Table of Contents
- Start here
- Accessing raw data
- Data transformation
- Downstream use cases
- Building a data team
- Joining a data team
Downstream use cases
Analytics engineers typically produce datasets for four key downstream use cases:
BI + Reporting: Often built by data analysts and used by business users.
Data science: Predictive machine learning models, built by data scientists and potentially deployed in collaboration with data engineers.
Operational analytics: Modeled data is propagated to frontline operations tools used by marketing, ops, sales and support teams (ex: Hubspot, Intercom, Salesforce).
Exploratory analysis: Used for collaboration amongst all flavors of data practitioner (analysts, engineers, scientists and business users) — often done in notebook applications or spreadsheets.
Since each of these use cases involves different team members, we’ll walk through the workflow summary (who / what / where / when) within each of them individually.