Descriptive Analytics Machine Learning Metrics 
CIO Business Intelligence
JULY 5, 2022
Business analytics and business intelligence (BI) serve similar purposes and are often used as interchangeable terms, but BI can be considered a subset of business analytics. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward. Business analytics techniques.
Smart Data Collective
MARCH 29, 2021
Get started by focusing on these four insights and metrics. Many businesses restrict themselves to descriptive analytics, or what’s described above as knowing what your customers have already done. In recent years, though, there’s been significant growth in the use of predictive analytics. Highlight CLV.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
datapine
MARCH 25, 2022
While BI tells you what has happened in the past and what is happening now (descriptive analytics), BA tells you what will happen in the future (predictive analytics). Descriptive analytics : As its name suggests, this analysis method is used to describe and summarize the main characteristics found on a dataset.
CIO Business Intelligence
JANUARY 20, 2023
The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs. Business intelligence vs. business analytics Business analytics and BI serve similar purposes and are often used as interchangeable terms, but BI should be considered a subset of business analytics.
datapine
JANUARY 31, 2022
Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was. BI dashboards like the one presented below provide a centralized view of the most important metrics businesses need to stay ahead of their competitors.
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.
Domino Data Lab
JUNE 2, 2019
Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.
Expert insights. Personalized for you.
136 
Let's personalize your content