Data analysts are true data professionals. They build ad hoc analyses and connect to new data sources. At the same time, this group has the largest range in technical ability. Some may write SQL queries to retrieve data while others require a no-code environment. In either case, they are comfortable sorting through data sets to find what they need and can build reports or dashboards from scratch.

- Joe Hilleary in Supporting Self-Service Analytics with a Unified Platform

March 12, 2021 (Blog)

Similarly, Data Analysts most often (80% of the time) access a business view, but occasionally (20% of the time) pull data from also subject-oriented tables in a data hub (i.e. data lake, data warehouse, data fabric). Data Analysts use the flattened subject-oriented data to craft custom data sets for analysis purposes. To create data pipelines and analytic models, Data Scientists will query a data hub most of the time (80%), but when necessary, they will extract raw data from a staging area. Both Data Analysts and Data Scientists use a triumvirate of self-service analytic tools: a data catalog to find data, a data prep tool to combine data, and a visualization tool to analyze and share data.

- Wayne Eckerson in How to Succeed with Self-Service Analytics, Part II: Know Thy Customer

December 9, 2019 (Blog)

Data Analyst. The proverbial spreadsheet jockey with a master’s degree in business administration; works in finance, marketing, sales, and operations to answer ad hoc questions, manage pricing, build KPIs and performance metrics; develops plans and options.

- Wayne Eckerson in Organizational Frameworks for Data Analytics: Establishing Programs, Teams, and Roles in the Age of Self-Service

January 6, 2019 (E-book)