and even facial recognition data. By analysing these data sets, ML identifies correlations, patterns and trends that can be used to make predictions.
The role of big data
Machine learning is unique in that it works very much like the human brain does. The more information you input, the smarter it becomes. Globally, businesses and consumers collectively produce 2.5 quintillion bytes of data each day, which is enough to fill 100 million blu-ray discs! For ML, this amount of data would be considered a feast because it thrives on large sets of unstructured and structured data, which can reveal hidden forecasts, predictions and insights using algorithms.
You’ve likely experienced the end result of this analytical process for yourself without even realizing it. If you like to Netflix (and chill), you’ve seen a category for “Because you recently watched…” which serves up recommended shows based on your past viewing behaviors. Or if you post photos on Facebook you’ve probably used the facial recognition capabilities to tag a photo instead of typing in the person’s name. And following Amazon Prime Day, you may still receive product suggestions that complement your discount purchases.
