datapine

MARCH 31, 2022

BI Data Scientist. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis. SAS BI: SAS can be considered the “mother” of all BI tools.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

bridgei2i

NOVEMBER 25, 2020

This will also minimize the risk involved in the process. Auto-tagging, routing, organizing, adding data to relevant files, removing duplicate data, providing options to filter and sort the data to create different reports, providing data security, assisting with risk management and data compliance, etc.

Enterprise 52

Enterprise Data mining Consulting Risk Management 52

bridgei2i

JULY 28, 2020

She’s the founder and CEO of StatWeather, a company, which was recognized as number one in climate technology globally in the year, 2017, by the Energy Risk Awards. So, then we need systems, analysts, database administrators, people who can set in place, these types of backup systems for risk management. Not just that.

Enterprise 98

Enterprise Digital Transformation Insurance B2B 98

FineReport

MARCH 24, 2024

Their role extends to managing information for corporate decision-making, improving reporting systems , and performing complex analyses. Additionally, they facilitate organizational risk assessments, provide consulting services to leadership, and mentor junior analysts. JPMorgan Chase & Co.:

Statistics 52

Statistics Data mining Visualization Reporting 52

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? Crisis management and risk management: Text mining serves as an invaluable tool for identifying potential crises and managing risks.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

BizAcuity

MARCH 31, 2022

85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams.(Source: AI Adoption and Data Strategy. AI is used for investments, automating accounting, fraud detection, claims prediction, credit scoring and risk profiling among others. Source: Gartner Research). Source: PwC).

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

CIO Business Intelligence

MARCH 13, 2024

La base imprescindibile restano i big data, perché il machine learning ha bisogno di dataset molto estesi. Un altro campo di applicazione, ovviamente, è quello della gestione del credit risk, sempre basato sull’analisi di dati storici”. Perché l’IA Generativa è così “importante”?

Machine Learning 105

Machine Learning Deep Learning Big Data Testing 105

datapine

AUGUST 29, 2022

An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

IBM Big Data Hub

DECEMBER 15, 2023

While open-source AI offers enticing possibilities, its free accessibility poses risks that organizations must navigate carefully. Biased training data can lead to discriminatory outcomes, while data drift can render models ineffective and labeling errors can lead to unreliable models. Governments like the U.S.

Deep Learning 64

Deep Learning Experimentation Machine Learning Risk 64

Jet Global

MAY 9, 2024

This data is transformed, cleansed, and loaded into a data lake or warehouse for analysis. Finance organizations can then leverage advanced analytics and machine learning applications to gain valuable insights for strategic planning and risk management.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52