Big Data Architecture: On the Ground or in the Clouds?
As Big Data and predictive analytic strategies move into the business world's radar, decision-makers will be charged with determining whether they want to develop a customized architecture or deploy a system that has already been built. In many cases, organizations are choosing to create and manage these solutions in-house, although executives should first consider several factors before making a final decision.
Organizations need to think about how they intend to collect data and the amount of information that will accumulate over time, according to a Forbes report. Businesses now have the opportunity to collect information from smartphone, Internet, and other web-based activities, but decision-makers must assess which data will be useful and which data should be ignored. Experts often call the digital trail left in the wake of consumer activity "data exhaust," and companies should be sure they only collect the information they can actually use.
The truth is that in-house environments only have a finite amount of space that businesses can allocate toward Big Data projects. This limitation has led many decision-makers to turn to cloud computing and other outsourcing strategies that enable internal teams to acquire the storage capacity needed to maintain the massive volumes of information that are being collected, Forbes noted, while realizing cost and resources savings in the process.
At the same time, decision-makers must be aware that capturing, managing, analyzing, and ultimately using data all require different processes. This understanding will help companies determine whether customized in-house architectures are as efficient as landscapes built off-site or in the cloud.
Are companies using the cloud?
In many cases, cloud computing technologies are relatively new to organizations, just like the concept of Big Data itself. As a result, decision-makers may wonder if combining two relatively unknown strategies will provide any measurable benefits or if doing so will only introduce more significant challenges. Nevertheless, many firms are moving forward with projects that encompass this convergence.
A Gigaspaces Technologies survey found that 43 percent of companies believe their Big Data processing strategies are mission-critical, and another 37 percent said these initiatives are at least somewhat important. So it's not surprising that approximately 44 percent of businesses said they are using the cloud to augment their Big Data storage and analytics endeavors.
Rather than relying on internal, inexperienced or overworked teams to build large, complex Big Data architectures, decision-makers should consider collaborating with proven service providers that can give organizations access to more sophisticated business intelligence tools, storage capacity, and cloud/infrastructure expertise. In doing so, companies will likely experience greater success with their analytic strategies.
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