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Cloud-based computing has become widespread, particularly in line-of-business applications from vendors such as Salesforce and SuccessFactors. Our benchmark research also suggests a rise in the acceptance of cloud-based analytics.  We’ve seen the emergence and growth of cloud-only analytics vendors such as Domo and GoodData as well as cloud-based delivery by nearly all the on-premises analytics vendors. Almost half (48%) of organizations in vr_DAC_04_widespread_use_of_cloud_based_analyticsour benchmark research on data and analytics in the cloud are using cloud-based analytics today, and two-thirds said they expect to be using cloud-based analytics within 12 months. In fact, only 1 percent said they do not intend to use cloud-based analytics at some point. This popularity leads to the question of how to maximize the value of investments in cloud-based analytics. We assert that one of the most important best practices for cloud-based analytics is to empower business users with modern analytics tools they can work with without relying on IT.

Part of the premise of cloud computing in general is to reduce reliance on in-house IT. Line-of-business groups are drawn to the cloud because it enables them to concentrate on the business at hand. They don’t have to wait for IT to set up systems and often can purchase cloud-based services without a capital requisition process. Not only do users want this independence, but cloud-based systems benefit IT, too, by reducing the administrative burden – there’s no need to acquire, install and configure hardware and the associated software. They also help reduce ongoing maintenance since some of that is the responsibility of the cloud application provider.

Cloud-based analytics have benefits that go beyond reducing the administration burden. Organizations in our research most often ranked first or second improved communication and knowledge sharing (39%), improved efficiency in business processes (35%) and decreased time to market (24%). In the context of cloud-based applications of any type, these findings should not come as a surprise. These systems enable access to data from any device in any location. Ready access to information should improve communication, efficiency and consistency. Workers can review and share information as they are performing their jobs in the field, on the shop floor, in the warehouse or when meeting with customers. In addition, more than half (52%) of organizations reported improved data quality and data management as a benefit.

For these and other reasons users want to be self-sufficient. Usability is consistently the most important software evaluation criterion in our various benchmark research studies. vr_DAC_22_self-service_for_cloud--based_analyticsIn the data and analytics in the cloud research, usability was the highest-ranked of seven evaluation criterion: Almost two-thirds (63%) of participants said it is very important.

However, the research also finds that most users do not access their cloud-based analytics without the help of IT. Only 40 percent said they are able to analyze their data by themselves. Is this important? If we look at the results organizations are able to achieve, the answer is yes. Those that operate without IT are both more confident (77% vs. 44%) and more often satisfied (71% vs. 55%) in their ability to use cloud-based analytics than those that do not.

As our research shows, the advent of cloud-based analytics is here. Empowering business users makes it possible to improve business outcomes. The IT organization will be free to focus its attention on critical issues only it can address. Thus modern tools for cloud-based analytics can benefit both the lines of business and IT.

Regards,

David Menninger

SVP & Research Director

Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.

As a technology, predictive analytics has existed for years, but adoption has not been widespread among businesses. In our recent benchmark research on business analytics among more than 2,600 organizations, predictive analytics ranked only 10th among technologies they use to gene­rate analytics, and only one in eight of those companies use it. Predictive analytics has been costly to acquire, and while enterprises in a few vertical industries and specific lines of business have been willing to invest large sums in it, they constitute only a fraction of the organizations that could benefit from them. Ventana Research has just completed a benchmark re­search project to learn about how the organizations that have adopted predictive analytics are using it and to ac­quire real-world information about their levels of maturity, trends and best practices. In this post I want to share some of the key findings from our research.

As I have noted, varieties of predictive analytics are on the rise. The huge volumes of data that organizations accumulate are driving some of this interest. Our Hadoop research highlights the intersection of this big data and predictive analytics: More than two-thirds (69%) of Hadoop users perform advanced analytics such as data mining. Regardless of the reasons for the rise, our new research confirms the importance of predictive analytics. Participants overwhelmingly reported that these capabilities are important or very important to their organization (86%) and that they plan to deploy more predictive analytics (94%). One reason for the importance assigned to predictive analytics is that most organizations apply it to core functions that produce revenue. Marketing and sales are the most common of those. The top five sources of data tapped for predictive analytics also relate directly to revenue: customer, marketing, product, sales and financial.

Although participants are using predictive analytics for important purposes and are generally positive about the experience, they do not minimize its complexities. While now usable by more types of people, this technology still requires special skills to design and deploy, and in half of organizations the users of it don’t have them. Having worked for two different vendors in the predictive analytics space, I personally can testify that the mathematics of it requires special training. Our research bears this out. For example, 58 percent don’t understand the mathematics required. Although not a math major, I had always been analytically oriented, but to get involved in predictive analytics I had to learn new concepts or new ways to apply concepts I knew.

Organizations can overcome these issues with training and support. Unfortunately, most are not doing an adequate job in these areas. Not half (44%) said their training in predictive analytics concepts and techniques is adequate, and fewer than one-fourth (24%) provide adequate help desk resources. These are important places to invest because organizations that do an adequate job in these two areas have the highest levels of satisfaction with their use of predictive analytics; 89% of them are satisfied vs. 66% overall. But we note that product training is not the most important type. That also correlated to higher levels of satisfaction, but training in concepts and the application of those concepts to business problems showed stronger correlation.

Timeliness of results also has an impact on satisfaction. Organizations that use real-time scoring of records occasionally or regularly are more satisfied than those that use real-time scoring infrequently or not at all. Our research also shows that organizations need to update their models more frequently. Almost four in 10 update their models quarterly or less frequently, and they are less satisfied with their predictive analytics projects than those who update more frequently. In some ways model updates represent the “last mile” of the predictive analytics process. To be fully effective, organizations need to build predictive analytics into ongoing business processes so the results can be used in real time. Using models that aren’t up to date undermines the whole effort.

Thanks to our sponsors, IBM and Alpine Data Labs, for helping to make this research available. And thanks to our media sponsors, Information ManagementKD Nuggets and TechTarget, for helping in gaining participants and promoting the research and educating the market. I encourage you to explore these results in more detail to help ensure your organization maximizes the value of its predictive analytics efforts.

Regards,

David Menninger – VP & Research Director

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