Big data analytics has been around for a while and its worth has widely been accepted. Yet, companies still struggle with maintaining their data, storing it, and then actually using it. When large amounts of data are being processed at high speeds, companies need a solution to help leverage their data. One solution is cloud computing, which gives organizations the ability to consolidate data across all of their channels and sources at a grand scale.
So what exactly in cloud computing? Cloud Computing is the practice of accessing remote servers on the internet for managing, storing, and processing data rather than on a local server or data center. This takes away the need for a local server room/ data center, a larger IT team, and all of the headaches of ongoing updates and maintenance. With an abundance of data, many enterprise-level companies have shifted their focus towards cloud computing to tackle their massive storage and processing needs.
Take, for example, GE, who committed $1 billion to put sensors on gas turbines, jet engines, and other machines to connect them to the cloud and analyze the resulting flow of data to identify ways to improve machine productivity and reliability1. To put in perspective about how much data they have flowing through the cloud - each engine can generate 1TB of data from a single flight.
But is cloud computing right for your business? We took a look at the following advantages that could computing brings to the analytics world:
Robust Data Foundation
Analytics data can’t paint a truly cohesive and accurate picture if it’s scattered across multiple local premises, especially if third party data involving subscriptions from users is being collected. Having a centralized repository of data helps overcome this obstacle to merge, collect, and analyze data in an efficient way.
Scalability and Elasticity
Analytics constantly asks for assessing more data, and at the same time, acquiring more processing power to analyze such huge data-sets. Cloud computing provides great options to scale your systems virtually based on the processing power required.
Previously, companies used to spend large amounts of money to buy on-premise hardware to fulfill their needs of storing and analyzing expansive data sets. With could computing, companies can target customized virtual servers to target specific processing and storage requirements.
Speed and Agility
It takes weeks to setup the entire hardware infrastructure on-premise and complete the installation. With cloud computing, hundreds of servers are available within minutes, increasing the speed and efficiency of setting up virtual networks.
Taking it to the cloud.
A longtime, global client of ours follows the cloud computing model religiously. They laid the foundation for cloud computing in 2010, having to provide services to more than 600,000 customers across hundreds of global locations, they needed fast, agile, and steadfast data processing and storage to ensure proper service. To achieve this, they follow software-as-a-service (SaaS), infrastructure-as-a-service (IaaS), and also a hybrid cloud implementation model with public could services from Amazon. Amazon Web Services, the largest cloud service provider in the world, leads the pack with 3 other U.S. providers (read about the top four providers here).
By utilizing this cloud computing infrastructure, this leading hospitality provider is able to bring the reservation system more closely aligned and located near the geographic centers from where the visitor is accessing their webpages to make the site more responsive. This also helps them with carrying out their software development and testing needs.
If all of this sounds like a jumble of technical jargon that only IT personnel would understand, I feel your pain. Here is a quick breakdown of the cloud computing models:
Infrastructure as a Service (IaaS)
Provides access to networking features, virtual hardware and data storage space. It provides the highest level of flexibility to the user to control your IT resources.
Platform as a Service (PaaS)
Platforms as a service remove the need for organizations to manage the underlying infrastructure (usually hardware and operating systems) and allow you to focus on the deployment and management of your applications.
Software as a Service (SaaS)
With SaaS you do not need to worry about managing your servers or handling the infrastructure. It is an end user application that only relies on the usage of the customers, an example would be web-based email where emails can be sent and received without worrying about handling network infrastructure or managing operating systems.
Cloud computing isn’t for every business. But if you have massive amounts of data that takes up time, manpower, and physical storage space to maintain, or even worse, if you have massive amounts of data you’re not using (gasp!) - it might be time to think about cloud computing to help elevate your analytics.
Have you moved your analytics data to the cloud? Comment below and tell us your story!
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