Reveal Analytics Glossary

Business Analytics: Business analytics refers to the process of analyzing data to draw conclusions about business processes. It can be descriptive, predictive, or prescriptive in nature.
Business Intelligence: Business intelligence refers to the processes and tools used to collect, store, retrieve, and analyze data for the purpose of making sound business decisions.
Dashboard: Dashboard in the context of data analysis is the user interface via which KPI metrics or analytics are viewed. Dashboards might also consist of built-in solutions that let users pull or manage certain reports and analysis.
Data Sources: Data Sources are where data comes from. Most applications draw data from sources such as databases and spreadsheets. Data sources can also be people, processes, images or other items that contain information that can be received and parsed.
Data Visualization: Data visualization is the process of using charts, graphs, maps, and other visual elements to tell a story with data. It can help make data and data-backed conclusions more accessible to the entire organization.
Data-Driven Storytelling: Data-Driven Storytelling is putting a narrative behind numbers and analytics to make it more coherent and easier to engage with or remember. It’s a powerful analytic tool because it lets you support your message with data or present that data in a way that resonates with your audience.
Embedded Analytics: Embedded Analytics are integrated into user content or applications to provide ease of access to data and efficient analysis of key metrics. The analytics are often visual in nature and may be interactive via options such as filters, sorts, and display preferences.
Forecasting: Forecasting is the process of making predictions for future data points based on past and present data.
KPI Metrics: KPI Metrics, or key performance indicator metrics, are measurements that tell you how a process is performing so appropriate business decisions can be made. Examples include sales numbers, average speed of answer and number of widgets produced per hour.
Linear Regression: Linear Regression quantifies the relationship between one or more predictor variable(s) and one outcome variable and is commonly used in predictive analytics.
Machine Learning: Machine learning is a subset of artificial intelligence that enables systems to learn and predict outcomes without explicit programming.
Marketing Analytics: Marketing Analytics is the data and analysis that helps organizations understand marketing performance as well as make decisions about marketing processes and spend. Marketing analytics help identify target audiences and best practices for messaging and advertising to drive up ROI.
Outlier: Outlier is a data point that deviates drastically from the average data set, causing averages and trends within your data to be skewed towards anomalies.