Rocket-Powered Data Science

OCTOBER 6, 2023

Decades (at least) of business analytics writings have focused on the power, perspicacity, value, and validity in deploying predictive and prescriptive analytics for business forecasting and optimization, respectively. How do predictive and prescriptive analytics fit into this statistical framework?

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

IBM Big Data Hub

JANUARY 25, 2024

Many managers in asset-intensive industries like energy, utilities or process manufacturing, perform a delicate high-wire act when managing inventory. 2 Unless your demand forecasting is accurate, adopting a reactive approach might prove less efficient. What’s at stake? trillion, up from USD 864 billion in 2019 to 2020.

Optimization 82

Optimization Cost-Benefit Prescriptive Analytics Manufacturing 82

CIO Business Intelligence

JUNE 7, 2022

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

IBM Big Data Hub

JULY 6, 2023

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. A manufacturer developed powerful, 3D-printed sensors to guide driverless vehicles. An e-commerce conglomeration uses predictive analytics in its recommendation engine.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

FineReport

APRIL 26, 2023

For example, a computer manufacturing company could develop new models or add features to products that are in high demand. Predictive Analytics assesses the probability of a specific occurrence in the future, such as early warning systems, fraud detection, preventative maintenance applications, and forecasting.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

BizAcuity

MARCH 31, 2022

85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: Not just banking and financial services, but many organizations use big data and AI to forecast revenue, exchange rates, cryptocurrencies and certain macroeconomic variables for hedging purposes and risk management.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Andrew White

JANUARY 11, 2021

As such banking, finance, insurance and media are good examples of information-based industries compared to manufacturing, retail, and so on. Some data is more a risk than valuable. What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? GI-GO of course we all know. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52