Designing a Successful Governed Citizen Data Science Strategy

Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job. Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization

Rapid Response Analytics (RRA), an application suite that consists of two elements: curated, modular data kits called DOS™ Marts and Population Builder, a powerful self-service tool that lets any type of user, from physician executive to frontline nurses and population health teams explore their data and quickly build populations without needing to know how to write SQL and data science code. RRA increases an analytics team’s productivity by up to 10x and reduces its time to develop analytics by as much as 90 percent. Analysts can spend more time focusing on key strategic analysis and less time on repetitive tasks that can lead to inconsistent results and a backlog of requests. Learning Objectives: Discover how RRA is like a meal delivery kit that allows you to take components and customize them to quickly tailor and deliver meaningful insights.

Equifax analysis indicates that approximately 91.5 million consumers in the US either have no credit file, or have insufficient information in the file to generate a traditional credit score. Your visibility into credit applicants determines whether you will offer more loans or miss profitable opportunities. By giving you incre

Getting insights from observability data such as logs and metrics is essential for managing cloud services reliably. However, managing massive observability data volumes can be expensive and complex. Luckily observability data pipelines can help IT handle large data volumes and reduce costs by processing, routing, and filtering data across teams, tools, and storage options. Should you build your own or buy?