CIO Business Intelligence

JUNE 7, 2022

What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI). Diagnostic analytics uses data (often generated via descriptive analytics) to discover the factors or reasons for past performance. Data analytics methods and techniques.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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

FineReport

MARCH 24, 2024

Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnostic analytics: Uncovering the reasons behind specific occurrences through pattern analysis.

Statistics 52

Statistics Data mining Visualization Reporting 52

BizAcuity

MARCH 31, 2022

of organizations who participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. By 2025, AI will be the top category driving infrastructure decisions, due to the maturation of the AI market, resulting in a tenfold growth in compute requirements. AI in Marketing.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Rocket-Powered Data Science

OCTOBER 6, 2023

There are other dimensions of analytics that tend to focus on hindsight for business reporting and causal analysis – these are descriptive and diagnostic analytics, respectively, which are primarily reactive applications, mostly explanatory and investigatory, not necessarily actionable.

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

Jet Global

MAY 1, 2023

Section 2: Embedded Analytics: No Longer a Want but a Need Section 3: How to be Successful with Embedded Analytics Section 4: Embedded Analytics: Build versus Buy Section 5: Evaluating an Embedded Analytics Solution Section 6: Go-to-Market Best Practices Section 7: The Future of Embedded Analytics Section 1: What are Embedded Analytics?

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52