Dashboard: A tool that is used to create, deploy and analyze information. Typically, a dashboard will consist of a single screen and show various reports and other metrics that the organization is studying.
Database: A collection of data that is purposefully arranged for fast and convenient search and retrieval by business applications and Business Intelligence software.
Data Blending: Provides a fast and straightforward way to extract value from multiple data sources to find patterns without the deployment of a traditional data warehouse architecture.
Data Cleansing: Transforming data in its native state to a pre-defined standardized format using vendor software.
Data Cube: A database structure with multiple dimensions which can be stacked, combined and manipulated to enable browsing.
Data Democratization: Provides users across an enterprise with access to data, allowing them to run analysis at any time to answer any question.
Data Discovery: User-driven process of searching for patterns in a data set, providing self-service and data democratization. Data Discovery has been labeled by Gartner as “modern Business Intelligence.”
Data Governance: The management of the availability, usability, integrity and security of the data stored within an enterprise.
Data Integration: The combination of technical and business processes used to combine data from disparate sources into meaningful insights.
Data Lake: A storage repository that holds a large amount of raw data in its native format until it is needed.
Data Lineage: Referred to as the data life-cycle, which includes the origins of the data and where it moves over time, describing what happens to data as it goes through diverse processes.
Data Management: The development and execution of architectures, policies and practices to manage the data life-cycle needs of an enterprise.
Data Mart: A collection of reports, metrics and other stored data on a specific subject matter. Think of this as an organization of like information, making for easier discovery.
Data Migration: The process of moving data between two or more storage systems, data formats, warehouses or servers.
Data Mining: Extracting previously unknown data from databases and using that data for important business decisions, in many cases helping to create new insights.
Data Protection: Safeguarding vital business data from corruption or loss.
Data Quality: Refers to the contextually quality of an organization’s collection of data. The more relevant, available, complete and accurate the information, the better chance profitable business insights will be created.
Data Replication: The frequent copying of data from a database to another so that all users may share the same level of information, resulting in a distributed database that allows users to access data relevant to their own specific tasks.
Data Science: A field of study involving the processes and systems used to extract insights from data in all of its forms. The pfofession is seen as a continuation of the other data analysis fields, such as statistics.
Data Staging: A temporary location where all data from outside resources are copied.
Data Warehouse: A system used for Data Analytics. They are a central location of integrated data from other more disparate sources, storing both current (real-time) and historical data which can then be used to create trends reports. In multidimensional data sets, drilling is the process of navigating among levels of data ranging from the most summarized (up) down to the most detailed (down).
Data Visualization: Transforming numerical data into a visual or pictorial context in order to assist users in better understanding what the data is telling them.
Drilling: The process of navigating through different levels of data in multidimensional sets.