With businesses eagerly pursuing big data analytics, it only stands to reason that they’d look for the methods and strategies that will best help them get the most out of it. There are many ways to perform analytics, and each will change depending on the type of business and what insights organizations want to gain. With this variety, big data has clearly grown in popularity, with a recent survey from Gartner showing that 75 percent of companies are either currently investing in big data initiatives or plan to do so within the next two years. Even so, many companies have found utilizing their big data to be a difficult and at times arduous process. The traditional analytical approaches have trouble managing the vast volumes of data businesses can now collect, and as a consequence, the results aren’t always the most accurate. That’s not to mention how long it takes to get those results in some instances. To combat these issues, many organizations are turning to big data machine learning techniques, with promising outcomes hinting at their potential.
Machine learning isn’t exactly a new idea. The concept has been discussed and experimented with for decades, but only once big data truly took off did machine learning gain newfound attention. Many people tend to think of machine learning as artificial intelligence, but that would be an inaccurate description. The focus of machine learning tends toward the development of algorithms in order to process large amounts of data in real-time. The aim is to use machine learning to make predictions based on trends and properties that have already been identified. Machine learning algorithms create these predictive models at a blistering pace, and they do it without explicit programming telling them to do so.
It’s an automated process, one that would be next to impossible if humans were to try it all on their own. Considering the combination of computer programming and statistics involved, no human programmer could create predictive models at the same rate. As a result, the potential of big data can finally be realized. As referenced above, big data involves enormous sets of data gathered from a variety of sources. Analyzing big data with traditional techniques can only go so far, but with machine learning, predictive information can now be delivered with more accuracy much more quickly. Data is all around us, and the efforts to collect as much data as possible are ongoing. Only with machine learning can these amounts be thoroughly analyzed. Since there’s no need for human intervention in this process, more complex sets of data are now open for big data analysis.
In this way, machine learning offers the accuracy, scale, and speed needed to fully analyze the data that organizations can now collect. Since a larger variety of big data can be analyzed, the limits many companies ran into have effectively been torn down. At a time when real-time analysis is needed to take full advantage of information from different sources, machine learning becomes an indispensable tool that businesses cannot overlook. In many ways, machine learning has already been integrated into many aspects of our lives, often without us realizing it. Online recommendations (like those seen when perusing Amazon or Netflix) are a product of machine learning algorithms. Real-time ads found on websites and mobile apps come from data analyzed from numerous sources with machine learning. Even spam email filters are a form of machine learning as it takes established patterns and utilize new data to adapt their algorithms to predict what constitutes spam in the future.
And speaking of the future, big data machine learning will likely play an even more integral role in new technologies. Self-driving cars, for example, use machine learning to help navigate on the road and steer away from dangers and other drivers. And the Internet of Things (IoT) will need to use machine learning to deal with the incredible amount of information that will need to be analyzed to make the IoT function smoothly. Big data has accomplished much already, but machine learning’s role will be one of unlocking its full abilities. We’ve only just scratched the surface of what big data has to offer. Machine learning will allow us to dig deeper than ever before.