How Machine Learning Is Changing Big Data Management

A quick review of the evolution of big data management shows how machine learning has already driven serious change within the field.

October 27, 2017
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Few things have fundamentally reshaped how companies overcome business challenges than the application of machine learning in the market. Today’s companies, from the tech behemoths of Silicon Valley to the eager entrepreneurs cropping up in cities nation-wide, all exploit machine learning to cut cost and get better results. This widespread adoption of machine learning has consequences; big data isn’t an easy beast to tame, and companies today are facing serious challenges when it comes to keeping their data management systems up to date with rapidly evolving algorithms.

So how exactly is machine learning fueling a revolution in big data management, and what are today’s wisest companies doing to find solution to their big data problems? A quick review of the evolution of big data management shows how machine learning has already driven serious change within the field, and how that change is just getting started.

Finding the signal in the noise

If there’s one universal truth about today’s market, it’s that big data is virtually ubiquitous. Companies of all shapes and sizes rely on data to predict consumer behavior patterns, better market their products, predict market trends and cut down on cost. Making use of countless reams of data is easier said than done, however, and many businesses are finding it challenging to keep up with data management’s dizzying pace.

When it comes to deciphering mountains of vague data to find the useful tidbits that have business applications, or deciphering the signal from the noise, as its often called, companies have more problems than ever. Data mining, as the process is usually titled, is growing complicated precisely because there’s such a torrential flood of information out there, to the point where it can be hard to determine what’s actually an underlying trend and what’s merely a coincidence.

When it comes to dealing with this problem, today’s top firms are increasingly turning to automation. While it’s not pleasant to admit, the truth is that human employees are simply incapable of sifting through towers of information to find the one or two pages of data relevant to their businesses. Rather than wasting their human employee’s valuable time, companies are instead turning to algorithms to sort through that information more efficiently to gleam what valuable insights they can.

Determining what techniques or algorithms to apply isn’t always easy, but it’s much better than choosing a human alternative. The subsequently growing demand for this type of machine-learning approach to business has itself driven demands for new technologies to better facilitate that approach. Higher standards are being adopted for big data analytics tools, and more investors are realizing that data storage is vital if such huge amounts of information are to be successfully used.

Building better data management systems

As big data management grows to assume a vital role in today’s marketplace, it’s only natural that we see a corresponding growth of big data management studies and programs, too. Whether it’s preparing for an impending regulatory crackdown by governments or by self-regulating by adopting market-based solutions, more big data management programs are springing up seemingly by the day.

Lovers of machine learning and business enthusiast hoping to cash in on big data analytics should rejoice at this news. A skilled workforce of human employees and a highly-technical slew of algorithms and other tech-based tools for them to use, is crucial for any company’s success in the 21st century, and data is only going to grow to become more important. Given that global internet traffic surpassed one zettabyte in 2016, it’s wise to assume that data demands will keep growing. So where should companies turn to for big data management solutions?

Companies should be prepared to create lucrative partnerships with data storage vendors. Large corporations or businesses which use massive amounts of data in particular should consider creating their own data storage operations; it won’t be cheap in the short run to create a data storage or data analytics facility, but in the long term it will undeniably prove a huge boon to today’s top firms. As the IoT continues to grow at a staggering pace and more digitally-connected devices proliferate, current data woes will only exacerbate without a matching increase in big data funding.

The big data revolution isn’t on its way; it’s already arrived. As machine learning continues to develop at a breakneck pace, we’ll only see further innovations and investment in the field of big data management, and with good reason. Already, today’s leading firms have invested huge sums in their IT departments to prepare for that future demand. Rather than being left behind in the dust, smart companies with tech-savvy executives realize that, in the future, adopting big data management solutions will be necessary for business success.