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

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

Alation

AUGUST 17, 2022

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data? Enterprise risk management.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

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

This is due to a common misconception about data mesh as a data strategy, which is that it is effectively self-organizing—meaning that once presented with the opportunity, data owners within the organization will spring to the responsibilities and obligations associated with publishing high-quality data products.

Management 79

Management Risk Measurement Optimization 79

Domino Data Lab

FEBRUARY 4, 2019

In other words, your talk didn’t quite stand out enough to put onstage, but you still get “publish or perish” credits for presenting. 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. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Andrew White

JANUARY 11, 2021

This was for the Chief Data Officer, or head of data and analytics. Gartner also published the same piece of research for other roles, such as Application and Software Engineering. What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52