One way to think about data science is as a wide and wonderful world, myriad sub-genres encompassed within the wide umbrella it spreads. Another way to understand data science is as the meeting point at which machine learning tools, predictive analytics, and other forms of analysis converge to draw critical insights from within vast oceans of raw data. Whichever way you look at it, you can see that data scientists work with many different processes to gain the insights and information they need to make all that data collection worthwhile. After all, just collecting data is useless unless you have the tools necessary to separate the wheat from the chaff and use that grain to nourish the decision-making you take on as a business leader.
One of the processes that data scientists use to glean important insights and useful information from within the raw data at their disposal is machine learning. In other words, although data science makes use of machine learning, they are not the same thing. Machine learning is part of data science; it is just one aspect that allows data scientists to do their job effectively and well. Data science is the umbrella under which machine learning resides.
It’s an exciting time for data science. According to the 2020 Gartner MQ Data Science report, there are advancements in the field of data analytics, machine learning, and business intelligence that allow business users and vendors alike access to insight-driven power they didn’t believe possible just a few short years ago. So, with that in mind, what is machine learning, and how can it be used to drive your business all the way to success?
Machine Learning Processes
Machine learning is sort of what it sounds like—machines that can learn. Not in the way we may think about learning—a child learning their ABCs and much—more in the vein of learning from mistakes. For example, an algorithm can be trained to recognize certain patterns, images, and other information. The algorithms learn by being fed large quantities of information and undergoing training processes to learn from their mistakes. If the algorithms guess that an image of a dog is an image of a cat, it undergoes a correction until the level of precision is extremely high and reliable.
This is, of course, a very rudimentary example. Advanced algorithms can do a whole lot more than telling cats from dogs. These days, powerful data analytics solutions and business intelligence platforms can use real-time streaming data and immersive imaging to extract key insights from within the seas of data that a company amasses daily. Based on historical data, advanced machine learning processes can use predictive analytics and statistical analysis to very nearly tell the future of a company’s outlook with some accuracy. Knowing what tomorrow may bring is a huge boon to any business users who use machine learning as it works within business intelligence platforms.
How to Use Machine Learning and Data Science
As suggested above, machine learning and data science provide great power to any technology users who may choose to make use of them. That being said, even regular people who aren’t CEOs of major corporations actually already use machine learning daily, whether we know it or not. When Google maps suggest a location you usually go to at a certain time, the algorithm is learning your habits. When Netflix recommends a movie you may like, the machine is learning your tastes, and when Facebook recommends someone, you may know their algorithm is learning about your social circles.
Machine learning and data science are used in other ways—to recognize dangerous or copyrighted content being spread around the web, to help anticipate natural disasters so that governments can prepare ahead of time, and drive market change and industry developments. With the whole wild and wonderful world of data out there, it’s exciting to think what tomorrow may bring. Even with advanced predictive analytics, that’s one thing we can’t know completely for sure.
