CIO Business Intelligence

FEBRUARY 6, 2025

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. This article reflects some of what Ive learned.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

CIO Business Intelligence

JULY 5, 2022

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.

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

CIO Business Intelligence

JUNE 7, 2022

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).

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

datapine

MARCH 25, 2022

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.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Domino Data Lab

JUNE 2, 2019

We had data science leaders presenting about lessons learned while leading data science teams, covering key aspects including scalability, being model-driven, being model-informed, and how to shape the company culture effectively. Data science leadership: importance of being model-driven and model-informed.

Data Science 110

Data Science Data-driven Machine Learning Modeling 110

Cloudera

MARCH 2, 2018

Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. Gain improved intelligence on operating context and needs through expanded use of descriptive analytics techniques.

Data-driven 43

Data-driven Prescriptive Analytics Machine Learning Descriptive Analytics 43