Tag: Data-Driven Culture

We’ve heard that companies must become data-driven. They must treat data as an asset, govern it, improve its quality, and make it easily available across the enterprise. Perhaps these pronouncements are becoming tiresome. But really they understate the change in how we regard data and compute and their relationship. IT has always overseen both data […]

AWS recently partnered with Harvard Business Review Analytic Services to understand the current state of agentic AI in organisations.1 The results were exciting and informative: While expectations are high, the path to value at scale has yet to be discovered. Outlined below is what we found creates the gap between appreciating AI’s importance and using […]

Harvard Business Review Analytic Services has conducted sponsored research to gain insight into how organizations are currently using data, with a particular focus on its application to generative AI. The resulting report, titled “Scaling Generative AI for Value: Data Leader Agenda for 2025,” examines trends across various industries and companies worldwide. Prior sponsored research with […]

The Data Hoarding Trap Many organizations are guided by the adage “Knowledge is power.” Teams closely guard their data to maintain an internal competitive edge. But what if this mentality is holding your company back? Imagine your marketing team has gathered valuable data on customer preferences but fails to share that information with your product […]

The exponential growth of enterprise data presents unprecedented opportunities for innovation, yet many organizations struggle to capitalize on it due to inadequate data governance. A robust governance framework is crucial for future-proofing and maintaining competitiveness. Effective data governance rests on four pillars: Data visibility: Clarify available data assets to inform decision-making. Access control: Balance accessibility […]

Balance security with innovation – seamlessly integrate compliance controls behind intuitive self-service capabilities to empower data-driven productivity and agility.

Automate tedious tasks, gain faster insights through conversational analytics, and innovate using synthetic data – accelerating time-to-value from data assets.

Maximize AI’s value without breaking the bank – this guide explores cost-optimization strategies across the AI lifecycle, from model selection and data management to FinOps practices.

Unlocking innovation demands aligning talent strategy with organizational aspirations; an oil company’s overlooked compensation model discouraged risk-taking, undermining its transformation goals until leadership revised employee stock rewards.

Data powers groundbreaking generative AI; curate diverse, high-quality datasets to cultivate innovation, while mitigating risks and biases.