Enterprises use both standard and custom data pipelines to process raw data into analytics-ready data sets. They must apply the right mix, creating efficiency and scale via standardization and automation where possible, but still accommodating customization in order to innovate. But many get the balance wrong and standardize too slowly, limiting the value they derive from analytics.

This report examines the impact of standardization and customization on data pipelines, with a focus on design, building, testing, rollout, operations, and adaptation. It seeks to help architects, data engineers, application developers, and data scientists strike the right balance in their environments. Enterprises can start by standardizing overly customized data pipelines and demonstrating clear ROI with bite-size projects. As they standardize more and regain the right balance, they will drive data democratization, increased productivity, and reduced risk. As they standardize, enterprises can also free up and redirect resources to custom work, fostering innovation and increasing analytics value.


  • Architectural Approaches to Data Pipelines
  • Why Customize?
  • Why Standardize?
  • The Data Pipeline Lifecycle
  • Why Enterprises Lose Balance
  • Adoption Patterns & Striking the Balance