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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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The Latest

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...