Why it’s not too late to get started with Big Data

We’ve heard a lot about Big Data over the past several years. It’s been one of the biggest topics in IT and practically every enterprise has been clamoring to get onboard the Big Data train. Now that some of the hype has died down and been replaced by the Internet of Things, organizations are stepping back to re-evaluate their processes of capturing and analyzing Big Data. Reaching an impasse, many companies have begun to realize the only way they’re ever going to make any sense of the enormity of the data out there, is through better tools, processes, and smarter strategies for capturing and analyzing in real-time all that’s available.

 

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With Internet of Things and wearables now exploding in the marketplace there is even more urgency for organizations to figure out how to capture all the information generated by these devices. This has given many organizations cause to pause, take stock, and reassess their goals for capturing and analyzing Big Data in real-time. This has meant as well that there are plenty of new opportunities for newbies to get onboard with the latest trends.

No one has cornered the market in the Big Data space and in fact it’s a constantly moving target. In many ways we’re at a new staging ground in the Big Data life-cycle as more and more organizations who have experimented with historical data are admitting their need for deeper insights. If you’re in this camp then it’s probably a good idea to stop and take an inventory of your processes and decide what has worked and what hasn’t, before pushing on. For other organizations that have somehow missed the Big Data train, you’ll have some catching up to do but it’s never too late to get started. There are plenty of new and exciting technologies that will keep everyone – newbies and more experienced firms alike – extra busy in the years ahead. Let’s map out some of the major trends happening in the Big Data space, which indicate why it’s not too late to get onboard this train.

1. Machine learning is big and getting bigger

Big Data has become really good at analyzing historical data, but the next big shift in these technologies is with applying machine learning algorithms to “infer” or predict new types of knowledge. The synergies and connections are nicely spelled out by one blogger as follows:

Think of big data and machine learning as three steps (and phases of companies that have come out of this space): collect, analyze, and predict. These steps have been disconnected until now, because we’ve been building the ecosystem from the bottom up — experimenting with various architectural and tool choices — and building a set of practices around that.

Businesses that can crack the predictive nut from the masses of data available today will have a veritable cash machine at their disposal to drive new types of customer value and service for their verticals.

 

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2. Big Data in the Cloud will increase scalability

The growth in Big Data, as well as the expansion in data analytics platforms in recent years such as Hadoop and NoSQL, are creating new opportunities for cloud computing to become a key enabler of Big Data analytics. Public clouds providers, such as Amazon Web Services, Google, and Microsoft, offer their own brands of big data systems in their clouds, whether NoSQL or SQL, that are cost efficient and easily scalable for businesses of all sizes. All of this points us to the reciprocal relationship between cloud and Big Data that is driven by consumer demand for bigger, better, and faster applications. In fact, Big Data + Cloud has led to another new cloud computing service model known as Analytics as a Service (AaaS). This model will provide companies with faster, scalable ways to integrate, analyze, transform, and visualize various types of structured, semi-structured, and unstructured data in real time.

3. Big Data-as-a-Service will open up new business opportunities

As Big Data meets Internet of Things it will pose a whole new set of business challenges, and opportunities. Raj Badarinath, senior director of product marketing at commerce solutions provider Avangate, has this to say: “In the IoT era, new models such as subscriptions, freemiums and bundles are rapidly becoming the preferred choice over traditional hardware options. Services are easily upgradeable, much more amenable to ecosystems that are constructed around hardware, and provide multiple revenue opportunities rather than a one-time sale.” Big data will become critical to this new business model, and this will mean gaining a 360 degree view of the customer in ways that will keep them happy and engaged. These services will also provide a framework for making data more intelligible, accessible, and actionable from a business standpoint.

 

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4. Apple Watch will transform the Big Data landscape

Apple has just raised the bar on wearable technology a notch with the pending release of its new Apple Watch, which doubles as a health and digital fitness tracking device. Wearables gathering these kinds of metrics are just some of the innovations that will bring Big Data into an epic new era. Here, as never before, businesses also will have incredible opportunities to capture additional stores of information about who we are, what we do in our free time, our favorite books, and many, many other life preferences. Consumers, developers, and businesses are lining up. Make sure your organization is ready to capitalize on the massive amounts of data that will emerge out of the Apple Watch era.

 

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