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
Data readiness and governance are critical for AI success Getting these foundational aspects of AI governance in place will be critical to successful adoption, and for unlocking an opportunity that the Tech Council of Australia estimates could contribute $45 billion to $115 billion per year to the Australian economy by 2030.
Synthetic data will be invaluable for avoiding privacy violations in the future, and Gartner predicts that by 2025, synthetic data will enable organizations to avoid 70% of privacy violation sanctions. Gartner predicts that by 2030, synthetic data will completely overshadow real data in AI models. DataGovernance.
Europe’s Digital Decade declaration targets for 2030 outline the digital rights and principles complementing data protection, privacy legislation and other rights. This includes Principle 4, “citizens able to engage and have control over their own data” (including their health data).
AI pioneer Andrew Ng recently underscored that robust data engineering is foundational to the success of data-centric AI —a strategy that prioritizes dataquality over model complexity.
Start with data as an AI foundation Dataquality is the first and most critical investment priority for any viable enterprise AI strategy. Data trust is simply not possible without dataquality. A decision made with AI based on bad data is still the same bad decision without it.
We organize all of the trending information in your field so you don't have to. Join 42,000+ 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