CIO Business Intelligence

MARCH 18, 2024

One such company has built a tool that predicts customer intent and behavior based on previous interactions and other market data. Our data team uses gen AI on Amazon cloud to explore sustainability metrics. A key focus of the bank’s AI team is likewise data quality.

Risk 133

Risk Cost-Benefit Data Processing Testing 133

FineReport

APRIL 9, 2024

One of the key aspects of the role of BI platforms is their ability to streamline the process of data analysis and decision-making. They offer functionalities that allow for the integration and transformation of raw data into meaningful and actionable insights.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Database design is often an important part of the business analyst role.

Statistics 97

Statistics Metrics Visualization Finance 97

AWS Big Data

AUGUST 14, 2023

Earlier in their lifecycle, data products may be measured by alternative metrics, including adoption (number of consumers) and level of activity (releases, interaction with consumers, and so on). Organizational governance for these data products typically favors availability and data accuracy over agility.

Management 76

Management Risk Measurement Optimization 76

DataRobot Blog

JULY 21, 2022

Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. If there is no forward-looking predictive component to the use case, it can probably be addressed with analytics and visualizations applied to historical data.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration. Or something.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66