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
A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.
Independent research firm Verdantix recently identified IBM as a leader in their report, “ Green Quadrant: ESG Reporting and Data Management Software ” (July 17, 2023), which evaluated and provided a detailed assessment of solution providers and their product offerings.
While most companies have historically published annual Environmental Social Governance (ESG) reports long after their annual financial statements, it is likely that the SEC will require companies to disclose ESGdata with financial statements. It is about accountability and driving comparability for real impact.
The variety and volume of data and the different types of data add additional complexity to an extremely complex real-world problem: how to optimize supply chain performance. In other words, you cannot use a generally trained model.
These are critical steps in ensuring businesses can access the data they need for fast and confident decision-making. As much as dataquality is critical for AI, AI is critical for ensuring dataquality, and for reducing the time to prepare data with automation.
this article for an explanation of the mental model for AI systems) AI opportunities arent created equal AI is often used to automate existing tasks, but the more space you allow for creativity and innovation when selecting your AI use cases, the more likely they will result in a competitive advantage.
We organize all of the trending information in your field so you don't have to. Join 15,000+ 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