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. In business analytics, this is the purview of business intelligence (BI). It is frequently used for risk analysis.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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. Descriptive analytics: Assessing historical trends, such as sales and revenue.

Statistics 52

Statistics Data mining Visualization Reporting 52

Birst BI

OCTOBER 30, 2018

Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Today, the most common usage of business intelligence is for the production of descriptive analytics. .

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

FineReport

APRIL 26, 2023

The Big Data ecosystem is rapidly evolving, offering various analytical approaches to support different functions within a business. Descriptive Analytics is used to determine “what happened and why.” ” This type of Analytics includes traditional query and reporting settings with scorecards and dashboards.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. Data visualization and visual analytics are two terms that come up a lot when new and experienced analytics users alike delve into the world of data in their quest to make smarter decisions.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

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

Domino Data Lab

JUNE 2, 2019

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. Clearly in these situations, becoming model-driven addresses an existential risk and that’s the point Nick Elprin made in his keynote. Because of compliance.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110