Engineering Enablement by Abi Noda cover image

The science behind DORA | Derek DeBellis (Google)

Engineering Enablement by Abi Noda

Understanding Outcomes and Conducting Research

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Exploring the challenges of determining outcomes individuals seek, the importance of self-reported organizational performance, and the significance of survey item development and pre-testing in research studies. The conversation delves into the complexities of using self-reported measures versus objective metrics, the difficulties in assessing organizational performance, and the importance of identifying drivers that predict outcomes accurately.

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In this week's episode, we welcome Derek DeBellis, lead researcher on Google's DORA team, for a deep dive into the science and methodology behind DORA's research. We explore Derek's background, his role at Google, and how DORA intersects with other research disciplines. Derek takes us through DORA's research process step by step, from defining outcomes and factors to survey design, analysis, and structural equation modeling.

Mentions and Links:

Derek DeBellis on LinkedIn
DX’s guide to measuring GenAI adoption and impact
2023 Accelerate State of DevOps Report

Discussion points:

(3:00) Derek’s transition from Microsoft to the DORA team at Google
(4:28) Derek talks about his connection to surveys
(6:16) Derek’s journey to becoming a quantitative user experience researcher
(7:48) Derek simplifies DORA
(8:19) DORA - Philosophy vs practice
(11:09) Understanding desired outcomes
(12:45) Self reported outcomes vs objective outcomes
(16:16) Derek and Abi discuss the nuances of literature review
(19:57) Derek details survey development
(27:55) Pretesting issues
(29:30) Designing surveys for other companies
(35:02) Derek simplifies model analysis and validation techniques
(38:48) Benchmarks: Balancing data limitations with method sensitivity

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