25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING

Welcome to Customer Analytics Summit, the most exciting data & customer insights event of the year! The Customer Analytics Summit is specifically tailored to unpack the issues that are most relevant to the data & customer insights community. You'll get the chance to hear from some of the most successful leaders in data & customer insights and learn how they've maximized data-driven, actionable insights to unlock extraordinary customer value. Time for you to join the fun, you won't want to miss this action-packed conference!

Data is the crown jewels of an organization. It empowers companies to make informed business decisions, improve operations, reduce costs and increase revenue. Organizations are increasingly adopting DataOps to get full visibility and control of their data pipeline, boost efficiency and quality of data, fuel digital transformation initiatives and maximize business value. At DataOps Day, we’ll take a deep dive into the world of data and DataOps. Join us to hear from data and business leaders who have adopted DataOps to achieve their goals for quality, security and speed.

Come to San Diego and participate in an interactive summit designed for business, data science, and IT leaders who are responsible for selecting, managing, and delivering value from analytics applications, AI/ML, business intelligence, and the data that fuels them. Organizations count on analytics to innovate, attract and retain customers, improve efficiency, and determine risk amid today’s unpredictable and fast-changing economic landscape. However, TDWI finds that many struggle to move ahead. Organizations face difficulties that lead to user frustration, errors, and higher costs. As your analytics models, workloads, and data pipelines grow in number and complexity, orchestrating the journey into production and operationalization is a challenge.

The avalanche of data generated by companies and their customers should be a competitive advantage, but most companies are struggling to manage it all. Data accessibility, quality, governance and security are all critical for operationalizing machine learning applications, so the stakes are high. Emerging frameworks are helping us come to grips with these issues but the answers go beyond just technology.