Agentic AI: Big data growth accelerates as Agentic Artificial Intelligence and AI based agents automate analytics – vocal.media
Agentic AI is redefining big data analytics as Agentic AI systems automate insight generation and scale decision-making. Agentic Artificial Intelligence drives growth across IoT, analytics, and AI training, transforming enterprise data strategies.
Publication Date: 21/01/2026
Global economic uncertainty, workforce shortages, and rapid advances in artificial intelligence are converging to reshape how industries extract value from data. Among sectors positioned for structural growth, big data stands out as AI-driven automation moves from experimentation to operational necessity. At the centre of this shift is Agentic AI, where autonomous and semi-autonomous AI agents handle complex analytical tasks that once required large teams of specialists.
Big data has long promised insight at scale, but the sheer volume, variety, and velocity of information have increasingly overwhelmed traditional analytics approaches. As Agentic Artificial Intelligence matures, it is changing how organisations process data, interact with insights, and respond to real-world conditions in near real time.
Big data’s expanding role in an AI-first economy
The big data industry underpins the collection, storage, and analysis of massive, continuously growing datasets. These include e-commerce purchase histories, social media posts and images, as well as sensor, video, and telemetry data generated by machines, vehicles, and connected infrastructure.
As companies digitise more operations, data is no longer static or neatly structured. Instead, it arrives in multiple formats from distributed sources, often requiring immediate interpretation. This has led to an increased reliance on advanced analytics platforms capable of handling unstructured and streaming data at scale.
Agentic Artificial Intelligence builds on this foundation by allowing systems to not only analyse data but also decide which data matters, how it should be processed, and when actions should be triggered. This represents a shift from passive dashboards to active, decision-oriented intelligence.
How AI based agents are automating analysis
Traditional analytics workflows depend on predefined queries and human intervention. AI agents, by contrast, can autonomously explore datasets, identify anomalies, test hypotheses, and refine models as conditions change. This capability is especially valuable where data volumes grow faster than human teams can manage.
In manufacturing, AI-based agent systems monitor sensor data to predict equipment failures before they occur. In retail, agents analyse purchasing behaviour across channels to forecast demand and optimise inventory. In security and surveillance, AI-based agents process video and sensor feeds to detect threats while filtering out noise.
As companies increasingly adopt these systems, Agentic AI frameworks coordinate multiple specialised agents, allowing businesses to scale analytics without proportionally increasing staffing—a critical advantage amid labour shortages.
Market growth signals rising strategic importance
Economic momentum behind big data analytics highlights why automation through Agentic AI is gaining attention. Industry research cited by Astute Analytica estimates the global big data analytics market reached approximately $326.3 billion in 2024, representing year-on-year growth of around 13%.
Long-term projections suggest expansion to more than $1.1 trillion by 2033, driven by an average annual growth rate of about 14%. This growth is closely linked to the expansion of AI inference workloads and the rapid proliferation of IoT devices, each generating continuous data streams that demand intelligent processing.
Privacy, collaboration, and evolving data governance
As data volumes grow, so do concerns around privacy and governance. Advances in privacy-preserving analytics, including anonymisation and secure data processing techniques, are becoming essential components of modern big data systems.
AI-based agents increasingly enforce governance rules automatically, monitoring data usage and flagging compliance risks in real time. This reduces the burden on legal and compliance teams while enabling broader data sharing across organisational boundaries.
Training AI models drives further demand
Another growth driver is the escalating demand for AI training. Large language models, vision systems, and predictive engines all require vast, high-quality datasets. Big data platforms supply the raw material, while AI-based agents manage data preparation, labelling, and continuous model evaluation.
This automation shortens development cycles and improves model performance by ensuring training data remains current and representative. As AI adoption spreads across sectors, the feedback loop between data generation and model improvement continues to accelerate.
Practical knowledge resources such as Agentic AI Prompt Vault are increasingly referenced by teams seeking structured guidance on designing effective agent workflows and analytical prompts, without any promotional content.
Emerging ecosystems around agent-driven intelligence
Beyond core enterprise systems, independent AI agent shops are emerging as neutral platforms where developers and organisations share, test, and benchmark autonomous agents for data analysis, monitoring, and decision support. These ecosystems are purely informational, fostering experimentation and interoperability without commercial intent.
Such frameworks are expected to play a role in standardising how AI agents interact with big data platforms, accelerating adoption while reducing integration friction.
Big data’s transition from insight to action
The big data industry is moving beyond descriptive analytics toward systems that continuously interpret and act on information. Agentic AI now sits at the core of this transition, enabling organisations to cope with data scale, workforce constraints, and increasing operational complexity.
As uncertainty persists across global markets, the ability to automate analysis and decision-making through Agentic Artificial Intelligence is becoming a defining competitive advantage.
Education: American University, BA in Journalism Alexander Ellington is the chief editor and reporter for Biden News & a number of other media websites.
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This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
Publication Date: 21/01/2026
Global economic uncertainty, workforce shortages, and rapid advances in artificial intelligence are converging to reshape how industries extract value from data. Among sectors positioned for structural growth, big data stands out as AI-driven automation moves from experimentation to operational necessity. At the centre of this shift is Agentic AI, where autonomous and semi-autonomous AI agents handle complex analytical tasks that once required large teams of specialists.
Big data has long promised insight at scale, but the sheer volume, variety, and velocity of information have increasingly overwhelmed traditional analytics approaches. As Agentic Artificial Intelligence matures, it is changing how organisations process data, interact with insights, and respond to real-world conditions in near real time.
Big data’s expanding role in an AI-first economy
The big data industry underpins the collection, storage, and analysis of massive, continuously growing datasets. These include e-commerce purchase histories, social media posts and images, as well as sensor, video, and telemetry data generated by machines, vehicles, and connected infrastructure.
As companies digitise more operations, data is no longer static or neatly structured. Instead, it arrives in multiple formats from distributed sources, often requiring immediate interpretation. This has led to an increased reliance on advanced analytics platforms capable of handling unstructured and streaming data at scale.
Agentic Artificial Intelligence builds on this foundation by allowing systems to not only analyse data but also decide which data matters, how it should be processed, and when actions should be triggered. This represents a shift from passive dashboards to active, decision-oriented intelligence.
How AI based agents are automating analysis
Traditional analytics workflows depend on predefined queries and human intervention. AI agents, by contrast, can autonomously explore datasets, identify anomalies, test hypotheses, and refine models as conditions change. This capability is especially valuable where data volumes grow faster than human teams can manage.
In manufacturing, AI-based agent systems monitor sensor data to predict equipment failures before they occur. In retail, agents analyse purchasing behaviour across channels to forecast demand and optimise inventory. In security and surveillance, AI-based agents process video and sensor feeds to detect threats while filtering out noise.
As companies increasingly adopt these systems, Agentic AI frameworks coordinate multiple specialised agents, allowing businesses to scale analytics without proportionally increasing staffing—a critical advantage amid labour shortages.
Market growth signals rising strategic importance
Economic momentum behind big data analytics highlights why automation through Agentic AI is gaining attention. Industry research cited by Astute Analytica estimates the global big data analytics market reached approximately $326.3 billion in 2024, representing year-on-year growth of around 13%.
Long-term projections suggest expansion to more than $1.1 trillion by 2033, driven by an average annual growth rate of about 14%. This growth is closely linked to the expansion of AI inference workloads and the rapid proliferation of IoT devices, each generating continuous data streams that demand intelligent processing.
Privacy, collaboration, and evolving data governance
As data volumes grow, so do concerns around privacy and governance. Advances in privacy-preserving analytics, including anonymisation and secure data processing techniques, are becoming essential components of modern big data systems.
AI-based agents increasingly enforce governance rules automatically, monitoring data usage and flagging compliance risks in real time. This reduces the burden on legal and compliance teams while enabling broader data sharing across organisational boundaries.
Training AI models drives further demand
Another growth driver is the escalating demand for AI training. Large language models, vision systems, and predictive engines all require vast, high-quality datasets. Big data platforms supply the raw material, while AI-based agents manage data preparation, labelling, and continuous model evaluation.
This automation shortens development cycles and improves model performance by ensuring training data remains current and representative. As AI adoption spreads across sectors, the feedback loop between data generation and model improvement continues to accelerate.
Practical knowledge resources such as Agentic AI Prompt Vault are increasingly referenced by teams seeking structured guidance on designing effective agent workflows and analytical prompts, without any promotional content.
Emerging ecosystems around agent-driven intelligence
Beyond core enterprise systems, independent AI agent shops are emerging as neutral platforms where developers and organisations share, test, and benchmark autonomous agents for data analysis, monitoring, and decision support. These ecosystems are purely informational, fostering experimentation and interoperability without commercial intent.
Such frameworks are expected to play a role in standardising how AI agents interact with big data platforms, accelerating adoption while reducing integration friction.
Big data’s transition from insight to action
The big data industry is moving beyond descriptive analytics toward systems that continuously interpret and act on information. Agentic AI now sits at the core of this transition, enabling organisations to cope with data scale, workforce constraints, and increasing operational complexity.
As uncertainty persists across global markets, the ability to automate analysis and decision-making through Agentic Artificial Intelligence is becoming a defining competitive advantage.
Education: American University, BA in Journalism Alexander Ellington is the chief editor and reporter for Biden News & a number of other media websites.
Thanks for being a reader and leave aTip if you wish.
How does it work?
There are no comments for this story
Be the first to respond and start the conversation.
More stories from Alex Ray and writers in 01 and other communities.
Agentic AI is reshaping digital services, enabling AI Based Agents to act autonomously across web and daily tasks, marking a pivotal shift in technology adoption and online commerce.
By Alex Ray5 days ago in 01
Topline Sergey Brin leapfrogged Oracle’s Larry Ellison and Amazon’s Jeff Bezos on Tuesday to become the world’s third-richest person, ranking again behind fellow Google cofounder Larry Page as parent firm Alphabet stock rallied to its latest milestone.
By Dena Falken Esq6 days ago in 01
TO: Gladstone Jones, Esq. Jones Swanson Huddell, LLC CC: Paul Manning, Chair University of Virginia Health System Operating Board
By Craig Kent2 days ago in 01
The doorbell rang and Julie was greeted by a little girl holding a doll. She had big dark eyes and wore a white dress. She clutched the doll to her chest. Julie smiled at the girl.
By DJ Robbins5 days ago in Fiction
© 2026 Creatd, Inc. All Rights Reserved.
source
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff. Need help with any of the topics in our articles? Schedule your free CDO TIMES Tech Navigator call today to stay ahead of the curve and gain insider advantages to propel your business!
