O'Reilly on Data
DECEMBER 12, 2019
In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Sensitivity analysis.
Cloudera
SEPTEMBER 7, 2023
The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
erwin
APRIL 4, 2019
Some other common data governance obstacles include: Questions about where to begin and how to prioritize which data streams to govern first. Issues regarding data quality and ownership. Concerns about data lineage. You can encourage feedback through surveys, workshops and open dialog.
datapine
FEBRUARY 22, 2022
Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7
Andrew White
JANUARY 11, 2021
What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. Risk Management (most likely within context of governance). Product Management. Saul Judah is our main person focusing on D&A risk management. Governance.
Expert insights. Personalized for you.
191 
Let's personalize your content