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The New Efficiency of Cloud Analytics

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More businesses are turning to cloud analytics - analytics performed in the cloud, without the need for on-site equipment or software.

Cloud analytics have been gaining attention as a superior alternative to on-premise analytics solutions. On-premise analytics solutions have a number of disadvantages. For example, such solutions are expensive to procure and maintain, have scalability issues and it can be difficult to achieve collaboration among multiple teams who have stakes in the information such systems produce. Cloud analytics, on the other hand, is inexpensive and scalable, offers a variety of services from the cloud, is offered on a subscription model and fosters superior decision making. In fact, there are case studies to prove that cloud analytics have brought several business benefits. However, according to Gartner, to get the best out of cloud analytics, businesses need to plan well. Hasty adoption can equal problems.

What Is Cloud Analytics?

Cloud analytics is primarily a set of solutions, hosted in the cloud, that enable businesses to perform business intelligence (BI) tasks. According to Gartner, the set of solutions contains data sources, data models, computing power, analytic models, processing applications and sharing or storage of results. The solutions are provided through private or public cloud under a subscription-based or utility (pay-per-use) pricing model. Subscribers can opt for one or more services. (To learn about analytics in business, see The 4 Key Benefits of Business Analytics.)

Examples of cloud analytics could be software-as-a-service business intelligence (SaaS BI), hosted data warehouses and cloud-based social media analytics. For example, a company could subscribe to data warehousing and BI solutions hosted in the cloud. The data warehouse hosts huge volumes of data gathered from different sources, whereas the BI tool could provide services such as charting, categorization, dashboards and pivot tables after querying the data hosted in the data warehouse. The subscriber could scale up or down the services depending on the requirements.

Reasons to Use Cloud Analytics

The business environment is always changing and businesses need to constantly adapt. Cloud analytics, as statistics will corroborate, enables businesses to adapt to changing situations. The example of D&B, one of the biggest commercial information providers, shows how companies are embracing cloud analytics to adapt. D&B has acquired a small analytics startup, Indicee. Indicee offers an analytics system on top of Salesforce’s Chatter ESN product. Obviously, D&B wants to provide analytics on the commercial information to its clientele because information is now easily available, even for free. However, the question could be: Why did D&B choose a cloud analytics system over an on-premise analytics system? Cloud analytics offers a number of advantages over traditional BI systems which are discussed below.

Integrated View of the Business

Cloud analytics offer integrated views of all relevant data which is crucial in understanding business problems and making insightful decisions. Decision makers have a clearer, more comprehensive view of a situation that helps enable better decision-making. Let us better understand this benefit with the example of the sports industry. Leagues such as the NFL, NBA and NHL use cloud analytics to gain superior insights about player performances, ticket sales, audience interest, ground capacity and numerous other indices. These sports leagues use cloud analytics to integrate all big data and based on the insights from the analytics, develop strategies on ticket sales and planning, player performance, audience amenities and entertainment as well as numerous other aspects.

Easy Accessibility and Better Collaboration

Cloud analytics are easy to access on any standard browser from anywhere in the world. This is especially convenient for companies that have offices around the world. Teams spread across the globe can view the same information with just a few clicks of the mouse. This fosters better collaboration, saves time and produces quality output.


To make quick and well-informed decisions, the required information needs to be available to all stakeholders irrespective of their locations. However, confinement of important information means that everyone has access only to partial information. Hence, there is the risk of inferior decisions. When the data warehouse in the cloud is able to store all information and the BI system is able to provide rich, comprehensive analytics, it is available to all stakeholders.


There is a perception that information on the cloud is not secure. However, with cloud analytics gaining so much of reputation, steps are being taken to improve information security. For example, the providers of cloud analytics for industries such as banking, finance and healthcare all have to comply with strict regulations and standard protocols. However, subscribers and their IT and business teams should still be vigilant about protecting their own data.

Case Studies on Cloud Analytics

Daimler AG

Daimler AG, the famous automotive company, faced the complex problem of understanding the needs of its customers before they communicated their needs and of offering suitable products. To do that, Daimler subscribed to a cloud analytics solution that pulls and integrates data such as vehicle registration, mileage, issues and fail rates from different sources and uses predictive modeling on the analytics to estimate the time owners might replace their vehicles. Such information was provided to the dealers. Additionally, the marketing team would use data visualization tools to refine the information by adding market data. (To learn more about predictive analytics, see How Contextual Integration Can Empower Predictive Analytics.)


Urbio is an e-commerce startup that sells magnetic, vertical wall organizers. They had launched a promotional campaign for their products and wanted to have a comprehensive and integrated view of the data related to the campaign such as the number of sales and inquiries, time of sales, sales channels and prospect demography. It was not an easy task considering that the data were scattered across multiple sources such as Shopify, Facebook, Twitter and YouTube.

Blair Stewart, the vice president of operations, steered the campaign and wanted an integrated view of data which was also well-organized, curated, clean and user-friendly so that some action could be taken based on the data. So, Urbio used SumAll, a data analytics tool for marketing and e-commerce data. SumAll is a SaaS tool available in the Shopify app store. First, SumAll connected all platforms and moved the data into a single interface. After that, SumAll provided data visualization which presented data from all the platforms in a filtered manner relevant to the user.


Considering that cloud analytics is less expensive than on-premise analytics systems, it is only natural that more companies will adopt cloud analytics. But in the huge number of adoption lies the problem: Many will just jump onto the bandwagon without even considering whether they need it or are ready for it. So, it is likely that problems will arise. Companies need to first determine whether they actually need cloud analytics. And if they do, they need to identify the right solutions and take steps to protect their data in the cloud. Generally, though, cloud analytics represent a mouth-watering opportunity of reducing acquisition and maintenance costs and making better-informed decisions. In a sense, it has the potential to redefine how companies are doing business. It can, in fact, save companies from becoming irrelevant, as shown in the case of D&B.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…