Descriptive Analytics Predictive Modeling Risk Technology 
Rocket-Powered Data Science
OCTOBER 6, 2023
In these applications, the data science involvement includes both the “learning” of the most significant patterns to alert on and the improvement of their models (logic) to minimize false positives and false negatives. Broken models are definitely disruptive to analytics applications and business operations.
CIO Business Intelligence
JUNE 7, 2022
The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.
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Birst BI
OCTOBER 30, 2018
A personal crystal ball that predicts your days ahead is what financial services firms everywhere want. 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? Will this next trade return a profit?
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. Finances: can Iower financial risk? Let’s see a conceptual definition of the two.
Jet Global
MAY 1, 2023
BI is a set of independent systems (technologies, processes, people, etc.) And Manufacturing and Technology, both 11.6 The Hitchhiker’s Guide to Embedded Analytics Download Now Section 2: Embedded Analytics: No Longer a Want but a Need Find out how major shifts in technology are driving the need for embedded analytics.
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