BI Glossary

Ad-hoc analysis is any business report or data analysis curated and created by users, as and when they need it. Ad-hoc analysis in business intelligence sits in stark contrast with the managed reports seen in the early days of business analytics.

Advanced analytics is an umbrella term for a group of high-level methods and tools that can help you get more out of your data. The predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviors.

Agile analytics is a paradigm for exploring data that focuses on finding value in a dataset by using a free-form adaptive approach to focus on agility.

An analytical database collects historical data related to operations’ KPIs and metrics such as sales, performance, and inventory and is easily accessible.

Analytics architecture is the way you structure your data, in both hardware and software layers, to define how users access and interact with it.

Analytics as a Service typically offers cloud-based BI software that breaks down your data in a way that even non-techies gain insight and take action.

Application analytics track the performance of web, mobile, and desktop applications to measure efficiency and success and monitor improvements and fixes.

Application delivery refers to the combination of services that companies must perform to provide functionality for the end-user or client. Application delivery uses a variety of platforms and technologies to create applications that are robust, available, and scalable.

Application lifecycle management is a set of tools and processes that oversee an application from inception to retirement and ensures smooth operations.

Application modernization is when a business updates its infrastructure or key product features to extract more value and align to current needs.

Automatic concurrency scaling is a feature of cloud-based data warehouses that automatically adjusts computational capacity to handle ever-changing demand.

Hadoop handles big data that conventional IT systems cannot manage because the data is too big, arrives too fast, or comes from too many different sources.

Big data analytics helps businesses to get insights from today’s huge data resources. This data can be examined to see trends, opportunities, risks, etc.

Big data security is the collective term for the measures and tools used to guard an organization's data and analytics processes from attacks and theft.

Business intelligence architecture is the infrastructure that organizations use to define their data collection streams and information administration.

Business process modeling is the data visualization of companies’ workflows and business processes to extract insights and identify areas for improvement.

Cloud analytics refers to any BI or data analytics carried out in the cloud, which can be more versatile and cheaper than on-premise solutions.

Cloud automation refers to the tools, activities, and processes used to reduce the manual oversight and management of cloud computing workloads and systems.

Cloud data management simplifies the communication of an organization’s data across the cloud and physical storage, and between private and public clouds.

Learn what Is Cloud Migration, what are the benefits, challenges, common strategies, and even a cloud migration checklist for your use.

Cognitive computing refers to the use of reasoning, language processing, machine learning, and human capabilities that solve problems and analyze data.

Cohort analysis is part of behavioral analytics that examines data from large sets and breaks it down into related groups to parse in a defined time frame.

A columnar database writes and reads data much more efficiently than row-oriented databases and can move very quickly to and from data sources.

Contextual data provides background information that organizations can use to understand customer behavior patterns and improve their experiences.

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Data analytics is the process of discovering patterns in your company’s data.

Data blending is combining multiple datasets to create a single, new dataset, which can then be processed or analyzed. Learn More!

Data cataloging is the process of making an organized inventory of your data. The data catalog is what you’ll use to index where everything is stored.

Data cleaning refers to preparing data for analysis by removing or modifying data that is incomplete, irrelevant, duplicated, or improperly formatted.

Data discovery is the process of uncovering relevant data insights and getting those insights to the business users who need them.

Data exploration is the process of analyzing a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest.

Data governance ensures data is safe and handled in compliance with regulations, but also helps you gain value from all the information at your fingertips.

Data intelligence refers to all the analytical tools companies employ to form a better understanding of their information in order to improve operations.

Data literacy is the skill set of reading, communicating, and deriving meaningful information from data, to put the information in context and tell a story.

As companies grow, data configurations become more elaborate. Data mapping is a way to establish a cohesive data model for a data-driven organization.

A data mart is a subset of data stored within the overall data warehouse, for the needs of a specific team, section or department within the enterprise.

As you become data mature, it’s important to manage your own expectations and be realistic about the value that you’re getting from these investments.

Data mining basics is an analytical method that uses automation tools like machine learning to extract usable information from massive sets of raw data.

What is data modeling and how does it relate to data analysis? This 3-minute read will walk you through the bare essentials.

Data organization is the practice of categorizing and classifying data to make it more usable, so it can be easily accessed, processed, and analyzed.

A data pipeline is the structure that facilitates moving data from the applications or systems where it is stored

Data preparation is the process of cleaning, reformatting and modeling your data in order to get it into shape for analytics or other evaluation purposes.

Data needs to be processed to get the most out of it. Data products are what enable users to extract value from this resource.

Data quality measures the condition of your data, using different factors. Get in to explore the factors and deep dive into data quality management.

Data reporting refers to collecting and formatting raw information and translating it into a digestible format to assess ongoing business performance.

Data science is the multidisciplinary field that focuses on finding actionable information in large, raw or structured data sets to identify patterns.

Data standardization is the process of converting data to a common format to enable users to process and analyze it.

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A complete guide that will open the door for you to the data visualization world. From basic explanations to advanced design resources and examples

Data warehouse architecture refers to the design of an organization’s data collection and storage framework, placing it into an easily digestible structure.

Database security uses a wide variety of tools to protect large virtual data storage units, like databases, from external attacks, corruption, and malware.

Descriptive analytics gives you a general view of your historic data, to provide a clear, straightforward picture of your company’s operations.

Deterministic finite automata are finite state machines that accept or reject strings of characters by parsing them through a uniquely determined sequence.

DevOps continuous integration is a development model that focuses on fine tuning and live additions to existing software rather than releasing new versions.

Diagnostic analytics describes techniques that let you deep-dive into your data, search for insights, and uncover the reasoning behind certain results.

In edge analytics, incoming data streams are analyzed at a non-central point like a sensor or peripheral node, and emphasizes speed and decentralization.

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embedded analytics refers to the process of plugging a third party’s software into your own app

Embedded reporting is the method of incorporating BI visualizations, such as dashboards and charts, into software applications for customized viewing.

An entities relationship diagram describes how data elements relate to each other, within your business or a certain business process. Read more:

Financial data management is the part of your BI solution that helps monitor information and reporting needs, as well as comply with legal requirements.

Geo analytics adds location data to your visualizations, providing a new axis to discover insights for sales, engagement, and marketing departments.

Google BigQuery is a cloud-based and scalable enterprise data warehouse that offers rapid SQL queries and interactive analysis of massive datasets.

Healthcare analytics basics refers to analyzing current and historic industry data to predict trends, improve outreach, and manage the spread of diseases.

Healthcare informatics focuses on gathering and understanding industry data to improve service provision and collaboration between doctors and providers.

In-Memory BI’s claim to fame is to provide an alternative to the hefty data warehouse and OLAP projects. But does it deliver?

Insights as a service use predictive analytics and BI to reveal insights from cloud-based data warehouses and provide solutions to data-specific challenges.

Integrated analytics fuse your data insights into everyday workflows and applications, helping employees and customers make smarter decisions.

Interactive analytics is an extension of real-time analytics that has the capacity to crunch through huge volumes of unstructured data at scale and speed.

Interactive visualization refers to graphic representations of data, often in the form of dashboards, that improve the way we interact with information.

A KPI dashboard brings all your KPIs together in one place. You can see how you’re performing on each different measurement, side by side.

KPI tracking, the way companies monitor performance metrics, is what your data dashboards are based on. Use it to power your sales and innovations.

A Linux database is built specifically for the open-source operating system and is generally more stable, safe, and customizable, with reliable uptimes.

A Linux server is designed to handle the intense demands of larger organizations, and outstrips Windows servers in stability, security, and flexibility.

Location analytics extracts additional insights from transactional data like logistics and supply chains by adding a layer of geolocation information.

Managed cloud is a service that helps smaller businesses build servers and infrastructure on the cloud by handling set-up and support on the back-end.

Marketing analytics is the technology and BI tools marketers use to measure the impact of their strategies and campaigns, with data from various sources.

Mobile analytics monitor and understand how users interact with an app to gain a better comprehension of their behavior and the application’s performance.

An MPP database handles operations from multiple processing units simultaneously, providing faster analytics from large datasets and saving server space.

In a multi tenant environment, a single instance of software runs on a server and serves multiple customers by creating a dedicated instance for each one.

Natural language understanding helps AI learn how to talk to humans. This technology can be used in features like chatbots and search engine queries.

OEM manufacturing refers to providing parts and supplies to companies to incorporate into their own products and resell, saving cost and hassle. .

OEM partners offer third-party software, including analytics, that you can embed into your product, adding functionality and value for your customers.

OEM software refers to applications that are sold wholesale to hardware and software manufacturers for the purpose of bundling with existing offerings.

The magic behind OLAP comes from its ability to pre-calculate and pre-aggregate data. But what happens when you need answers NOW?

Online transactional processing is a class of software program that supports transaction-based applications on the internet, like shopping or POS terminals.

Open database connectivity (odbc) is an API that allows applications to interact with multiple database systems simultaneously while remaining independent.

Open source embedded analytics are non-proprietary tools that can be integrated with a company’s own software to enable developers to view and modify code.

Operational intelligence is a data analytics philosophy that focuses on implementing quick business decisions and taking action based on real-time data.

Operational reporting produces detailed reports of day-to-day organizational operations and delivers short-term BI insights that are immediately actionable.

Predictive data analytics uses current and past data to let you make predictions about the future or other unknowns. Learn More!

Predictive maintenance is a data-driven process to predict when operational equipment may fail and deploying strategies to avoid downtime and reduce costs.

Prescriptive analytics focuses on finding the best course of action in a scenario, emphasizing actionable insights instead of data monitoring.

Product analytics uses AI and machine learning to make sense of customer behavior by providing a unified central view of historical and real-time data.

Python is a multi-purpose programming language popular for its flexibility, as well as its extensive collection of libraries, widely used for analytics.

Python for data science refers to the extensive libraries and tools, including machine learning, that the programming language offers for data analysis.

R analytics is a free, open-source programming language used for data science, statistics, and visualization projects. It's a powerful and versatile tool.

R for data science is a popular programming language featuring a catalog of tools including machine learning, along with heavy-duty regression and modeling.

Real-time analytics gives insights immediately after the data enters the system, allowing businesses to react without delay.

A real time dashboard contains visualizations that are automatically updated with current data that can identify emerging trends and evaluate efficiency.

Amazon's redshift database is a cloud-based warehouse solution that lets companies store petabytes of data in easy-to-access clusters for analysis and BI.

Read the definition of a relational database and the benefits of using one to store organizational data.

Retail analytics focuses on providing insights related to sales, inventory, customers, and other important aspects crucial for the decision-making process.

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Self-service bi are data analytics tools that nontechnical users - not just IT and data teams - can use to connect and analyze data and reveal insights

Self-service reporting gives users direct access to data analysis, requiring little technical knowledge to get actionable BI insights and accurate reports.

The Snowflake data warehouse is a cloud-based tool that supplies companies with flexible and scalable storage while simultaneously hosting solutions for BI.

SQL for data analysis refers to SQL's ability to perform surprisingly complex data analysis and queries, despite the simplicity of the language.

Talent analytics is a BI tool mainly used by HR departments. It can maximize your team's potential by giving you insights into your employee metrics.

Text mining involves taking unstructured text (like customer complaints) and organizing it within a database to simplify the process of analyzing it.

Unified data management (udm) describes the process of consolidating disparate data sources to create a single narrative and build a more agile data model.

A visual data analysis will fit data in an interface, like an interactive dashboard, to make it easier for human beings to see patterns and identify trends.

Business intelligence is a data-driven process for analyzing and understanding your business so you can make better decisions based on real-time insights.

Data analysis is the process of manipulating your data for evaluation purposes with the goal of improved business operations and informed decision-making.

Data lineage traces the journey of your data. This information is stored as technical metadata and used by database systems to address issues.

As an embedded product, white label analytics offer the benefits of data analysis outsourcing without customers having to leave your website or application.

Revealing new insights and conclusions from data is the reason why business analytics tools and techniques are necessary for data-driven organizations.