The data world has its very own vocabulary. Husprey helps you make sense of all the terms used daily by Analytics teams.
Ad-hoc analysis is a business intelligence process. It is designed by Data Analysts to answer specific business questions at a specific point in time.
Data engineering is the science of designing and building data pipelines. Its aim is to create the best possible infrastructure that would empower Data Scientists and Analysts to make raw data easily available and actionable for everyone.
Data freshness refers to the data’s timeliness and accuracy: in other words, how up-to-date this data is and how relevant it is to the current situation.
Data literacy is the ability to understand and analyze data and – most importantly – to draw meaningful conclusions from it. Data literacy is also the ability to understand and analyze data and to draw meaningful conclusions from it.
What we commonly call the data pipeline is a set of processes and technologies used to move data from one stage to another. It is a must-have tool for managing the data flow and data lifecycle.
The term “Data quality” refers to the extent to which a given dataset serves its purpose. As a result, “high quality data” is data that represents real-world scenarios in a consistently accurate way.