Level: Intermediate to Advanced
Prerequisite: None
Organizations often struggle to deliver production reporting with high reliability while at the same time creating new value propositions from their data assets. Gartner has observed that organizations that focus only on mode one (predictable) deployment of analytics in the construction of reliable, stable, and high-performance capabilities will very often lag behind the marketplace in delivering competitive insights because the domain is moving too fast for traditional SDLC methodologies.
Explorative analytics requires a very different model for identifying analytics opportunities, managing teams, and deploying analytics into production. Rapid progress in the areas of machine learning and artificial intelligence exacerbates the need for bimodal deployment of analytics. In this course, Stephen Brobst will describe best practices in both architecture and governance necessary to modernize an enterprise and enable participation in the digital economy. Emerging best practices in data mesh deployment will be discussed with examples.
You Will Learn
- The difference between mode one (reliable) and mode two (innovative) analytics deployment
- How to help mode one and mode two deployment styles coexist within a single enterprise
- Best practices in using agile DevOps to both create and deliver high-value analytics in compressed time frames
- How to use data mesh best practices for delivery
- How to use cloud deployment to accelerate innovation
Geared To
- Data architects
- Solution architects
- Big data architects
- BI analysts
- Business and data analysts
- Data warehousing and data integration specialists
- Business and IT leaders
- Analytics project managers
- Analytics program managers