Statistics sourced from business2community.com, originally published August 2017. Source reports include Deloitte/EMA/Informatica, Gartner, BARC Research, Dresner Advisory Services, KPMG, Wikibon, BDO, and Nomura Holdings. Note that these figures reflect 2017-era forecasts and should be treated as historical context rather than current benchmarks.
Cloud Is Reshaping Enterprise Business Intelligence
As once-common objections to cloud adoption — particularly around security — began to ease in the mid-2010s, organizations started identifying which IT and business initiatives would benefit most from cloud deployments. Business intelligence (BI) and analytics emerged as one of the clearest candidates. Research from that era consistently pointed toward cloud becoming the dominant delivery mechanism for analytics workloads. According to a joint Deloitte, EMA, and Informatica State of Cloud Analytics Report, 70.1% of respondents already considered cloud an essential part of their analytics strategy.
The 17 statistics below — drawn from leading research firms and industry surveys circa 2016–2017 — paint a picture of the momentum that was building behind cloud BI, and provide useful historical context for understanding how the market evolved.
Market Growth and Spending Trends
- Enterprise cloud spending was forecast to grow at a 16% compound annual growth rate (CAGR) between 2016 and 2026. — Wikibon 2017 True Private Cloud Forecast and Market Shares
- Adoption of cloud BI solutions grew 50% over three years, from 29% to 43%. — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
- By 2020, more than $1 trillion in IT spending was projected to be directly or indirectly affected by the shift to cloud. — Gartner Market Insight: Cloud Shift — The Transition of IT Spending from Traditional Systems to Cloud
Strategic Adoption and Executive Priorities
- By 2020, any non-cloud strategy for new IT initiatives was expected to require formal justification at more than 30% of large enterprises. — Gartner Predicts 2017: Cloud Computing Enters Its Second Decade
- By 2021, more than half of global enterprises already using cloud were forecast to adopt an all-in cloud strategy. — Gartner Predicts 2017: Cloud Computing Enters Its Second Decade
- 74% of technology CFOs said cloud computing would have the most measurable business impact in 2017. — 2017 BDO Technology Outlook Survey
- CIOs ranked (1) security, (2) cloud computing, and (3) big data analytics and mobile as their top 2017 IT budget priorities. — Nomura Holdings CIO Survey Research
Why Organizations Are Moving BI to the Cloud
Understanding the motivations behind cloud BI adoption helps explain why momentum built so quickly. Research from KPMG and Deloitte identified both technical and business drivers.
Business Drivers
- The three top reasons CIOs adopted cloud were: (1) improve agility and responsiveness, (2) accelerate product development and innovation, and (3) reduce costs. — KPMG, Journey to the Cloud: The Creative CIO Agenda
- 29% of CIOs cited data center modernization as their primary reason for cloud adoption. — KPMG, Journey to the Cloud: The Creative CIO Agenda
- The top three business drivers of cloud analytics adoption were: enhancing business processes (23.5%), improving customer experience (22.2%), and better collaboration (19.1%). — Deloitte, EMA and Informatica State of Cloud Analytics Report
Technical Drivers
- The top three technical factors driving cloud analytics adoption were: reducing costs (19.5%), data security (15.1%), and collaboration (14.4%). — Deloitte, EMA and Informatica State of Cloud Analytics Report
Cloud BI Adoption and Organizational Readiness
- Cloud was an essential part of the analytics strategy for 70.1% of organizations; a further 21.6% said it was necessary as part of their analytics adoption path. — Deloitte, EMA and Informatica State of Cloud Analytics Report
- Nearly 50% of executive management teams viewed cloud BI as critical or very important to their information needs. — 2017 Cloud Computing and Business Intelligence Market Study, Dresner Advisory Services
- Financial services, higher education, business services, and retail were the four industries placing the greatest importance on cloud BI. — 2017 Cloud Computing and Business Intelligence Market Study, Dresner Advisory Services
How Organizations Use Cloud BI
- The top three cloud BI use cases were: dashboard-based reporting (76%), ad-hoc analysis and exploration (57%), and dashboard authoring (55%). — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
- Top planned investments in cloud BI for the coming year included advanced and predictive analytics (53%), operational planning and forecasting (44%), and strategic planning and simulation (44%). — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
- 69% of companies planned to increase their use of cloud BI, and more than one quarter of companies that had not yet implemented cloud BI said they would do so in the coming year. — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
Key Takeaways for DevOps and Data Engineering Teams
Even viewed as historical data, these statistics underscore a structural shift that has continued well beyond 2017. Several patterns remain relevant for teams architecting modern data and analytics platforms:
- Agility and speed-to-insight outweigh pure cost savings as the primary justification for cloud BI — cost is a secondary benefit, not the lead argument.
- Security perception shifted from barrier to driver: by 2017, data security was already listed as a factor motivating cloud adoption, not blocking it.
- Dashboard reporting dominates near-term use cases, but predictive analytics investment was already the top planned priority — a trend that has accelerated significantly with modern ML platforms.
- Collaboration and cross-team access consistently appeared in both business and technical driver lists, pointing to the importance of governed, shared data platforms over siloed on-premise warehouses.
For teams evaluating or evolving their current cloud analytics stack, these foundational drivers — agility, collaboration, cost efficiency, and advanced analytics readiness — remain valid criteria for architectural decisions today.