Have you ever wondered how we get recommendations from amazon?? How youtube recommend videos?? The answer is a predictive analysis that can be performed on big data from the company’s respective cloud.
The practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer and avails of using the cloud for manipulating big data for the predictive analysis. The advancement of technology has allowed companies to reap the benefits of streamlined processes and cost-efficient operations. But the one thing that has become a game-changer for businesses of all sizes is the availability of data from every source imaginable – social media, sensors, business applications, and many more. These large stores of data that bombard companies’ day in and day out are collectively known as big data. Most have heard of it, many aim to maximize its potential to propel their business forward, and yet, only a few have truly succeeded in doing so. At the same time, it will be very helpful for enterprises to adopt cloud computing to improve their IT operations and develop better software, faster. Merging big data with cloud computing is a powerful combination that can transform your organizations. In general, 51% of industrial companies use cloud storage. But Traditional database systems are based on structured data i.e. traditional data is stored in fixed format or fields during a file. Big data uses semi-structured and unstructured data and improves the variability of the info gathered from different sources like customers, audiences, or subscribers.
What is big data?
Gartner defines big data as high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative sorts of information science that enable enhanced insight, deciding, and process automation. To get a better idea of how big data is, let’s review some statistics:
- Over 1 billion Google searches are made and 294 billion emails are sent every day
- Every minute, 65,972 Instagram photos are posted, 448,800 tweets are composed, and 500 hours worth of YouTube videos are uploaded.
- By 2020, the number of smartphone users could reach 6.1 billion. And taking the Internet of Things (IoT) into account, there could be 26 billion connected devices by then.
Why does big data in the cloud make perfect sense?
The benefits of moving to the cloud are well documented. But these benefits combat a much bigger role once we talk about big data analytics. Big data involves manipulating petabytes (and perhaps soon, exabytes and zettabytes) of data, and the cloud’s scalable environment makes it possible to deploy data-intensive applications that power business analytics. The cloud also simplifies connectivity and collaboration within an organization, which gives more employees access to relevant analytics and streamlines data sharing. While it’s easy for IT leaders to recognize the advantages of putting big data in the cloud, it may not be as simple to get C-suite executives and other primary stakeholders on board. Data is also key for the CMO looking to increase customer engagement and loyalty, and for the CFO seeking new opportunities for cost reduction, revenue growth, and strategic investments. A Forrester Research survey in 2017 revealed that big data solutions via cloud subscriptions will increase about 7.5 times faster than on-premise options.
Predictive analysis – The perfect use case for cloud computing:
Cloud computing provides the processing and large data support needed for predictive analytics. Predictive analytics – matching current datasets against historical patterns to work out the probability of an occasion occurring within the future – requires tons of compute power and draws on a lot of data. In other words, an ideal use case for the cloud. James Taylor, an automated decision management proponent and author/co-author of two books on the topic, says cloud computing is elevating the art and science of predictive analytics to a whole new level. No longer do such efforts need to be constrained by companies’ current server and storage capacity — with online, sharable resources, the sky’s the limit. Based on a survey Taylor conducted at the top of last year among 200 business intelligence professionals, 43% have already developed predictive analytics solutions within their companies, and 82% have predictive analytics in their plans going forward.
“Separately, predictive analytics and cloud solutions are changing the way organizations do business.
Together, they open up a wealth of opportunities.”
Other industry research confirms the growing level of interest in moving business intelligence to the cloud. Another survey of 1,364 IT managers by Gartner, for example, finds that almost a third (27%) already use or plan to use cloud/SaaS options to augment their BI capabilities for specific lines of business or subject areas within the next 12 months.
While predictive analytics features a range of applications, from fraud detection to production system management, Taylor’s survey identifies the “sweet spot” for cloud-based predictive analytics because of the effective acquisition, management, and retention of consumers. the highest two areas for predictive analytics projects are marketing/customer acquisition (with 61% implementing or having specific plans to implement) and customer retention (50%). Other areas that scored well included the broader category of customer management (48%), sales, and cross-sell/up-sell (46%). an identical question focused on cloud adoption showed campaign management (60%), and CRM scoring highest (59%). Time to value, pervasiveness, agility, scalability, and data access are the key pros of cloud-based predictive analytics. Organizations don’t want to attend more than a couple of months before seeing a positive outcome, he explains.
Access to big data is another key advantage cloud computing offers. “Many new big data sources are only available within the cloud,” Taylor says. Plus, cloud means organizations are going to be less restricted by data transmission rates. “All this increases the worth of moving analytic modeling to the cloud where they are often near this new source of knowledge.
Amazon predictive analytics are often used to reverse-engineer past customers’ experiences and actions that resulted in a positive outcome. This will be beneficial for both the customer and also the company.
When the treasury team at Microsoft wanted to streamline the gathering process for revenue transactions, Core Services Engineering (formerly Microsoft IT) created an answer built on Microsoft Azure Machine Learning to predict late payments
Azure Machine Learning may be a cloud-based service that detects patterns in processing large amounts of knowledge, to predict what is going to happen once you process new data. In other words, it helps us do predictive analytics.