article thumbnail

AI adoption in the enterprise 2020

O'Reilly on Data

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

article thumbnail

Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

Its about investing in skilled analysts and robust data governance. This means fostering a culture of data literacy and empowering analysts to critically evaluate the tools and techniques at their disposal. It also means establishing clear data governance frameworks to ensure data quality, security and ethical use.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Data landscape in EUROGATE and current challenges faced in data governance The EUROGATE Group is a conglomerate of container terminals and service providers, providing container handling, intermodal transports, maintenance and repair, and seaworthy packaging services. Eliminate centralized bottlenecks and complex data pipelines.

IoT 106
article thumbnail

Mastering healthcare data governance with data lineage

IBM Big Data Hub

The healthcare industry faces arguably the highest stakes when it comes to data governance. For starters, healthcare organizations constantly encounter vast (and ever-increasing) amounts of highly regulated personal data. healthcare, managing the accuracy, quality and integrity of data is the focus of data governance.

article thumbnail

CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

Yet, while businesses increasingly rely on data-driven decision-making, the role of chief data officers (CDOs) in sustainability remains underdeveloped and underutilized. Collaborating with research institutions can improve ESG data methodologies while engaging with regulators ensures compliance with changing disclosure requirements.

IT 59
article thumbnail

Oracle Advances its AI-Enabled Supply Chain Management Suite

David Menninger's Analyst Perspectives

For example, in demand planning, predictive analytics can be applied to use historical sales data, market trends and seasonal patterns to predict future demand with greater accuracy and reduced bias. In line with our concept of the data pantry , the systems can unify data from disparate sources.

article thumbnail

Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

Building this single source of truth was the only way the airport would have the capacity to augment the data with a digital twin, IoT sensor data, and predictive analytics, he says. It’s a big win for us — being able to look at all of our data in one repository and build machine learning models off of that,” he says.