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What is data quality?

Data quality is about how well data meets certain standards like accuracy, completeness, consistency, timeliness, and relevance. In the context of data analytics, it helps determine whether the data is good enough to be used for analysis, reporting, or decision-making.
Data issues such as missing values, duplicates, or outdated information can lead to wrong conclusions and poor business decisions

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Artificial Intelligence is growing smarter and more capable. With agentic AI, machines don’t just wait for commands anymore. They take action on their own, choose what to do next, and complete tasks across different tools and systems. This shift is helping businesses work faster and more efficiently.

Discover how Agentic Al is transforming finance, call centers, healthcare, manufacturing, and IT with real-world use cases and practical examples.

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What is Agentic AI?

When people ask, “What is Agentic AI?”, the answer lies in its autonomy. Unlike rule-based systems or generative AI (which creates content), Agentic AI analyzes data, learns dynamically, and makes decisions without human intervention. For instance, while AI in automation might streamline workflows, Agentic AI goes further—predicting risks, personalizing customer interactions, and executing actions in real time.

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What is Generative AI in Data Analytics?

Generative AI refers to artificial intelligence systems that create new and original data by analyzing and learning patterns from vast datasets. Unlike traditional AI, which primarily processes and classifies information, generative AI can synthesize new insights, automate report generation, and provide contextual data-driven recommendations. Using deep learning models like transformers and GANs, it identifies patterns, detects anomalies, and generates real-time insights. Businesses can use AI for data analytics to automate report generation, refine data modeling, and run advanced scenario simulations. By integrating natural language processing (NLP), it simplifies data interpretation and makes analytics more accessible. With its ability to streamline workflows and enhance business intelligence, Generative AI in data analytics is reshaping the way organizations analyze and act on data.

What is Agentic AI Architecture

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Agentic AI architecture allows AI systems to set objectives, make choices, and take action without constant guidance, instead of being told what to do every step of the way.
In agentic AI architecture, the system perceives its environment, plans its next steps, remembers past experiences, and executes tasks, all with minimal human input. This makes the AI more goal-driven and adaptable.

For example, rather than just generating a report when asked, an agentic AI might identify a business objective, gather the right data, analyze it, and deliver useful insights without being told exactly how.
This architecture also scales to multi-agent systems, where several AI agents work together to solve more complex problems.