Logical data warehouse’, ‘data fabric’, and ‘data mesh’ are just a few of the names for new modern data architecture paradigms that are being promoted as the way forward. These new architectures are focused on internal data sources across disparate data warehouses, data lakes, databases, data marts, and files located both on-premise and the cloud. When considering how to modernize the data architecture within an organization, it’s essential to include external data sources, especially for analytics and machine learning use cases. Overlooking external data and its value is a missed opportunity as it provides important context not always captured in internal data: economic trends, consumer preferences, weather, reviews, social media trends, competitive intelligence, and more. Organizations that are leveraging external data in their analytics programs are outperforming their competitors and have improved customer acquisition, operational efficiencies, and risk & compliance management.
The management consulting firm McKinsey said that “A well-structured plan for using external data can provide a competitive edge”. Adding an external data platform as a component of your modern data stack is a competitive advantage and will boost the efficacy and performance of your analytics teams.
This latest Data Science Central webinar will outline the core components of modern architectures and why external data platforms are a new requirement.
Omer Har, Co-Founder & CTO - Explorium
Sean Welch, Host and Producer - Data Science Central