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Gartner: Advanced Analytics Is a Top Business Priority

Gartner, Inc. said advanced analytics is a top business priority, fueled by the need to make advanced analysis accessible to more users and broaden the insight into the business. Advanced analytics is the fastest-growing segment of the business intelligence (BI) and analytics software market and surpassed $1 billion in 2013.

"While advanced analytics have existed for over 20 years, big data has accelerated interest in the market and its position in the business," said Alexander Linden, Research Director at Gartner. "Rather than being the domain of a few select groups (for example, marketing, risk), many more business functions now have a legitimate interest in this capability to help foster better decision making and improved business outcomes."

IT and business leaders must expand their efforts to move their organizations from using only traditional BI that addresses descriptive analysis (what happened) to advanced analytics, which complements by answering "why", "what will happen" and "how we can address it".


Source: Gartner (October 2014)

"While basic analytics provide a general summary of data, advanced analytics deliver deeper data knowledge and granular data analysis," added Linden. The rewards of data-driven decision making can be a powerful boost to business outcomes. However, creating value from data requires a range of talents, from data integration and preparation, to architecting specialized computing/database environments, to data mining and intelligent algorithms. "Extracting value out of data is not a trivial task," said Linden. "One of the key elements of any such 'making sense out of data' program is the people, who must have the right skills and capabilities."

Data scientists in the organization support big data initiatives for which the No. 1 use case is enhancing customer experience. According to Gartner's latest global big data survey conducted in 2014, 68 per cent of respondents said that they use big data to enhance their customer experience. This is the third year customer experience has been a top business problem to address.

"Data scientists are not business analysts," said Linden. "They are professionals with the capability to derive mathematical models from data to reap clear and hard-hitting business benefits. They need to network well across different business units and work at the intersection of business goals, constraints, processes, available data and analytical possibilities."

Ultimately, data science is inevitable as it can help extract various kinds of knowledge from data, for example, how to acquire new customers (database marketing), how to do more cross-selling (via propensity-to-purchase modeling), information on route optimization, drug design and demand or failure prediction.

"Whether an organization calls the role data scientist or something else, individuals (or teams of professionals) with these core skills and soft skills will prove essential in maximizing the realized value of your information assets, and discovering opportunities for enhanced business performance and competitive advantage," said Linden.

About Gartner's Global Big Data Survey: The Gartner survey of 302 Gartner Research Circle members worldwide, which was conducted in June 2014, was designed to explore organizations' technology investment plans relating to big data, stages of big data adoption, business problems solved data, technology and challenges and compare the results with those from previous years.

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Gartner: Advanced Analytics Is a Top Business Priority

Gartner, Inc. said advanced analytics is a top business priority, fueled by the need to make advanced analysis accessible to more users and broaden the insight into the business. Advanced analytics is the fastest-growing segment of the business intelligence (BI) and analytics software market and surpassed $1 billion in 2013.

"While advanced analytics have existed for over 20 years, big data has accelerated interest in the market and its position in the business," said Alexander Linden, Research Director at Gartner. "Rather than being the domain of a few select groups (for example, marketing, risk), many more business functions now have a legitimate interest in this capability to help foster better decision making and improved business outcomes."

IT and business leaders must expand their efforts to move their organizations from using only traditional BI that addresses descriptive analysis (what happened) to advanced analytics, which complements by answering "why", "what will happen" and "how we can address it".


Source: Gartner (October 2014)

"While basic analytics provide a general summary of data, advanced analytics deliver deeper data knowledge and granular data analysis," added Linden. The rewards of data-driven decision making can be a powerful boost to business outcomes. However, creating value from data requires a range of talents, from data integration and preparation, to architecting specialized computing/database environments, to data mining and intelligent algorithms. "Extracting value out of data is not a trivial task," said Linden. "One of the key elements of any such 'making sense out of data' program is the people, who must have the right skills and capabilities."

Data scientists in the organization support big data initiatives for which the No. 1 use case is enhancing customer experience. According to Gartner's latest global big data survey conducted in 2014, 68 per cent of respondents said that they use big data to enhance their customer experience. This is the third year customer experience has been a top business problem to address.

"Data scientists are not business analysts," said Linden. "They are professionals with the capability to derive mathematical models from data to reap clear and hard-hitting business benefits. They need to network well across different business units and work at the intersection of business goals, constraints, processes, available data and analytical possibilities."

Ultimately, data science is inevitable as it can help extract various kinds of knowledge from data, for example, how to acquire new customers (database marketing), how to do more cross-selling (via propensity-to-purchase modeling), information on route optimization, drug design and demand or failure prediction.

"Whether an organization calls the role data scientist or something else, individuals (or teams of professionals) with these core skills and soft skills will prove essential in maximizing the realized value of your information assets, and discovering opportunities for enhanced business performance and competitive advantage," said Linden.

About Gartner's Global Big Data Survey: The Gartner survey of 302 Gartner Research Circle members worldwide, which was conducted in June 2014, was designed to explore organizations' technology investment plans relating to big data, stages of big data adoption, business problems solved data, technology and challenges and compare the results with those from previous years.

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One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...