Capital One’s Post

Building a robust data management foundation is crucial to harnessing the full potential of the AI revolution. By ensuring data quality, accessibility, and governance, organizations can unlock valuable insights, drive innovation, and make informed decisions in this rapidly evolving landscape. #DataManagement #AIRevolution #DataStrategy #DataInnovation #AI

The AI revolution shows no signs of slowing down, transforming industries at an unprecedented pace. To stay competitive, organizations must build scalable data ecosystems that provide a foundation for strong data management, which is necessary to handle the complexity of data inherent with the AI revolution. Sponsored by Capital One Learn More: https://lnkd.in/erR82K4H

"To address these (data management) challenges, organizations should focus on building data products, processes, and platforms that ensure data is well-governed, yet accessible and usable for those who need it. ... this has meant a focus on three key principles: standardization, automation, and centralization." "The success of any AI initiative also requires high-quality data with context. Simply put, AI is only as good as the data fueling it, and the ability of users and models to understand it." "Perhaps the most important ingredient in building and maintaining an ecosystem full of high-quality data is establishing a curious and data-driven culture. Encouraging continuous learning and upskilling among product leaders and data managers is crucial in having talent that is abreast of the latest technologies and best practices"

This is a great article about managing data and what Capital One is doing to leverage AI. I just had a talk about this yesterday concerning Supply Chain Optimization and found this article very intersting so wanted to share.

Thinking about modernizing #datamanagement for #AI use cases? Here's a useful article from Wesley Hasting of Capital One. Lots of useful tidbits in this post. But two stood out to me. First, the reworking of data management tools to allow for modular deployments in a data-fabric-esque deployment is smart. Second, the relentless focus on automating data management with #machinelearning & #AI. No one WANTS to manually manage growing volumes of data. It's a necessary evil. If you can fully automate it, you can put your resources to work on newer, more valuable tasks. Like building AI models and co-pilots to solve key business needs. https://lnkd.in/gP2FCt7N

Useful intel on Capital One's data modernization program. If you are interested in modernizing data management for the #AI era, check out out upcoming webinar on September 24 - https://lnkd.in/gp_hbDJG Sachin Wadhwa & David Corrigan will discuss three practical, cost-effective and quick data modernization projects you can plan for 2025 without breaking your budget!

Data powers AI success. Advanced data maturity has a multiplier effect on AI programs. The interesting corollary that I didn't highlight in this article is we're seeing initial success in AI experiments with Caylent customers drive the value proposition that justifies investment in advancing data maturity. Building data maturity and impactful AI programs in parallel is a virtuous cycle that amplifies the business outcome and delivers results much more quickly than a serial approach. https://lnkd.in/g8MA5aZW

We have waited for this for decades: Having an AI algorithm defining acceptable #dataquality ranges for attributes and adapting as your data evolves. Learn how this is done by one of our technology partners here: https://lnkd.in/dZrUZJrV

Based on your data having #ai helping with setting data quality ranges as explained below is great. In the context of Product Data Lake this extends to the data in your ecosystem of business partners. Then it becomes greater than great.

In the latest issue of Big Data Quarterly, Appian's Adam Glaser shares insights on how leading enterprises are transforming their data infrastructure to support AI, and why data fabric is a critical component of this transformation.