CIO Business Intelligence

JULY 5, 2022

Business analytics is a subset of data analytics. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more. What is the difference between business analytics and business intelligence?

Business Analytics 136

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 136

datapine

NOVEMBER 27, 2019

Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among the BI professionals, especially since big data is becoming the main focus of analytics processes that are being leveraged not just by big enterprises, but small and medium-sized businesses alike.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Rocket-Powered Data Science

JULY 6, 2021

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

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. Paired to this, we get a breakdown of the cash conversion cycle (CCC) for the past 3 years.

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

Sisense

AUGUST 11, 2019

BA is a catch-all expression for approaches and technologies you can use to access and explore your company’s data, with a view to drawing out new, useful insights to improve business planning and boost future performance. BA primarily predicts what will happen in the future. What About “Business Intelligence”?

Business Intelligence 75

Business Intelligence Business Analytics Analytics OLAP 75

Sisense

APRIL 10, 2020

These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Using XG-Boost to model the text data resulted in an almost identical score for Python and R. There are many performance metrics to evaluate performance of Machine Learning models.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

SEPTEMBER 19, 2023

This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.)

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76