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.
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.
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.
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.
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 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.
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 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.
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 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 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.
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.
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.
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.
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.
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.
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.
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
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.