Smart Data Collective

JUNE 19, 2019

As such, you should concentrate your efforts in positioning your organization to mine the data and use it for predictive analytics and proper planning. The Relationship between Big Data and Risk Management. Tips for Improving Risk Management When Handling Big Data. Vendor Risk Management (VRM).

Risk Management 71

Risk Management Risk Management Big Data 71

IBM Big Data Hub

FEBRUARY 28, 2024

Successful strategic sourcing often results in process optimization, cost management, customer satisfaction, risk management , increased sustainability and other benefits. Sourcing teams are automating processes like data analysis as well as supplier relationship management and transaction management.

Strategy 104

Strategy Technology Cost-Benefit Risk Management 104

FineReport

APRIL 9, 2024

Benefits of Utilizing BI Tools The utilization of data analysis tools such as business intelligence software offers numerous benefits for organizations seeking to gain a competitive edge in today’s dynamic market landscape. This results in optimized resource utilization and cost efficiencies while enhancing overall productivity.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

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

Cloudera

JUNE 29, 2023

Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs. Enhance counterparty risk assessment. Use ML to more realistically model and simulate stress scenarios.

Data Architecture 83

Data Architecture Risk Management Risk Management 83

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