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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

The hype around large language models (LLMs) is undeniable. But heres the question I keep asking myself: do we really need this immense power for most of our analytics? Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. They leverage around 15 different models.

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. What are the benefits of business analytics? What is the difference between business analytics and business intelligence? Business analytics techniques.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI).

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What Is The Difference Between Business Intelligence And Analytics?

datapine

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. A fundamental differentiation factor is in the method each of them uses as a base.

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics. Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Today, the most common usage of business intelligence is for the production of descriptive analytics. . Descriptive Analytics: Valuable but limited insights into historical behavior. The vast majority of financial services companies use the data within their applications for what is called “ Descriptive Analytics.”

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Python for Business: Optimize Pre-Processing Data for Decision-Making

Smart Data Collective

Besides, it offers data model creation, systematized data sets, developable web services, ML-powered algorithms, versatile use of data mining and so many other very efficient functionalities that make it very flexible and productive to use for Data Preprocessing. Why Choosing Python Over Other Technologies in FinTech?