This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
And, while change at large organisations is tough, data leaders would be wise to reframe such transformations as business opportunities rather than burdens. The business opportunity: Datagovernance exposes inefficiency. However, these manual steps weren’t transparent until active datagovernance required it.
I recently taught an online class on BCBS 239: Effective Risk Data Aggregation and Reporting for Risk.net. Preparing the course materials took me back to 2007-2008, when I worked for Merrill Lynch managing the Credit Risk Reporting team.
The abundance of data systems has also made the monitoring of complicated tasks even more challenging. Datagovernance practices Datagovernance is a data management system that adheres to an internal set of standards and policies for the collection, storage, and sharing of information.
In recent years, this new learning paradigm has been successfully adopted to address the concern of datagovernance in training ML models. This allows you to train an ML model on distributed data, without the need to share or move it. in Electrical Engineering and Computer Sciences from UC Berkeley in 2008.
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content