Remove Descriptive Analytics Remove Machine Learning Remove Unstructured Data
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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. You get the picture.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Additionally, the Python ecosystem is flush with open source development projects that maintain the language’s relevancy in the face of new techniques in the field of data science. It’s worth noting that there is a landscape of proprietary tools dedicated to producing descriptive analytics in the name of business intelligence.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

When BI and analytics users want to see analytics results, and learn from them quickly, they rely on data visualizations. Analytics acts as the source for data visualization and contributes to the health of any organization by identifying underlying models and patterns and predicting needs.

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10 Best Big Data Analytics Tools You Need To Know in 2023

FineReport

The value of Big Data is not solely dependent on the volume of data available, but on how it is utilized. The Big Data ecosystem is rapidly evolving, offering various analytical approaches to support different functions within a business. Descriptive Analytics is used to determine “what happened and why.”

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Data trust and the evolution of enterprise analytics in the age of AI

CIO Business Intelligence

According to Gartner , lack of data management practices and rigor around governance can introduce risk and significantly impede data and analytics strategic readiness and ultimately AI readiness. This capability has become increasingly more critical as organizations incorporate more unstructured data into their data warehouses.

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Your data’s wasted without predictive AI. Here’s how to fix that

CIO Business Intelligence

Descriptive analytics: Where most organizations begin and linger Descriptive analytics answers the question: What happened? These are your standard reports and dashboard visualizations of historical data showing sales last quarter, NPS trends, operational thoughts or marketing campaign performance.