A practical guide to get started with DataOps

How your organization can adopt the (non-technical) practices in DataOps to improve Data Governance outcomes

A few months ago I wrote a post on the coming rise of DataOps, in which I predicted that the world of Data Governance will see some of the same shakeups that IT Operations experienced during the rise of DevOps. In this post, I’ll share some practical tips for how your organization can get started down the path to leveraging the new set of practices that underlie the DataOps movement. The goal is to allow your organization to derive value while adopting new roles and processes.

Adopt an analytics lifecycle

Your organization should think of the development of analytics pipelines as a 3 stage process, with a different focus at each stage:

1. Conceptualize: generate the ideas and business cases for analytical data products. Focus on creativity and prioritize a balance of value and complexity.
2. Experiment: prove the feasibility and value of an idea. Focus on accessing data and transforming it into products.
3. Operationalize: move a proven idea to production and make the operational changes to leverage it. Focus on realizing the expected value of an idea.