New Generation of Data and Analytics in Cloud

It’s widely agreed that cloud computing is a major technology innovation. Many companies use cloud-based systems for specific business functions such as customer service, sales, marketing, finance and human resources. More generally, however, analytics and business intelligence (BI) have not migrated to the cloud as quickly. But now cloud-based data and analytics products are becoming more common. This trend is most popular among technology companies, small and midsize businesses, and departments in larger ones, but there are examples of large companies moving their entire BI environments to the cloud. Our research into big data analytics shows that more than one-fourth of analytics initiatives for companies of all sizes are cloud-based.

vr_bti_br_top_benefits_of_cloud_computingLike other cloud-based applications, cloud analytics offers enhanced scalability and flexibility, affordability and IT staff optimization. Our research shows that in general the top benefits are lowered costs (for 40%), improved efficiency (39%) and better communication and knowledge sharing (34%). Using the cloud, organizations can use a sophisticated IT infrastructure without having to dedicate staff to install and support it. There is no need for comprehensive development and testing because the provider is responsible for maintaining and upgrading the application and the infrastructure. The cloud can also provide flexible infrastructure resources to support “sandbox” testing environments for advanced analytics deployments. Multitenant cloud deployments are more affordable because costs are shared across many companies. When used departmentally, application costs need not be capitalized but instead can be made operational expenditures. Capabilities can be put to use quickly, as vendors develop them, and updates need not disrupt use. Finally, some cloud-based interfaces are more intuitive for end users since they have been designed with the user experience in mind. Regarding cloud technology, our business technology innovation research finds that usability is the most important technology evaluation criterion (for 64% of participants), followed by reliability (54%) and capability (%).

vr_bti_why_companies_dont_use_cloudFor analytics and BI specifically, there are still issues holding back adoption. Our research finds that a primary reason companies do not deploy cloud-based applications of any sort are security and compliance issues. For analytics and business intelligence, we can also include data related activities as another reason since cloud-based approaches often require data integration and transmission of sensitive data across an external network along with a range of data preparation. Such issues are especially prevalent for companies that have legacy BI tools using data models that have been distributed across their divisions. Often these organizations have defined their business logic and metrics calculations within the context of these tools. Furthermore, these tools may be integrated with other core applications such as forecasting and planning. To re-architect such data models and metrics calculations is a challenge some companies are reluctant to undertake.

In addition, despite widespread use of some types of cloud-based systems, for nontechnical business people discussions of business intelligence in the cloud can be confusing, especially when they involve information integration, the types of analytics to be performed and where the analytic processes will. The first generation of cloud applications focused on end-user processes related to the various lines of business and largely ignored the complexities inherent in information integration and analytics. Organizations can no longer ignore these complexities since doing so exacerbates the challenge of fragmented systems and distributed data. Buyers and architects should understand the benefits of analytics in the cloud and weigh these benefits against the challenges described above.

Our upcoming benchmark research into data and analytics in the cloud will examine the current maturity of this market as well opportunities and barriers to organizational adoption across line of business and IT. It will evaluate cloud-based analytics in the context of trends such as big data, mobile technology and social collaboration as well as location intelligence and predictive analytics. It will consider how cloud computing enables these and other applications and identify leading indicators for adoption of cloud-based analytics. It also will examine how cloud deployment enables large-scale and streaming applications. For example, it will examine real-time processing of vast amounts of data from sensors and other semistructured data (often referred to as the Internet of Things).

It is an exciting time to be studying this particular market as companies consider moving platforms to the cloud. I look forward to receiving any qualified feedback as we move forward to start this important benchmark research. Please get in touch if you have an interest in this area of our research.


Ventana Research

The Next Generation of Predictive Analytics

Our benchmark research consistently shows that business analytics is the most significant technology trend in business today and acquiring effective predictive analytics is organizations’ top priority for analytics. It enables them to look forward rather than backward and, participate organizations reported, leads to competitive advantage and operational efficiencies.

In our benchmark research on big data analytics, for example, 64 percent of organizations ranked predictive analytics as the most Untitledimportant analytics category for working with big data. Yet a majority indicated that they do not have enough experience in applying predictive analytics to business problems and lack training on the tools themselves.

Predictive analytics improves an organization’s ability to understand potential future outcomes of variables that matter. Its results enable an organization to decide correct courses of action in key areas of the business. Predictive analytics can enhance the people, process, information and technology components of an organization’s future performance.

In our most recent research on this topic, more than half (58%) of participants indicated that predictive analytics is very important to their organization, but only one in five said they are very satisfied with their use of those analytics. Furthermore, our research found that implementing predictive analysis would have a transformational impact in one-third of organizations and a significant positive impact in more than half of other ones.

In our new research project, The Next Generation of Predictive Analytics, we will revisit predictive analysis with an eye to determining how attitudes toward it have changed,  along with its current and planned use, and its importance in business. There are significant changes in this area, including where, how, why, and when predictive analytics are applied. We expect to find changes not only in forecasting and analyzing customer churn but also in operational use at the front lines of the organization and in improving the analytic process itself. The research will also look at the progress of emerging statistical languages such as R and Python, which I have written about.

vr_predanalytics_benefits_of_predictive_analytics_updatedAs does big data analytics, predictive analytics involves sourcing data, creating models, deploying them and managing them to understand when an analytic model has become stale and ought to be revised or replaced. It should be obvious that only the most technically advanced users will be familiar with all this, so to achieve broad adoption, predictive analytics products must mask the complexity and be easy to use. Our research will determine the extent to which usability and manageability are being built into product offerings.

The promise of predictive analytics, including competitive advantage (68%), new revenue opportunities (55%), and increased profitability (52%), is significant. But to realize the advantages of predictive analytics, companies must transform how they work. In terms of people and processes a more collaborative strategy may be necessary. Analysts need tools and skills in order to use predictive analytics effectively. A new generation of technology is also becoming available where predictive analytics are easier to apply and use, along with deploy into line of business processes. This will help organizations significantly as there are not enough data scientists and specially trained professionals in predictive analytics that will be available for organizations to utilize or afford to hire.

This benchmark research will look closely at the evolving use of predictive analytics to establish how it equips business to make decisions based on likely futures, not just the past.


Ventana Research