Marketing analytics and data science

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.

As organisations continue to disrupt their industries through better use of data, it has never been more crucial for business leaders to understand how data in and around the organisation can transform business models. The Data & Analytics Summit will bring together senior data and analytics leaders from across different industries, for a strategic conversation around how businesses can leverage the latest tools to manage large amounts of diverse information faster, improve processes and achieve wider business objectives.

PASS Data Community Summit is the go-to conference for data professionals to connect, share, and learn with peers and industry leaders. Summit happens in the Fall in Seattle, WA, and runs over 5 days, November 13-17. There are two days of Pre-Cons on Monday and Tuesday, and three days for the main conference Wednesday through Friday. Find out more about key timings for the week.

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.