Level: Beginner to Intermediate
Prerequisite: None
Today, data, analytics, and machine learning teams are delivering a wider variety of data-centric products than ever before. Our architectures have evolved to respond to growing demands as we generate, acquire, inspect, and analyze data; execute machine learning models, 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 completely to the cloud, requiring deeper introspection.
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 journey 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 measuring business outcomes, with stakeholders’ verification. That starts with measuring our data platform, which today includes the data lake, operational data sources, third-party feeds, external references, the machine learning models and repository, and analytics. But just a measurement is not enough. We need to apply feedback and drive improvement in areas where we do not score well.
This approach is an evolution in measuring data management success, but you can begin here even if you are not measuring success today. Krish Krishnan will teach you to leverage the objectives and key results process (OKR) and the writings of Marty Cagan, John Doerr, and Christina Wodtke. You will see why Intel, Google, Facebook, and many start-ups have accomplished success, and you will learn from their failures, which we can rectify more easily with new measurement practices.
In this course, you will learn to apply the OKR framework 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 traditional governance towards new efficiencies, increased effectiveness, and improved measurement of business outcomes.
You Will Learn
- What OKR is all about
- The components of an OKR
- A brief history on OKR
- Benefits of OKR
- The data journey
- The components of the data journey
- The workflow in the data journey (what we do)
- Measurement of business outcomes (what we delivered)
- Feedback review (did we do well)
- Measuring the outcomes and maturity movement
Geared To
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