As a new year kicks off, the words “big data” continue to be spoken in multiple industries all over the world. 2015 was a tremendous year for big data analytics as more businesses and organizations than ever before adopted data science in order to grasp some of the benefits it provides. In much the same way, 2016 look to continue that trend, with more in-roads being made for big data in areas such as finances, health care, manufacturing, and even sports organizations. At the same time, machine learning is quickly catching on. Both big data and machine learning are often mentioned in the same breath, yet the two are not synonymous. Machine learning can be performed without the massive and complex data sets people often think of when referring to big data. Likewise, big data analytics doesn’t necessarily involve machine learning techniques. While the difference should be noted, if the goal of an organization is to extract useful and actionable information from the data they collect, machine learning will likely be in the cards. And if they want to use machine learning to the fullest extent, even taking it to an exceptional level, big data will be needed.
The details behind how machine learning works usually require a mind best suited for data science. Put in simple terms, machine learning involves processing data for the purposes of learning a specific task. How the solution is reached is mainly based on the algorithms followed, but it’s particularly important to note that no actual programming is involved in the process. By following the algorithms, the computer basically finds the best way to reach a desired outcome. That means that two different machines may actually end up finding different answers to the same problem. This of course could lead to some exciting possibilities and interesting applications, some of which are already being employed by businesses. Take Google for instance and their Google Translate function. Through massive data sets collected from input by users, machine learning algorithms find the best ways to translate from one language to another. Perhaps a gifted computer programmer could have created code for that purpose, but the code would have been extremely long, incredibly complicated, and likely filled with errors. Machine learning simply works best in this situation, and that concept is quickly spread to other functions that businesses and organizations encounter.
There’s good reason that machine learning has gained so much interest in such a short period of time: it works. That’s actually putting it lightly. Machine learning works, in the words of Quanta Magazine’s Ingrid Daubechies, “spectacularly well.” Classic and traditional techniques can certainly get the job done, but machine learning is well the results are fascinatingly effective. This effectiveness even extends to the likes of deep learning and neural networks, what many experts are predicting are the precursors to true artificial intelligence. In fact, the incredible results shown from machine learning have even baffled mathematicians in terms of reaching an adequate explanation. The effectiveness of the algorithms used definitely have a part to play in machine learning’s reputation, but some of the results can be credited to the use and intersection of big data analytics. If anything, it’s big data that turns machine learning into something really revolutionary.
There are, of course, different ways to employ machine learning. Techniques can generally be split into two types: supervised and unsupervised learning. Without going into too much detail, supervised learning involves turning large sets of inputs into a corresponding output, while unsupervised learning looks at those large data sets to identify any connections that might be hidden or at the least difficult to find compared to traditional techniques. It is through this process that machine learning can offer up previously hidden insights and spot particular trends that might have gone unnoticed before. Long gone are the days of trial-and-error analysis, which would simply be impractical in the era of big data science.
Much of the emphasis placed on machine learning involves how well it works with big data. When machine learning is used for predictive analytics, for example, much of the advantage lies in its ability to process the data in real time. While static analysis of data sets can certainly yield valuable insights, the most value can be derived from getting data that is as up to date as possible. By taking in data this way, you can get the more reliable and accurate results that are often wanted. And as many people within the business world know, getting accurate predictions can mean the difference between a successful company and a failed one.
Big data on its own can be highly complex and involved. Analytics tools and big data solutions have been created to help with this high degree of complexity, but they may still fall short in some area. Machine learning can essentially fill that gap, in part because it is designed with the complexity of big data in mind. Even more important is how it handles the various data sources that big data can come from. Even in a typical business, data can come from places like customer input, surveys, website transactions, transportation logistics, payroll and taxes, and even energy bills. With so many possibilities, adopting a machine learning solution can ensure that mundane tasks don’t have to be done by data scientists. Instead, solutions are found that simplify the problem and a data scientist’s time can best be spent finding more interesting insights that can affect business in more groundbreaking ways.
So it’s clear that big data plays a pivotal role in ensuring machine learning reaches higher degrees of effectiveness. One may reasonably ask, if some big data is good for machine learning, then a lot of big data could be even better. After all, if machine learning relies on big data as a type of fuel to reach maximum potency, having more of it on hand can only mean better results will be obtained. Such thinking isn’t necessarily flawed, but it doesn’t take into account the full picture of how machine learning works. It’s even been a subject of a lot of debate among data experts. First, while not always true, in certain situations more data does mean more exceptional machine learning. It’s actually pretty simple in why that is the case. With more information at its disposal, machine learning can come up with more accurate results. Greater accuracy is obviously a major goal for any organization, so collecting as much data as possible is usually a wise strategy. Second, the effectiveness of the data within a machine learning algorithm is largely dependent on what type of model is being used. In some scenarios, more data won’t actually lead to more accuracy and could even complicate issues further.
In cases where a model is high variance, adding in more data points can actually improve it significantly. If such a model is encountered, the idea that more big data means better machine learning would be an accurate one. However, not every model follows the high variance type. Some models are referred to as high bias, which is a model that appears to be too simple when looking at the data. Adding more data to the model won’t make it any more accurate and could just muddy up the waters, so to speak.
While it should be noted that more data doesn’t always lead to improved machine learning, one thing that can’t be overlooked is the importance of the quality of the data. Having better data on hand can in many circumstances be even more crucial to positive results than having a larger amount of data. This usually requires cleaning the data that has been collected, ensuring that each data point is needed for the task at hand. With the right sampling available, having high quality data is the result, making machine learning that much more effective.
The machine learning phenomenon will likely not be dying out any time soon. As businesses get used to the idea, they’re only likely to keep using it even more. That, combined with the push to adopt big data analytics solutions, means people will be looking to make machine learning a permanent part of their organizations. As businesses embrace new technologies, from software defined storage to converged infrastructure, they’ll find ways to put machine learning to good use. However they decide to do that, big data will need to be part of the equation. Much can be accomplished by mixing big data in with machine learning. It all boils down to making sure machine learning reaches its full potential, and without big data, that will likely never happen.