The Hidden Costs of Poor Data Quality in AI

The Hidden Costs of Poor Data Quality in AI

Explore how poor data quality impacts AI by introducing errors, biases, and inefficiencies, undermining accuracy, scalability, and ROI.

By Data Science Connect

Date and time

Wednesday, November 12 · 11am - 12pm PST

Location

Online

About this event

  • Event lasts 1 hour

Data quality is the backbone of effective AI implementation, yet errors, biases, and inconsistencies in datasets often go unnoticed until they derail key projects. Join this illuminating webinar to uncover how poor data quality affects AI accuracy, scalability, and ROI.

Our panel of experts will share actionable insights into recognizing hidden pitfalls, establishing robust data governance frameworks, and ensuring your AI initiatives yield impactful results. This session is essential for professionals aiming to maximize the value of their AI investments while navigating common data challenges.


YOU'LL LEARN


1️⃣ Understanding Data Quality Risks: Learn how errors and biases in data undermine AI model accuracy and decision-making.
2️⃣ Strategies for Robust Data Governance: Discover practical approaches to ensuring data consistency and reliability.
3️⃣Optimizing ROI from AI Initiatives: Explore methods to enhance AI performance and scalability by improving input data quality.

Panelists to be announced soon

REGISTER HERE

Organized by

And we believe that this connectedness empowers us to use data science for a higher purpose that reaches beyond traditional networking.

By joining forces and connecting Atlanta's diverse and progressive collection of

  • corporations
  • people
  • universities
  • civic initiatives
  • disciplines + resources

We are more equipped to use data science to tackle the greater challenges, drive breakthrough results and realize a higher potential across our lives, work and society.

Data Science Connect (DSC) is the largest data science organization in the southeastern United States, and one of the most attended in the world.

Free