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.
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.
Augmented intelligence refers to the broad application of
artificial intelligence (AI) technologies to augment human
intelligence with the scale and speed of machine
intelligence.
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.
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 is the means through which operational problems and issues are
systematically identified and investigated, different approaches are evaluated,
and optimal solutions are determined.
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 software refers to technology or software applications
used to collect, combine, and analyze various types of business-relevant
information.
Change data capture is a technology used for identifying and capturing changes
made to data in a database and delivering those changes to another database or
other type of data repository.
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 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.
A cloud data warehouse aggregates current and historical data in a single
repository within a cloud platform that can efficiently deliver data and
insights to users across an enterprise.
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.
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.
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 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 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 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 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.
A data lake is a large and diverse reservoir of corporate data stored across a
cluster of commodity servers running software, most often the Hadoop platform,
for efficient, distributed data processing.
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 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.
A data mart is a structured data repository purpose-built to support the analytical needs of a particular line of business, department, or geographic region within an enterprise.
Data migration is the process of moving data between different types of storage or file formats. Cloud data migration refers to transferring or replicating data from on-premise systems to cloud-based storage.
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.
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 visualization enables people to easily uncover actionable insights by
presenting information and data in graphical, and often interactive graphs,
charts, and maps.
A data warehouse is a data management solution to store large quantities of
historical business data, performing queries to support various business
intelligence and analytics use cases.
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.
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.
A decision support system includes the technologies used for management,
operations, and planning in an organization to help users make better decisions
by providing data and analytics capabilities.
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 is shorthand for – extract, transform, load. An ETL solution facilitates the
replication of data from one or more sources that is converted into format
suitable for use in analytics and moved into a destination system.
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.
Geoanalytics leverage spatial data and visualizations to reveal crucial
geospatial information and expose hidden geographic relationships to help users
make better location-related decisions.
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 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 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
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 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 is the collection, display and manipulation of location data—or
geodata—such as addresses, satellite images and GPS coordinates to uncover
location-based insights.
Visual analytics integrates computational analysis techniques with interactive
visualizations, offering users a new and innovative way to interact with,
explore, and manipulate data.
