Unstructured data has been a significant factor in data lakes and analytics for some time. Twelve years ago, nearly a third of enterprises were working with large amounts of unstructured data. As I’ve pointed out previously, unstructured data is really a misnomer. The data is structured; it's just not structured into rows and columns that fit neatly into a relational table like much of the other information enterprises process. Consequently, it requires different skills, different technology...
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Topics:
Artificial intelligence,
Analytics & Data,
AI and Machine Learning,
Computer Vision
The increasing importance of intelligent operational applications driven by artificial intelligence (AI) is blurring the lines that have traditionally divided the requirements between operational and analytic data platforms. Operational data platforms have traditionally been deployed to support applications targeted at business users and decision-makers to run the business, with analytic data platforms typically supporting applications used by data and business analysts to analyze the business.
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Topics:
embedded analytics,
Cloud Computing,
Analytics & Data,
operational data platforms,
Analytic Data Platforms,
AI and Machine Learning
We’ve been saying for years that natural language processing (NLP) and natural language analytics would greatly expand access to analytics. However, prior to the explosion of generative AI (GenAI), software providers had struggled to bring robust natural language capabilities to market. It required considerable manual effort. Many analytics providers had introduced natural language capabilities, but they didn’t really resonate with enterprise requirements. They required significant effort to...
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Topics:
business intelligence,
Artificial intelligence,
natural language processing,
Analytics & Data,
AI and Machine Learning,
GenAI
In recent years, many enterprises have migrated data platform workloads from on-premises infrastructure to cloud environments, attracted by the promised benefits of greater agility and lower costs. The scale of cloud data platform adoption is illustrated by Ventana Research’s Data Lakes Dynamic Insights research: For two-thirds (66%) of participants, the primary data platform used for analytics is cloud based. As the quantity and importance of the data platform workloads deployed in the cloud...
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Topics:
business intelligence,
Cloud Computing,
data operations,
robotic automation,
Analytics & Data,
Analytic Data Platforms,
AI and Machine Learning
Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.
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Topics:
embedded analytics,
Business Intelligence,
Data Governance,
Data Management,
natural language processing,
data operations,
Process Mining,
Streaming Analytics,
Analytics & Data,
Streaming Data & Events,
operational data platforms,
Analytic Data Platforms,
AI and Machine Learning
Ventana Research recently announced its Market Agenda in the expertise area of Customer Experience. CX has emerged as a way for enterprises to demonstrate value and stand out in the marketplace. The technology underlying modern CX is transitioning from tools that are based on communication to those centered on data analysis and process automation. No technology has had as dramatic impact as quickly as Generative AI, which has upended the industry. It allows enterprises to build great...
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Topics:
Customer Experience,
Voice of the Customer,
Self-service,
Analytics,
Contact Center,
agent management,
Customer Experience Management,
Field Service,
AI and Machine Learning
Imagine a world where artificial intelligence (AI) seamlessly integrates into every facet of your business, only to subtly distort your data and skew your insights. This is the emerging challenge of AI hallucinations, a phenomenon where AI models perceive patterns or objects that do not exist or are beyond human detection.
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Topics:
Digital Technology,
AI and Machine Learning
Discussion about potential deployment locations for analytics and data workloads is often based on the assumption that, for enterprise workloads, there is a binary choice between on-premises data centers and public cloud. However, the low-latency performance or sovereignty characteristics of a significant and growing proportion of workloads make them better suited to data and analytics processing where data is generated rather than a centralized on-premises or public cloud environment. ...
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Topics:
Cloud Computing,
Internet of Things,
Data,
Digital Technology,
Analytics & Data,
operational data platforms,
Analytic Data Platforms,
AI and Machine Learning
Sales and operations planning (S&OP) is trending toward becoming more strategic in product-centric companies through the end of the decade. The purpose of S&OP has grown in importance. Since the mid-teens, the trade and economic environment has become less benign and more unpredictable, forcing many enterprises to redesign their supply chains for resiliency while still surmounting the dual challenges of remaining cost competitive and achieving financial targets. Over the past decade, there have...
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Topics:
Continuous Planning,
Operations & Supply Chain,
Enterprise Resource Planning,
digital finance,
Sustainability Management,
AI and Machine Learning
The phrase ‘big data’ may have largely gone out of fashion, but the concept of storing and processing all relevant data continues to be important for enterprises seeking to be more data-driven. Doing so requires analytic data platforms capable of storing and processing data in multiple formats and data models. This will be an important focus for the forthcoming Data Platforms Buyer’s Guide 2024.
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Topics:
Analytics,
Business Intelligence,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data,
AI and Machine Learning