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As big data and cloud computing have become more popular, the market for cloud analytics has taken off. In the February 2017 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms report, a majority of those surveyed (51 percent) said that they had active or planned cloud business intelligence (BI) deployments. Gartner analysts added, "We expect this trend to continue, with the majority of new license buying (more than half) likely to be for cloud deployments by 2020." Overall, the firm forecasts that the BI and analytics market will grow 7.9 per year through 2020.
A Harvard Business Review report commissioned by Oracle found even higher interest in cloud analytics: 69 percent of those surveyed expected to be using cloud analytics by the end of 2017.
Source: the HBR report cited above.
This article will cover the definition of cloud analytics, the benefits of cloud analytics, cloud analytics challenges, popular use cases and links to popular cloud analytics tools. For help choosing a cloud provider for your business, see our comprehensive guide to cloud computing.
What is Cloud Analytics?
When people use the term "cloud analytics," they are usually talking about big data analytics software that is delivered on a software as a service (SaaS) basis. According to Gartner, "Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. . . . Increasingly, 'analytics' is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen." Other terms used to describe the same software include cloud-based BI, SaaS BI or SaaS analytics.
Sometimes, however, people use the term "cloud analytics" to refer to analysis of data related to cloud computing. These are usually cloud management solutions that monitor the performance of cloud infrastructure and applications.
This article will focus primarily on the first definition of cloud analytics (big data analytics delivered through the cloud) rather than the second (analysis of cloud-related data).
Cloud analytics brings together the benefits of cloud computing and the benefits of big data analytics. Some of the most important benefits of cloud analytics include the following:
- Improved decision-making: The HBR study found that 82 percent of respondents said that their BI and analytics tools had improved the quality of their decisions. Organizations were able to obtain valuable insights from their data that allowed them to gain new customers, increase revenues and margins, and bring new products to market more quickly.
- Improved planning and forecasting: Cloud analytics allows business users, particularly those in the finance department, to incorporate data from a wide variety of sources into their data models. That allows them to conduct more granular analysis that results in more accurate forecasts. That, in turn, allows business leaders to manage investor expectations more accurately, which can benefit the company's stock price.
- Single source of data: Many organizations have business data that resides in multiple databases and data warehouses. Some cloud analytics tools make it easier to validate and integrate that data, resulting in a "single source of truth" for the organization.
- Greater speed and efficiency: Generating reports with traditional on-premise BI tools can sometimes take hours, days or even weeks. Because they rely on more modern hardware and software, many cloud analytics services are able to reduce that time dramatically, in some cases taking just seconds to generate reports that otherwise would taken many more hours. That allows business users to run more reports that consider more variables, multiplying the benefit that they realize from their analytics efforts.
- Agility: That greater speed afforded by cloud analytics also leads to greater agility for the business. With cloud analytics, they are able to see and respond to changing market conditions more quickly, which can offer a competitive advantage.
- Lower costs: Many traditional BI tools rely on proprietary technology that can be very expensive. Cloud analytics are often much more cost-effective. In addition, using a cloud-based service eliminates the need for organizations to purchase, operate and maintain the hardware necessary for their analytics efforts, which further reduces costs.
- Scalability: Today organizations are experiencing exponential data growth. Purchasing the hardware to store and analyze their growing data would require constantly expanding and upgrading their infrastructure — at significant cost. But with the cloud, organizations can easily add storage or computing resources as necessary.
- User satisfaction: Business users frequently prefer cloud-based tools over traditional on-premise BI. That can boost analytics usage, while also improving employee morale.
A 2016 Aberdeen Group report found that many of these benefits are greater for organizations using cloud analytics than for those using on-premise BI solutions. The cloud advantage was particularly strong when it came to business user satisfaction with their analytics tools, customer retention and revenue growth.
While cloud analytics offers a number of significant benefits, enterprises will also face some challenges with their cloud analytics efforts. Some of the most significant potential pitfalls include the following:
- Security: Security is a concern with any public cloud computing service, and SaaS-based analytics offerings are no exception. Because many organizations will want to include transactional data and customer service data in their analysis efforts, they will need to make sure they have strong data protection measures in place in order to secure sensitive customer data. Any cloud analytics service they use should offer strong authentication and encryption capabilities, at a minimum.
- Compliance: In some industries, such as financial services and health care, organizations must comply with regulations that strictly control their handling of patient or customer data. Enterprises need to ensure their cloud analytics services will meet their compliance requirements.
- Lack of skills: Data scientists are in short supply and high demand, meaning that many organizations don’t have all the expertise they need in-house to select and use a cloud analytics solution. They may need to invest in staff training or bring in outside consultants to ensure the success of their cloud analytics initiatives. They may also want to look for tools designed for use by business users rather than data science experts.
- Data migration: The process of moving petabytes of data from in-house data centers to the cloud can be extremely challenging. And because data volumes are always growing, it’s not a one-time process. Organizations will have to adapt their processes and procedures to make sure that the appropriate data is available through their cloud analytics service.
- Ease of Use: In the HBR study, only 3 percent of respondents rated their current analytics tools "very good" when it comes to usability. The majority, 68 percent, said their tools were fair, poor or very poor. With demand for data scientists greatly exceeding the available supply, many organizations are looking for cloud analytics tools that business users without data science training can use. And while some say the current crop of tools are better than older tools, ease of use continues to be an issue.
- Lack of customization: With traditional on-premise BI tools, organizations could do a lot of customization in order to meet their unique needs. That isn’t always the case with cloud-based solutions, where the vendor must take a broader approach in order to meet the needs of many different types of organizations.
- Vendor lock-in: Different cloud analytics vendors use different technologies to support their offerings, and moving from one vendor to another can be extremely difficult. Organizations should take their time with the vendor selection process because it may be cost-prohibitive to make a change once they have begun using a particular data analytics tool in production.
Organizations apply cloud analytics capabilities to a wide variety of different domains. For example, business users might analyze sales, marketing, supply chain or customer service data to look for new opportunities. They might analyze transaction data to look for fraud. Frequently, they also incorporate external data, such as customer demographics, market size and competitive information, into their analysis in order to improve their marketing and forecasting capabilities.
The IT team can also make use of cloud analytics. For instance, they might use cloud-based tools to analyze their Web traffic, to spot security incidents in log data or to track the performance of their cloud-based infrastructure and applications.
Some vendors sell customized cloud analytics tools that are tailored to one of these specific needs, such as sales or marketing. Others sell tools with more broad capabilities that can be adapted to different use cases.
Cloud analytics vendors fall into two broad categories: those who only offer analytics tools and those who offer a wide variety of enterprise software, including cloud analytics. Some of the better-known cloud analytics tools from both categories are listed below, in alphabetical order:
Pure-play analytics vendors with cloud analytics offerings:
- Adaptive Insights
- Angoss Managed Services
- Domino Cloud Data Science
- KNIME Cloud Analytics
- RapidMiner Cloud
- Tableau Online
- Teradata Intellicloud
- TIBCO Spotfire Cloud
Enterprise software vendors with cloud analytics offerings:
- FICO Analytic Cloud
- HPE Big Data Software
- IBM Watson Analytics
- Informatica Cloud Analytics
- Microsoft Power BI
- Oracle Analytics Cloud
- Salesforce Analtyics Cloud Einstein
- SAP BusinessObjects Cloud
- SAS Cloud Analytics
- Zendesk BIME