Business Analytics Glossary

Welcome to our glossary for all things business analytics. In the world of data, definitions can change depending on where you look. With that in mind, we built this glossary to share our definitions of commonly used data terms and concepts, as well as ReconInsight terminology.

How to use:

If you are searching for a specific word, we recommend using the search function on your browser (CTRL+F) and typing the term in the search bar.

For general education, we’ve also grouped our glossary into categories:

Definitions for the technologies and features used in analytics.

Dashboard is a general software application user design construct used in many software applications to provide the user with a bird’s eye view of the software application. In data applications, dashboards display summary information of one or more reports in various visual arrays, such as charts, graphs and tabular summaries. Dashboards often use data visualizations.

Data Cleansing reviews all business data to ensure it is formatted correctly and consistently, corrects as needed, or notifies end-user to address ‘dirty’ data if it does not meet the business standards. Occurs in data processing, step one of the data pipeline. Only clean data can move to the next step of the data pipeline.

Data Processing / Data Processor is a software designed to extract business data from all data sources and conduct data cleansing and transformation. Also the first part of a business’s data pipeline. Also called: ETL (Extract, Translate, Load).

Data Store is a data information architecture. At ReconInsight, we believe it is an important part of a data pipeline that provides storage and security for all business data. Also called: Data warehouse, Data warehousing.

Data Translation involves mapping and applying custom business logic to cleansed business data with the goal of organizing and normalizing business information for storage and consumption. Occurs after data cleansing in data processing, step one of the data pipeline. Also called: Data transformation.

Data Visualization is the science of deriving meaning from data sets by using graphical and other non-tabular presentations. Examples of traditional data visualizations include line series charts, pie charts, and column charts. Research over the past decades have moved data visualization forward rapidly with the advent of new graphical representations. Impactful data visualization thought leaders include Edward Tufte and Stephen Few.

Database is a computer system (cloud or on-prem) that stores data in a persistent state, typically to be retrieved and modified by other software. In analytics, reporting and analysis software read, interpret, and display database data.

Geospatial Analytics associates data with a location. It is a type of data visualization that is often used to overlay data onto digital maps. The data is represented as data points or data summaries of geographic regions.

Pivot Table summarizes information extracted from large, detailed datasets. Multi-level pivots allow you to create hierarchies within the pivots to derive meaning. The term ‘pivot’ comes from a numeric value ‘pivoting’ on a discrete list. For example, the total number of cars pivoted against car model will summarize how many cars align with each car model. Pivot table data can be displayed as a table, pie chart, bar chart, or column chart.

Time-Series Forecasting applies statistical modeling to a time-series and forecasts that into future time periods.

Trends typically refer to a time-series data trend. Time-series trends address the change of a data value through multiple time periods.

User Interface represents any visual interface that a user of a technology interacts with during use. At ReconInsight we also use this to describe the last part of a data pipeline, which is often represented by a data analytics software.

High-level practices and methodologies used with data.

Artificial Intelligence (AI) is a broad term for using vast data sets to provide a high level of understanding and sometimes a higher level of consciousness. Within the realm of analytics, artificial intelligence can apply to machine learning.

Big Data is a widely-used term referring to the derivation of insights from large data sets. Big data is often associated with large unstructured data sets such as social media feeds and IoT data streams. “Big Data” is often overused and misused for the purposes of marketing.

Business Analytics (BA) involves analyzing business data to effect change at a company. It is process-oriented and focused on using data as a functional, decision making tool to improve business efficiencies, business operations, and business profits.

Business Intelligence (BI) describes the act of informing an organization using data. Business intelligence is typically associated with information dashboarding, specifically high-level reporting that summarizes previous traditional tabular reporting into charts and data visualizations. In recent years, business intelligence has been overcome by business analytics because users are asking deeper questions of their data.

Business Management encompasses all operations necessary to run an organization. Business management requires standardized processes to effectively operate all departments. Technologies for data pipelines and data analytics can greatly enhance the ease and efficacy of an organization’s business management efforts.

Data Analytics is the reporting and visualization of business information. A more technical definition would include: the process of cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

Data Science is the act of applying industry domain knowledge and advanced statistical analysis to one or more data sets. It includes bringing together data from various sources, applying statistical analysis, and presenting the results to key decision makers. Data science tools include statistics models, machine learning, artificial intelligence, and data visualization.

Database Management is the act of maintaining complex databases. Databases are computer application systems that require software patches, backups, storage management, performance tuning, and software patches.

Descriptive Analytics explains the current state of the data. It is the first of three stages in the progression of increased sophistication in data analytics. Descriptive Analytics is followed by predictive analytics and prescriptive analytics. Descriptive analytics explains the current state of the data. Predictive analytics applies statistical analysis to predict future behavior. Prescriptive analytics provides a specific action plan to improve a business function.

Information Repository refers to a data set that is organized for decision making. "Decisions are made on information, not data.” An information repository can be comprised of one or more databases, documents, or any other electronic data system.

Predictive Analytics applies statistical analysis to predict future behavior. explains the current state of the data. It is the second of three stages in the progression of increased sophistication in data analysis. Predictive Analytics is preceded by descriptive analytics and followed by prescriptive analytics. Related to: Time-Series Forecasting.

Prescriptive Analytics is finding the best course of action for a given situation. It is the third of three stages in the progression of increased sophistication in data analysis. Prescriptive analytics is preceded by descriptive analytics and prescriptive analytics.

Statistical Analysis is the general application of math and statistics to data. It is used in data science, time-series forecasting, predictive analytics.

Variety, Velocity, Volume are often used to describe large data sets used in data analytics. Volume describes the vast amount of data that often feeds into analysis. Velocity describes how quickly the data sets change. Variety describes how many types of data are used for analysis.

Definitions for terms and concepts used at ReconInsight.

Data Master is used to describe members of the ReconInsight data team. Our data masters are seasoned information professional that understand data from soup to nuts. Data Masters have the unique ability to work with both business users and technical users. They work directly with clients to understand business needs, model data, and implement a world class data pipeline using Ri360 technology.