Over the first half of this decade, big data grew massively in business. During the second half of the decade, we’re seeing a global migration of big data to the cloud. Here is why that transition is happening.

  • The Early Days of Big Data
  • Why are cloud data analytics now pivotal?
  • #1 Argument – More open-source-friendly
  • #2 Argument – Strong focus on ease-of-use
  • #3 Argument – Collaborative approach to analytic challenges
  • #4 Argument – Cloud a good place to assess cloud
  • #5 Argument – Most reasonable location for data pipeline
  • Onward & Upward

The Early Days of Big Data

Every company likes the basic idea of using big data to their advantage. That’s why there has been such a surge to adopt tools to do so. Look back a half-decade ago, and we see enterprises from retail to insurance to telecom all taking forward steps with these initiatives. Those were essentially the early days on the big data frontier. The experiences of those firms, and the developing services of their providers, bolstered the understanding of big data and how companies can work through obstacles.

Fast-forward to 2016, and we are of course much further along in the tools that are available and the extent to which companies have embraced them. However, companies have often used older applications and clunky legacy architecture to process their data and look for insights – which tends to be both complicated and expensive.

Cloud server hosting has been fundamental in allowing companies to implement big data systems and utilize them. That’s just a fact, backed up by the numbers. Tech research firm IDC predicted in 2015 that cloud analytics and big data would expand at a 200% faster rate than on-site options.

Why are Cloud Data Analytics Now Pivotal?

#1 Argument – More open-source-friendly

Cloud unleashes the power of open source programs, notes Dave Wang in InsideBigData; because deploying them traditionally can be expensive and slow, and it can require substantial support.

“The cloud will change this paradigm, allowing organizations to immediately harness the power of open source software without upfront investment, and pay only for resources consumed,” he explains. “This will lower the barrier to initiate data analytics projects, allow organizations to run more experiments, and ultimately yield more insights from data.”

#2 Argument – Strong focus on ease-of-use

On-site applications can be challenging for people to learn and put into action. Support can be expensive, and you often have to look multiple places to cobble together solid documentation, among other issues.

Cloud systems have been built in the era during which the notion of user experience came into much sharper focus. User interfaces are based in the browser, notes Wang, and documentation is often linked dynamically to the most up-to-date version. Other developments include “innovative new channels of customer support – such as allowing support engineers to troubleshoot a customer problem remotely,” he adds. Plus, “cloud-based tools have short release cycles in the order of weeks instead of months, which can further support changing customer needs.”

#3 Argument – Collaborative approach to analytic challenges

If there is one thing that’s obvious about analytics, it’s that it is not always straightforward. You need to look at problems you want to solve from technical, mathematical, and domain perspectives. An example would be creating a system to monitor and prevent fraud. Someone who focuses on the domain is the best person to find unusual activity. A mathematician can create a model that can help easily locate that same type of activity. Finally, a developer is able to build the algorithm into the code so that you can actually turn your insights into action. In other words, you need a team.

Cloud is built for optimal interaction by a variety of users. You can employ your analytic tools in conjunction with data visualization and collaborative notebooks to foster a meeting of minds between specialists in diverse areas and help a project succeed.

#4 Argument – Cloud a good place to assess cloud

In 2016, programs are often developed in the cloud, of course. It’s important for businesses to be able to access and work with that data as seamlessly as possible.

By using cloud hosting for your analytic platform, you have a solution that is built ready to integrate with other cloud components. For example, CRM and marketing automation are often hooked together. Basically you have an HQ for data analysis.

#5 Argument – Most reasonable location for data pipeline

You want to have a complete data pipeline at your fingertips if you want to really leverage big data. That means extracting the data, manipulating it, analyzing it, and then using what you find to drive additional algorithms, improve products, and refine customer relationships. That’s much easier in the cloud, comments Wang. “Doing so is exceedingly difficult on-premise because of the number of disparate capabilities one must integrate,” he says. “A cloud-based platform allows an organization to connect to a wide variety of data sources, gain better productivity with user-friendly tools, collaborate more effectively, and serve data products to a broad audience.”

Onward & Upward

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