Data & Data Culture

Making your organization fit for data, enhancing value with nontraditional stakeholders, and supporting working parents.

New research highlights nine key factors impeding organizations’ ability to advance their data science progress.

New value creation with strategic data assets, in-store shopping to build customer loyalty, and habits to enable enduring culture change.

To monetize data, companies must first transform it so it can be reused and recombined to create new value.

Six strategies can help guide data science teams toward greater success in cross-unit projects.

The data science management process, job moves for pay equity, and political concerns in M&As.

In this webinar, speakers discuss the benefits and pitfalls of monitoring in-person and remote workers.

Moderna’s chief data and AI officer explains how AI helped the pharma company develop a COVID-19 vaccine in record time.

Organizations should manage data science with an appropriate structure and enterprisewide process.

Many organizations have challenges with deploying AI. Wealth management is a clear exception.

There’s power in integrating subscription-based software, platform marketplaces, and machine learning.

Elizabeth Renieris of the Notre Dame-IBM Technology Ethics Lab discusses how businesses can responsibly govern AI projects.

PepsiCo’s Colin Lenaghan discusses AI’s role in the company’s pricing strategy and ongoing digital transformation.

A dedicated team can help maximize the utility — and competitive advantage — of automation systems.

Home Depot’s Huiming Qu discusses how the home improvement retailer leverages AI to improve the customer experience.

The Me, Myself, and AI podcast delves into how automotive supplier Cooper Standard uses open innovation to leverage AI.

Dramatic changes within the life sciences industry present unique opportunities to use AI.

JoAnn Stonier, chief data officer at Mastercard, discusses how design thinking enables better AI implementation.

To make data migration more effective, start with a minimum set of viable data, leave out nice-to-have data, and weigh speed vs. quality.

Amit Shah, president of 1-800-Flowers, explains why lifelong learners are the best technical talent.