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. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

SEPTEMBER 15, 2023

These requirements will likely mandate publicly traded companies to disclose their greenhouse gas (GHG) emissions footprint, climate-related goals, and progress, as well as climate-risk related financial impact and expenditures. Furthermore, companies would need to disclose the price and rationale for internal carbon prices.

Reporting 72

Reporting Risk Data-driven Publishing 72

Smart Data Collective

SEPTEMBER 25, 2019

However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely. If you trust the data, it’s easier to use confidently to make business decisions.

Technology 102

Technology Statistics Strategy Insurance 102

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 98

Management Risk Measurement Optimization 98

Alation

MARCH 14, 2023

Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence. Creating a clear process with documented steps will help.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Ontotext

JULY 6, 2023

By promptly identifying and addressing risks, it enhances operational resiliency and enables proactive risk management. The solution also reduces incident response times, optimizes processes and streamlines asset management. First of all, it helps bridge the gap between business abstracts and technical realities.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

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

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 Machine Learning Data-driven 66

datapine

AUGUST 29, 2022

Due to this book being published recently, there are not any written reviews available. 4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. The subsequent chapters focus on predictive and descriptive analysis.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

MAY 9, 2024

ETL pipelines are commonly used in data warehousing and business intelligence environments, where data from multiple sources needs to be integrated, transformed, and stored for analysis and reporting. Data pipelines enable data integration from disparate healthcare systems, transforming and cleansing the data to improve data quality.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

CIO Business Intelligence

APRIL 22, 2025

However, IT must now shift from a support function to a strategic driver of growth, aligning priorities and goals with the broader organizational strategy according to an article published in Exclaimer. To succeed in todays fast-paced business landscape, IT leaders must skillfully blend innovation with strategic risk management.

Technology 106

Technology Uncertainty Experimentation Risk Management 106

CIO Business Intelligence

MAY 14, 2025

AI initiatives often need centralized data lakes, while domain-driven models emphasize decentralized ownership. Ensuring data quality, lineage and compliance becomes more challenging as data is federated across domains but consumed centrally by AI models. On-demand data access. Complex governance. Want to join?

Enterprise 115

Enterprise Data Architecture Data-driven Data Quality 115