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.
Advanced analytics is a data analysis method designed to extract useful information and insights from data, in order to help business teams make informed decisions.
Business Intelligence refers to the process of collecting, analyzing, and presenting data to help businesses make informed decisions. BI involves a combination of software applications, data management systems, and reporting tools.
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.
A data lake is a centralized repository for storing vast amounts of raw data in its native format, such as logs, sensor data, images, and videos. Data lakes are designed to be highly scalable, meaning they can grow as data volumes increase.
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.
Data transformation is a part of the overall data preparation process. However data transformation is also a process in itself, made of several operations: converting, cleansing, structuring.
Data visualization is the representation of data in a visual format such as graphs, charts, and other visual aids. Assets are found in dashboards, reports, or notebooks.
A data warehouse is a central repository designed to store, manage, and analyze large volumes of data from various sources such as databases, applications, or external data sources.
The acronym ETL stands for “Extract, Transform, Load”. It refers to the data integration process, where raw data is prepared and transferred from a source server to a destination data system.
The acronym SQL stands for “Structured Query Language”. It refers to a standardized computer programming language that is used for creating, altering and querying data in many relational database management systems.
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