Gartner: 7 Dynamics Influencing the Evolution of BI and Analytics

Global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc. By the end of 2020, the market is forecast to grow to $22.8 billion.

According to Gartner, modern BI and analytics continues to expand more rapidly than the overall market, which is offsetting declines in traditional BI spending. The modern BI and analytics platform emerged in the last few years to meet new organizational requirements for accessibility, agility and deeper analytical insight, shifting the market from IT-led, system-of-record reporting to business-led, agile analytics including self-service.

The modern BI and analytics market is expected to decelerate, however, from 63.6 percent growth in 2015 to a projected 19 percent by 2020. Gartner believes this reflects data and analytics becoming mainstream. The market is growing in terms of seat expansion, but revenue will be dampened by pricing pressure.

"Purchasing decisions continue to be influenced heavily by business executives and users who want more agility and the option for small personal and departmental deployments to prove success," said Rita Sallam, Research VP at Gartner. "Enterprise-friendly buying models have become more critical to successful deployments."

Gartner believes the rapidly evolving modern BI and analytics market is being influenced by the following 7 dynamics:

Modern BI at scale will dominate new buying

While business users initially flocked to new modern tools because they could be used without IT assistance, the increased need for governance will serve as the catalyst for renewed IT engagement. Modern BI tools that support greater accessibility, agility and analytical insight at the enterprise level will dominate new purchases.

New innovative and established vendors will drive the next wave of market disruption

The emergence of smart data discovery capabilities, machine learning and automation of the entire analytics workflow will drive a new flurry of buying because of its potential value to reduce time to insights from advanced analytics and deliver them to a broader set of people across the enterprise. While this "smart" wave is being driven by new innovative startups, traditional BI vendors that were slow to adjust to the current "modern" wave are driving it in some cases.

Need for complex datasets drives investments in data preparation

Business users want to analyze a diverse, often large and more complex combinations of data sources and data models, faster than ever before. The ability to rapidly prepare, clean, enrich and find trusted datasets in a more automated way becomes an important enabler of expanded use.

Extensibility and embeddability will be key drivers of expanded use and value

Both internal users and customers will either use more automated tools or will embed analytics in the applications they use in their context, or a combination of both. The ability to embed and extend analytics content will be a key enabler of more pervasive adoption and value from analytics.

Support for real-time events and streaming data will expand use

Organizations will increasingly leverage streaming data generated by devices, sensors and people to make faster decisions. Vendors need to invest in similar capabilities to offer buyers a single platform that combines real-time events and streaming data with other types of source data.

Interest in cloud deployments will continue to grow

Cloud deployments of BI and analytics platforms have the potential to reduce cost of ownership and speed time to deployment. However, data gravity that still tilts to the majority of enterprise data residing on-premises continues to be a major inhibitor to adoption. That reticence is abating and Gartner expects the majority of new licensing buying likely to be for cloud deployments by 2020.

Marketplaces will create new opportunities for organizations to buy and sell analytic capabilities and speed time to insight

The availability of an active marketplace where buyers and sellers converge to exchange analytic applications, aggregated data sources, custom visualizations and algorithms is likely to generate increased interest in the BI and analytics space and fuel its future growth.

"Organizations will benefit from the many new and innovative vendors continuing to emerge, as well as significant investment in innovation from large vendors and venture capital-funded startups," said Sallam. "They do, however, need to be careful to limit their technical debt that can occur when multiple stand-alone solutions that demonstrate business value quickly, turn into production deployments without adequate attention being paid to design, implementation and support."

Related Links

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

Gartner: 7 Dynamics Influencing the Evolution of BI and Analytics

Global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc. By the end of 2020, the market is forecast to grow to $22.8 billion.

According to Gartner, modern BI and analytics continues to expand more rapidly than the overall market, which is offsetting declines in traditional BI spending. The modern BI and analytics platform emerged in the last few years to meet new organizational requirements for accessibility, agility and deeper analytical insight, shifting the market from IT-led, system-of-record reporting to business-led, agile analytics including self-service.

The modern BI and analytics market is expected to decelerate, however, from 63.6 percent growth in 2015 to a projected 19 percent by 2020. Gartner believes this reflects data and analytics becoming mainstream. The market is growing in terms of seat expansion, but revenue will be dampened by pricing pressure.

"Purchasing decisions continue to be influenced heavily by business executives and users who want more agility and the option for small personal and departmental deployments to prove success," said Rita Sallam, Research VP at Gartner. "Enterprise-friendly buying models have become more critical to successful deployments."

Gartner believes the rapidly evolving modern BI and analytics market is being influenced by the following 7 dynamics:

Modern BI at scale will dominate new buying

While business users initially flocked to new modern tools because they could be used without IT assistance, the increased need for governance will serve as the catalyst for renewed IT engagement. Modern BI tools that support greater accessibility, agility and analytical insight at the enterprise level will dominate new purchases.

New innovative and established vendors will drive the next wave of market disruption

The emergence of smart data discovery capabilities, machine learning and automation of the entire analytics workflow will drive a new flurry of buying because of its potential value to reduce time to insights from advanced analytics and deliver them to a broader set of people across the enterprise. While this "smart" wave is being driven by new innovative startups, traditional BI vendors that were slow to adjust to the current "modern" wave are driving it in some cases.

Need for complex datasets drives investments in data preparation

Business users want to analyze a diverse, often large and more complex combinations of data sources and data models, faster than ever before. The ability to rapidly prepare, clean, enrich and find trusted datasets in a more automated way becomes an important enabler of expanded use.

Extensibility and embeddability will be key drivers of expanded use and value

Both internal users and customers will either use more automated tools or will embed analytics in the applications they use in their context, or a combination of both. The ability to embed and extend analytics content will be a key enabler of more pervasive adoption and value from analytics.

Support for real-time events and streaming data will expand use

Organizations will increasingly leverage streaming data generated by devices, sensors and people to make faster decisions. Vendors need to invest in similar capabilities to offer buyers a single platform that combines real-time events and streaming data with other types of source data.

Interest in cloud deployments will continue to grow

Cloud deployments of BI and analytics platforms have the potential to reduce cost of ownership and speed time to deployment. However, data gravity that still tilts to the majority of enterprise data residing on-premises continues to be a major inhibitor to adoption. That reticence is abating and Gartner expects the majority of new licensing buying likely to be for cloud deployments by 2020.

Marketplaces will create new opportunities for organizations to buy and sell analytic capabilities and speed time to insight

The availability of an active marketplace where buyers and sellers converge to exchange analytic applications, aggregated data sources, custom visualizations and algorithms is likely to generate increased interest in the BI and analytics space and fuel its future growth.

"Organizations will benefit from the many new and innovative vendors continuing to emerge, as well as significant investment in innovation from large vendors and venture capital-funded startups," said Sallam. "They do, however, need to be careful to limit their technical debt that can occur when multiple stand-alone solutions that demonstrate business value quickly, turn into production deployments without adequate attention being paid to design, implementation and support."

Related Links

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...