The adoption of cloud-based business intelligence solutions has increased by 50 percent over the past three years, and by the end of this year a majority of organizations are expected to invest in the cloud for BI and data management.
That is the conclusion of a new study on business intelligence and data management trends by The Business Application Research Center (BARC) and Eckerson Group, both independent market analysis companies, entitled “BI and Data Management in the Cloud: Issues and Trends.”
The study polled 370 IT leaders on their strategies around BI and data management, including spending plans for the next 12 months. It revealed that, while the adoption of cloud-based BI and data management products had been hovering at around 30 percent for several years, the rate of adoption has increased dramatically in the past few months.
“Adoption of the cloud BI solutions has grown 50 percent in the past three years, from 29 percent to 43 percent,” according to Carston Bange, managing director at BARC, and Wayne Eckerson, principal consultant at Eckerson Group, authors of the study. “As more applications move to the cloud, companies find it easier to keep their data there as well. Although security is still a concern, many companies recognize that data is safer in a shared public cloud than in a corporate data center.”
The study also finds that organizations are more likely to run BI applications in the cloud than implement data warehouses, data marts and data integration tasks there.
“As a SaaS application, BI is much easier to deploy in the cloud than a data management solution, which requires infrastructure as a service (IaaS) and platform as a service (PaaS) deployments,” the authors note. “Also, companies must assess security, privacy and political issues when moving data in the cloud.”
IT leaders cite flexibility, cost, and scalability as the top reasons for implementing BI and data management in the cloud, at 40 percent, 39 percent, and 35 percent respectively. Those reasons are followed by speed of implementation (cited by 33 percent), reduced maintenance of hardware and software (cited by 31 percent), and agility (cited by 24 percent).
Interestingly, small companies are more likely to implement BI tools and data warehouses in the cloud than medium-size or large companies. They are also more likely to use the public cloud than private or hybrid clouds.
“This makes sense since many small companies don’t have legacy systems, IT staff or in-house infrastructure to prevent them from embracing the cloud,” Bange and Eckerson explain. “They can use the cloud to leapfrog bigger companies with more mature BI implementations.”
The top reason that organizations use the cloud with their BI and data management efforts is to deliver reports and dashboards (cited by 76 percent). But more than half also use the cloud to perform ad hoc analysis (57 percent) and author reports (55 percent). Next is data preparation for analysis (cited by 39 percent).
When it comes to data management, the top two use cases cited by survey respondents are data integration between cloud application databases (cited by 51 percent) and to provide data warehouses and data marts (cited by 50 percent).Those were followed by data integration between on-premises and cloud applications (cited by 46 percent), pre-processing of data (cited by 30 percent), to provide sandboxes or other types of business user controlled data stores (cited by 29 percent), and processing of calculations and data mining models (cited by 27 percent).
Finally, respondents were asked which business intelligence components run in the cloud. The results were: BI tools (62 percent), BI servers (51 percent), tools for data exploration (49 percent), predictive and advanced analytics tools (25 percent), analytical applications (22 percent), and performance management tools (18 percent).
For data management components, the results were: date warehouses (42 percent), ETL/batch data integration (35 percent), data preparation for business users (33 percent), real-time data integration/application integration (31 percent), data marts (31 percent), data lakes (18 percent), metadata management (18 percent), and data quality/profiling (18 percent).