Draft Recommendations for The Development of the Net-Zero Data Public Utility

whitePaper | December 29, 2022

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

whitePaper | December 12, 2022

In August 2022, the California Office of the Attorney General announced a $1.2 million settlement with international cosmetic retailer Sephora for violations of the California Consumer Privacy Act. Following this announcement came a wave of questions about the regulator’s emphasis on the broad interpretation of the CCPA’s “sale of data” and adoption of the Global Privacy Control.

whitePaper | May 18, 2021

Next-gen data operations require next-gen data technologies and strategies and Azure is an ideal platform for this modernization to take place. Download this white paper to better understand the strategies, philosophies, and technologies your organization needs to expand analytics abilities, reduce technical debt, embrace AI, and more.

whitePaper | September 28, 2022

By building smarter apps and increasing the speed of business insight, application-driven analytics gives you the opportunity to out-innovate your competitors and improve efficiency. However, it does require a shift in the way we think about and deliver analytics.

whitePaper | July 1, 2022

Data is an extremely valuable asset in today’s world. After all, there are entire industries based on it. Because of this, it’s important to evangelize the value of data within your organization.

whitePaper | May 27, 2021

In many organizations, we need more collaboration between businesses, analytic teams, application developers and IT operations. These teams often work with data in silos and end up duplicating efforts, failing to integrate, or missing opportunities to deliver value from data. In addition to siloed efforts, data scientists are also faced with ever-increasing volumes and speeds of data. And the reality is they’re expected to answer questions just as fast as - or faster than - before. It’s important that we’re using the right data and the right techniques to ensure optimal outcomes. Download this guide to learn more about SAS and open source.