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SAP HANA is an in-memory database platform for processing high volumes of operational and transactional data in real-time.

A data scientist is 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. To do so, the employee is using statistical & data mining techniques. 

A business intelligence competency center (BICC) is a team of people that, in its most fully realized form, is responsible for managing all aspects of an organization's BI strategy, projects and systems. 

Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Although big data doesn't refer to any specific quantity, the term is often used when speaking about petabytes and exabytes of data. See also Big Data vs. Business Intelligence

Change data capture (CDC) is the process of capturing changes made at the data source and applying them throughout the enterprise. CDC minimizes the resources required for ETL (extract, transform, load) processes because it only deals with data changes. The goal of CDC is to ensure data synchronicity. 

Data latency, in a data warehouse context, is the time between the creation of data in a source system and the exact time at which the same data is available for end users on the business intelligence platform. 

A data mashup is the integration of heterogeneous digital data from multiple sources for business purposes. 

Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns and trends in the data set being examined as a whole. 

Levels are an implementation of the hierarchies that represent the level of detail viewed in a dimension. As an example, for a time dimension hierarchy, we have levels for year, quarter, and month.

Text mining is 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.