Webinar On-Demand
Watch this fireside discussion between Phil Dale, Marks & Spencer, and GT Volpe, Alation, on the pivotal themes of data strategy, data leadership, and trusted data for critical data and AI initiatives. Discover how Marks & Spencer navigates the evolving data landscape, ensuring that data-driven decision-making is at the core of their business strategy.
This webinar for anyone passionate about transforming data into actionable insights and who recognizes data’s indispensable role in decision-making and competitive differentiation. Learn how a best-in-class organization like Marks & Spencer does it!
This webinar covers:
The strategic approaches and best practices that shape effective data leadership within organizations
The significant role of data leadership in aligning data initiatives to outcomes and measuring the value to your organization
Building a culture around data within an organization, maintaining trusted data, and active governance when planning and executing critical data initiatives
Alation Brief
Most of us are familiar with Google Maps, which shows the path to our destination with specific and timely directions from start to finish. Layers provide additional information like points of interest and traffic details such as construction or traffic volume ahead of time, which can help change our route and save time. The user experience makes it easy for anyone to use.
Alation Brief
Learn how Alation Anywhere brings the power of data intelligence directly to your fingertips - right in your browser. Our integrations now extend beyond Excel, Teams, Slack, Tableau, and Google Sheets to include Chrome. With the Alation Anywhere Chrome Extension, you can search, preview, and trust critical data without leaving Chrome, enabling faster, more informed decisions across your organization and seamlessly fitting into your workflow.
Webinar On-Demand
In today’s rapidly evolving digital landscape, effective data governance is critical for organizations striving to manage diverse and numerous data types. Ensuring the trustworthiness of AI/ML models requires robust governance of both inputs and outputs.