Level: Beginner to Intermediate
Today, data and analytics teams are delivering a wider variety of data-driven products than ever before. Our architectures must respond to growing demands as we generate, acquire, inspect and analyze data; execute machine learning models and learn behaviors; and package results through visualizations, data stories, and dashboards for maximum impact. At the same time, much of our data’s journey has not yet moved to the cloud.
For decades, we have striven to establish a central data repository for the enterprise to manage these challenges. As we have struggled to achieve success, the data warehouse is often assigned blame, and falls in priority. But is it to blame? How do we know when it is helping and when it is not? How do we identify areas that need improvement?
We have transitioned from being data-driven to becoming data-centric. To be successful, we must practice what we preach: measure success. That starts with measuring our data platform—the data warehouse—which today includes the data lake, operational data sources, third-party feeds, external references, the machine learning repository, and analytics. But simple measurement is not enough. We also need to apply feedback and drive improvement in areas where we do not score.
In this course, you will learn to apply the framework of objectives and key outcomes (OKR) to the world of data and how its use drives positive results. Learn to maximize benefits from known pieces, locate and improve the unknowns, leverage the lessons learned, and improve your data outcomes. This measurement approach allows you to move beyond simple governance towards new efficiencies, increased effectiveness, and improved maturity.
You Will Learn
- How the data warehouse in the cloud is a new paradigm
- The workflow and components of the data journey
- Stages of workflow in the data journey (what we do)
- Measurement of workflow outcomes (what we delivered)
- Feedback review (did we do well)
- Improvement – deploying Github
- Measuring the success and maturity movement
All data practitioners, including:
- Data strategists and data architects
- Data engineers and pipeline engineers
- Developers of BI and analytics solutions
- Managers of development and operations processes
- Data modelers and database administrators
- Data governance leadership and data stewards