Print out our handy glossary of essential big data analytics terminology for a fast reference. Online, each term links to our full definition, which also includes resources for further learning.
advanced analytics -- future-oriented analyses that can be used to help drive changes and improvements in business practices.
big data – a voluminous amount – perhaps petabytes or more -- of structured, semi-structured and unstructured data that has the potential to be mined for information.
big data analytics – the process of examining large data sets containing a variety of data types -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
big data as a service (BDaaS) -- the delivery of statistical analysis tools or information by an outside provider that helps organizations understand and use insights gained from large information sets in order to gain a competitive advantage.
big data CRM -- the practice of integrating big data into a company's customer relationship management processes with the goals of improving customer service, calculating return on investment on various initiatives and predicting clientele behavior.
big data management -- the organization, administration and governance of large volumes of both structured and unstructured data.
data analytics (DA) -- the science of examining raw data with the purpose of drawing conclusions about that information.
data mining -- sorting through data to identify patterns and establish relationships.
data scientist -- a job title for an employee or business intelligence (BI) consultant who excels at analyzing data, particularly large amounts of data, to help a business gain a competitive edge.
data visualization – representation of data in graphic form to make its information more readily apparent. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.
deep analytics -- the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets comprised of both unstructured and semi-structured data.
enterprise data hub, also referred to as a data lake -- a new big data management model for big data that utilizes Hadoop as the central data repository.
Google BigQuery -- a cloud-based big data analytics web service for processing very large read-only data sets. BigQuery was designed for analyzing data on the order of billions of rows, using a SQL-like syntax.
Hadoop -- a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
in-memory analytics -- an approach to querying data when it resides in a computer’s random access memory (RAM), as opposed to querying data that is stored on physical disks.
predictive analytics -- the branch of data mining concerned with the prediction of future probabilities and trends.
small data -- data in a volume and format that makes it accessible, informative and actionable. Examples include baseball scores, inventory reports, driving records, sales data, biometric measurements, search histories, weather forecasts and usage alerts.
text mining -- the analysis of data contained in natural language text. Text mining works by transposing words and phrases in unstructured data into numerical values which can then be linked with structured data in a database and analyzed with traditional data mining techniques.
unstructured data -- data that is not contained in a database or some other type of data structure.
visual analytics -- a form of inquiry in which data that provides insight into solving a problem is displayed in an interactive, graphical manner.