To take full advantages of cloud computing for big data analytics, the data that goes into the cloud must be rich and extensible - imbued with meaningful data from network traffic, scalable to handle billions of devices and sessions, and available in real-time. And last but not least important, this "smart data" must be cost effective to collect and create.
There is no doubt that big data analytics is fast becoming integral to business intelligence. Besides many initial failed projects, primarily due to the massive infrastructure needed to store, process and analyze big data in-house, there is an increasing number of success stories. This gives pause to completely discount the paradigm.
Moving big data analytics to the cloud seems to accompany these successes. It is impossible to ignore the competitive edge gained by organizations leveraging big data analysis. From real-time data analytics facilitating industrial processes to financial trading algorithms, big data is a definitive part of the corporate future.
What needs to be considered is why big data works so much better in the cloud than stand-alone systems. Here are some reasons why cloud-based big data solutions have reached a tipping point in terms of wide-scale industry acceptance.
Big data is a big undertaking
Big data is for all intents and purposes, big. In-house implementations of such projects involve some serious infrastructure, processing power and, above all, time. Many of these projects fail because the skills aren’t available to roll out the complexity and groundwork required to bring these developments to fruition.
The cloud moderates this situation in many ways. Assuming responsibility for platform development and maintenance, cloudware providers consequently take the weight of the shoulders of IT departments from this perspective. The required hardware arrangements are minimal, and in most cases, a steady internet connection and PC will suffice, as opposed to high-end servers and processors. This essentially allows organizations of all sizes to reap the rewards of big data capabilities.
Data scientists are at the forefront of big data analytics
Failures didn’t result just from lack of architecture but the talent behind the executions, especially regarding analytics at that scale. Initially, it was deemed ideal to keep data scientists and operations as separate entities, but it has since been revealed that these critical personnel are at an advantage working in conjunction with the operational side. This results in a fundamentally better model and more efficient processes, as the people handling the data are involved in the inception of the systems. The consequence is an expedited ecosystem that will effectually reduce the time taken to glean insights from the surfeit of data.
Why the cloud is a watershed for big data
Most modern organizations have come to the realization that there are distinct benefits to outsourcing big data requirements to the cloud. One such benefit is that the cloud is fast becoming the number one storage facility for generated data, thus it makes sense to farm out the analytics to the same platform. Besides the cost-saving implications of cloud storage, it relieves companies of the need for the specialized hardware to accomplish the same feats in house.
The complexities of big data platform construction are highly elaborate and CIOs are slowly but surely leveraging the cloud to underpin the required infrastructure and leave the data scientists free to focus on high-end analytical issues rather than developmental teething problems.
The cloud may prove big data’s redemption
The aim of big data analytics is to provide a framework wherein raw data can be converted into usable and actionable conclusions in a timeous, often instantaneous, manner. Past failures have dimmed the light for organizations on big data, but in view of cloud-based applications, it is pertinent for CIOs to reconsider big data options. Outsourcing back-end development to a third-party provider, the business can actually concentrate on what matters.
The establishment of the cloud as an alternative to in-house analytical systems has effectively become the tipping point for businesses to commit to a big data solution without the risk or outlay previously required and perhaps what companies have been waiting for.
To maximize the value of Big Data Analytics in the cloud, service providers must have access to cost effective "smart data," derived from network "wire" traffic. Otherwise, data lakes will be filled with data, that is costly, difficult to use, and doesn't address business questions. As the old computing adage goes, "garbage in, garbage out!" ~ John English, Sr. Solutions Marketing Manager, NETSCOUT