Like any new, cutting-edge technology, there is plenty of talk about the future impact of big data on companies big and small. As the world around us becomes even more "connected," all those data sources -- from smartphones to the Internet, and even our homes, cars, and cities via the Internet of Things (IoT) -- are doing more than making our lives easier: they're amassing unprecedented amounts of data.
As business leaders are well aware, sound decisions are made based on information -- which is where big data enters the picture. Collecting, analyzing and ultimately utilizing the vast amounts of information available -- and we're still in the early stages of big data -- is obviously essential.
According to a recent study from Gartner (IT -1.70%), business execs recognize that big data is about to become a disruptive technological force, which is why IBM (IBM -0.97%), Microsoft (MSFT -1.95%), and others are ready and waiting.
Ready or not, here it comes
The seemingly unending sources of information, and the analytical tools used to derive actionable results from all that data, may seem mysterious to non-techy types, but businesses are preparing to go all in. In the next two years alone, according to Gartner, 75% of the 437 global execs and information technology (IT) gurus interviewed said they'll invest in big data-related solutions.
As a research director at Gartner said, "This year begins the shift of big data away from a topic unto itself, and toward standard practices." Another indication that big data is about to go mainstream is that it's not just the IT folks who see the value and necessity of implementing analytics solutions: entire leadership teams are becoming more involved as organizations realize their potential.
The good news for providers like IBM that have already committed to big data -- as evidenced by the company's $1 billion investment to fund a new division featuring its cognitive computing wonder Watson -- is that Gartner's research spanned multiple industries. In other words, big data isn't just for tech and analytic solution providers; virtually all companies can benefit, regardless of industry.
Such a large market helps explain why conservative estimates suggest that big data will become a nearly $100 billion industry in just 10 years, reaching over $33 billion this year alone.
Too much of a good thing
As per Forrester Research, the biggest challenge for both large and small companies related to big data is too much information. Though nearly 90% of business folk believe big data will revolutionize operations just as the Internet has, the sheer volume of information is already overwhelming -- which is perfect for IBM, and to a lesser extent Microsoft.
IBM's early entry into the burgeoning big data market extends beyond its initial investment in its new Watson division. Since then, IBM has expanded its big data offerings via multiple acquisitions, including its most recent $1 billion deal for Merge Healthcare. IBM has also expanded its big data reach by splitting off a separate unit called Watson Health, and it's already inked some major deals.
Unfortunately, IBM wouldn't disclose revenue specifics. But IBM's business analytics unit, home of Watson and big data, jumped again last quarter, growing over 20% after factoring in the impact of currency fluctuations. Though not as far along as IBM, Microsoft's industry-leading Azure cloud platform -- where much of the big data solutions are expected to reside -- is an ideal starting point for the company's own solutions.
Like IBM's Watson, Microsoft has developed its own machine-learning, big data analytics solution. However, unlike Microsoft's cloud unit, which announced a more than $8 billion annual run-rate last quarter, its big data efforts are still in their infancy, while IBM is announcing a new big data client seemingly daily.
It's becoming apparent that big data isn't some futuristic concept -- it's here and it's growing fast. For investors who recognize its potential, IBM and its 3.6% dividend yield are a great opportunity to get onboard the big data train.