Data Integration & Analytics Glossary

Learn about the major concepts and terms for data analytics, business intelligence, and data integration with this in-depth industry glossary.


  • Active Intelligence

    Active Intelligence refers to a state of continuous intelligence where technology and processes support the triggering of immediate actions from real-time, up-to-date data.
    • Analytics as a Service

      Analytics as a service refers to a subscription-based model in which data analytics and BI processes take place on cloud-based, vendor-managed systems rather than using on-premise hardware.
      • Apache Kafka

        Apache Kafka is an open-source distributed event streaming platform which is optimized for ingesting and transforming real-time streaming data. By combining messaging, storage, and stream processing, it allows you to store and analyze historical and real-time data.
        • Augmented Analytics

          Augmented analytics (sometimes referred to as Augmented Intelligence) describes the use of artificial intelligence (AI) and machine learning technologies within a data analytics platform to enhance human intuition and productivity across the analytics lifecycle.
          • AutoML

            AutoML (short for automated machine learning) refers to the tools and processes which make it easy to build, train, deploy and serve custom machine learning models.
            • Azure Data Warehousing

              Microsoft's cloud data warehouse, Azure Synapse (formerly SQL Data Warehouse), provides the enterprise with significant advantages for processing and analyzing data for business intelligence.


              • BI Dashboard

                A BI dashboard is a business intelligence tool which allows users to track, analyze and report on key performance indicators and other metrics. BI dashboards typically visualize data in charts, graphs and maps and this helps stakeholders understand, share and collaborate on the information.
              • Big Data Analytics

                Big data analytics is the process of collecting, preparing and analyzing large, diverse data sets to generate valuable insights.
              • Big Data Management

                Big Data management includes processes and technologies to accelerate data ingestion, simplify real-time analytics, monitor data usage, control costs, and manage workloads.
              • Business Analysis

                Business analysis is the means through which operational problems and issues are systematically identified and investigated, different approaches are evaluated, and optimal solutions are determined.
              • Business Insights

                A business insight is a deep understanding of a business situation that has the power to drive an organization forward. Finding patterns and trends in your data—and acting on that knowledge—gives your business a competitive advantage.
              • Business Intelligence

                Business intelligence (BI) combines applications, processes, and infrastructure that enables access to and analysis of information to improve and optimize decisions and performance.
              • Business Intelligence Reporting

                Business Intelligence reporting is broadly defined as the process of using a BI tool to prepare and analyze data to find and share actionable insights.
              • Business Intelligence Tools

                Business intelligence tools are technology or software applications used to collect, combine, and analyze various types of business-relevant information.


              • Change Data Capture

                Change data capture (CDC) refers to the process of identifying and capturing changes made to data in a database and then delivering those changes in real-time to a downstream process or system.
              • Cloud Analytics

                Cloud analytics is a service model in which data analytics and business intelligence processes occur on a public or private cloud rather than on a company’s on-premise servers to help streamline the process of taking raw data to insights.
              • Cloud Data Migration

                Cloud data migration is the process of replicating and transferring data with technologies that simplify and accelerate data migration from many databases to many cloud platforms, efficiently and securely.
              • Cloud Data Warehouse

                A cloud data warehouse is a database stored as a managed service in a public cloud and optimized for scalable BI and analytics. It removes the constraint of physical data centers and lets you rapidly grow or shrink your data warehouses to meet changing business needs.
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              • Continuous Intelligence

                Continuous Intelligence refers to a system that leverages real-time analytics which are embedded directly into business operations, providing continuous access to the most up-to-date, accurate information, right where users need it.
              • Conversational Analytics

                Conversational analytics allow users to work with a data analytics platform using natural language interaction through text, voice and other means to ask questions, request data and discover insights.


              • Dashboard

                A dashboard presents critical data, visualizations, and KPIs focused on the specific needs of analytics user segments, allowing for a quicker, more organized review and analysis of business-critical information and trends.
              • Dashboard Reporting

                Dashboard reporting helps businesses make better informed decisions by allowing users to not only visualize KPIs and track performance, but also interact with data directly within the dashboard to analyze trends and gain insights.
              • Dashboard Software

                Dashboard software allows users to create visual representations of data and KPIs, helping them recognize patterns and make faster, data-driven decisions.
              • Data Analytics

                Data analytics refers to the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights.
              • Data Analytics Tools

                Data analytics tools are technology or software applications that allow users to find patterns, trends, and relationships in their data.
              • Data Catalog

                A data catalog is an inventory of data assets, organized by metadata and data management and search tools, which provides on-demand access to business-ready data.
              • Data Discovery

                Data discovery is the process of using a range of technologies that allow users to quickly clean, combine, and analyze complex data sets and get the information they need to make smarter decisions and impactful discoveries.
              • Data Exploration

                Data exploration is the process through which a data analyst investigates the characteristics of a dataset to better understand the data contained within and to define basic metadata before building a data model.
              • Data Fabric

                Data fabric refers to a machine-enabled data integration architecture that utilizes metadata assets to unify, integrate, and govern disparate data environments.
              • Data Governance

                Data governance refers to the set of roles, processes, policies and tools which ensure proper data quality throughout the data lifecycle and proper data usage across an organization.
              • Data Ingestion

                Data ingestion is the process of moving data from a single or multiple data sources to an on-premise or cloud destination where that data can be stored for subsequent analysis by different users within an organization.
              • Data Integration

                Data integration is the process of synchronizing data across applications and data platforms and providing users with comprehensive, accurate, and up-to-date information for business intelligence and analytics.
              • Data Integrity

                Data integrity refers to the accuracy, consistency, and completeness of data throughout its lifecycle.
              • Data Lake

                A data lake is a centralized repository that holds all of your organization's structured and unstructured data. It employs a flat architecture which allows you to store raw data at any scale without the need to structure it first.
              • Data Lake vs Data Warehouse

                Data lakes and data warehouses are both universal data repositories. Data lakes typically store large volumes of unstructured data and data warehouses store structured data that has been processed based on predefined business needs.
              • Data Lakehouse

                A data lakehouse is a data management architecture which combines key capabilities of data lakes and data warehouses. It brings the benefits of a data lake, such as low storage cost and broad data access, plus the benefits of a data warehouse, such as data structures and management features.
              • Data Lineage

                Data lineage refers to the process of understanding and visualizing data flows from source to current location and tracking any alterations made to the data on its journey.
              • Data Literacy

                Data literacy is the ability to read, work with, analyze and communicate with data, building the skills to ask the right questions of data and machines to make decisions and communicate meaning to others.
              • Data Management

                Data management consists of practices and tools used to ingest, store, organize, and maintain the data created and gathered by an organization in order to deliver reliable and timely data to users.
              • Data Mart

                A data mart is a structured data repository purpose-built to support the analytical needs of a particular department, line of business, or geographic region within an enterprise.
              • Data Migration

                Data migration is the process of moving data between storage systems, applications, or formats. Typically a one-time process, it can include prepping, extracting, transforming and loading the data.
              • Data Pipeline

                A data pipeline is a set of tools and processes used to automate the movement and transformation of data between a source system and a target repository. Building data pipelines can break down data silos and create a single, complete picture of your business.
              • Data Replication

                Data replication refers to the processes by which data is copied and moved from one system to another – from a database in the data center to a data warehouse in the cloud, for example.
              • Data Science vs Data Analytics

                Data science and data analytics are closely related but there are differences between the two fields. One key difference is that data science involves creating custom data models.
              • Data Streaming

                The process of moving data in a continual flow using modern replication technologies to inject database transactions into streaming systems like Kafka for real-time event processing, machine learning, and more.
              • Data Trends

                Our experts help you understand the top 10 emerging BI and data trends, and find out how to use them to your advantage.
              • Data Visualization

                Data visualization enables people to easily uncover actionable insights by presenting information and data in graphical, and often interactive graphs, charts, and maps.
              • Data Visualization Examples

                This guide showcases the ten most compelling and interesting data visualization examples from recent years. As you’ll see, a well-done chart can turn huge datasets into clear stories on any topic, from food to music to politics.
              • Data Visualization Tools

                Data visualization tools let users create graphics and imagery that help them make sense out of large amounts of data and make more informed decisions.
              • Data Warehouse

                A data warehouse is a data management system which aggregates large volumes of data from multiple sources into a single repository of highly structured and unified historical data.
              • Data Warehouse Automation

                The process of automating the entire data warehouse lifecycle from data modeling and real-time ingestion to data marts and governance to accelerate the availability of analytics-ready data.
              • Database Replication

                Database replication refers to the process of copying data from a primary database to one or more replica databases in order to improve data accessibility and system fault-tolerance and reliability.
              • DataOps

                DataOps is a data management methodology that aims to improve the communication, integration, and automation of data flows between data management and consumers throughout an organization.
              • Decision Support System

                A decision support system (DSS) is an analytics software program used to gather and analyze data to inform decision making, either by suggesting insights and analyses for humans to perform or by automating calculations and delivering best-case decisions.
              • Digital Dashboard

                A digital dashboard is an electronic interface which allows users to track, analyze and report on KPIs and metrics. Modern, interactive dashboards make it easy to combine data from multiple sources and deeply explore and analyze the data directly within the dashboard itself.


              • ELT

                ELT stands for “Extract, Load, and Transform” and describes the set of data integration processes to extract data from one system, load it into a target repository, and then transform it for downstream uses such as business intelligence (BI) and big data analytics.
              • Embedded Analytics

                Embedded analytics seamlessly integrate analytic capabilities and content from a data analytics platform into business applications, products, websites or portals to enable data-driven business processes.
              • ETL

                ETL stands for “Extract, Transform, and Load” and describes the set of processes to extract data from one system, transform it, and load it into a target repository.
              • ETL Pipeline

                An ETL pipeline is a set of processes to extract data from one system, transform it, and load it into a target repository. By converting raw data to match the target system before loading, ETL pipelines allow for systematic and accurate data analysis in the target repository.
              • ETL Tool

                An ETL tool is used to consolidate and transform multi-sourced data into a common format and load the transformed data into an easy-to-access storage environment such as a data warehouse or data mart.
              • ETL vs ELT

                The ETL and ELT acronyms both describe processes of extracting, transforming and loading data from a source into a target repository. In the ETL process, data transformation is performed in a staging area outside of the target repository and in ELT, transformation is performed on an as-needed basis in the target system itself.


              • Financial Analytics

                Financial analytics is the use of tools and processes to combine and analyze datasets to gain insights into the financial performance of your organization.


              • GeoAnalytics

                Geoanalytics leverage spatial data and visualizations to reveal crucial geospatial information and expose hidden geographic relationships to help users make better location-related decisions.


              • Interactive Data Visualization

                Interactive data visualization is the use of tools and processes to produce a visual representation of data which can be explored and analyzed directly within the visualization itself. This interaction can help uncover insights which lead to better, data-driven decisions.
              • IOT Analytics

                IoT analytics is the application of data analytics to the streams of information coming from networks of consumer, enterprise, and industrial, internet-connected devices.


              • Kafka Streams

                Kafka streams integrate real-time data from diverse source systems and make that data consumable as a message sequence by applications and analytics platforms such as data lake Hadoop systems.
              • KPI

                KPI stands for key performance indicator, a quantifiable measure of performance over time for a specific objective.
              • KPI Dashboard

                A KPI dashboard displays key performance indicators in interactive charts and graphs, allowing for quick, organized review and analysis.
              • KPI Examples

                KPI examples provide stakeholders guidance in selecting the most impactful key performance indicators for their organization and teams.
              • KPI Reports

                KPI reports provide a graphical, at-a-glance view of key metrics in real-time, helping decision-makers track the performance of their company, department, or initiatives, and identify areas in need of improvement


              • Marketing Analytics

                Marketing analytics is the practice of combining and analyzing datasets, identifying patterns, and then coming away with actionable insights that improve the ROI of marketing efforts.
              • Marketing KPIs

                Marketing KPIs are quantifiable measures of performance for specific strategic objectives. Marketing leaders and teams use KPIs to gauge the effectiveness of their efforts, guide their strategy, and optimize their programs and campaigns.
              • Metadata Management

                Metadata management refers to the organization and control of data which describes technical, business, or operational aspects of other data.


              • Predictive Analytics

                Predictive analytics refers to the use of statistical modeling, data mining techniques and machine learning to make predictions about future outcomes based on historical and current data.
              • Prescriptive Analytics

                Prescriptive analytics is the use of advanced processes and tools to analyze data and content to recommend the optimal course of action or strategy moving forward.


              • Reporting Analytics

                Reporting analytics refers to the process of collecting and analyzing data from various sources and presenting the results graphically and in an easy-to-consume format for efficient distribution.


              • SAP Analytics

                SAP analytics refers to the processes and technologies that enable use of SAP business application data for analysis using modern data integration and data analytics systems or SAP’s native analytics tools.
              • Spatial Analysis

                Spatial analysis is the collection, display and manipulation of location data—or geodata—such as addresses, satellite images and GPS coordinates to uncover location-based insights.
              • Streaming Data

                Streaming data refers to data which is continuously flowing from a source system to a target. It is usually generated at high speed by many data sources.
              • Supply Chain Analytics

                Supply chain analytics refers to the tools and processes used to combine and analyze data from multiple systems to gain insights into the procurement, processing and distribution of goods.


              • Visual Analytics

                Visual analytics integrates computational analysis techniques with interactive visualizations, offering users a new and innovative way to interact with, explore, and manipulate data.

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