Business analytics can be a complicated term to understand. With so many other complex terms surrounding business analytics, it can take time to understand multiple aspects of business analytics altogether. 

Here are the significant terms that are commonly implemented and used in the space of business analytics, along with their definitions, are mentioned in this article.

Business Analytics Terminologies

In the field of data, definitions might vary depending on your source. Hence, this article will provide you with a glossary of the meanings of the most commonly used data concepts and terms. 

  1. Data Mining: It systematically analyzes huge datasets to generate insightful information, identify patterns, and uncover hidden correlations.
  2. Predictive Analytics: This type of advanced analytics determines what is most likely to happen on the basis of historical data using machine learning, statistical techniques, or data mining.
  3. Prescriptive Analytics: Prescriptive Analytics is related to guided analytics, wherein your analytics is prescribing you towards a specific action to be taken. It effectively combines predictive analytics and descriptive analytics to drive decision-making.
  4. Descriptive Analytics: Descriptive Analytics involves informing you about past events by analyzing historical data and identifying patterns. Several organizations at a certain level of maturity in their analytics journey are already performing a degree of descriptive analytics.
  5. Big Data: Big data is a large and complex data set that contains unstructured and structured data, arriving in rising velocity and volumes.
  6. Business Intelligence: Business Intelligence implements services and software, helping business users make better-informed decisions by forwarding dashboards and reports to help them analyze certain data and actionable information.
  7. Data Visualization: Data Visualization refers to representing information and data through diagrams, tables, charts, or pictures to connect information, similar to how the human brain grasps information and identifies outliers accurately, precisely, and quickly.
  8. Machine Learning: It is a process of the practical application of artificial intelligence where a system utilizes information and data to study and improve over time by recognizing relationships, optimization, patterns, and trends.
  9. Artificial Intelligence: Artificial Intelligence imitates the human intelligence process but through machines. It combines robust data sets with computer science to enable problem-solving through the rapid learning capabilities of the machines.
  10. KPI (Key Performance Indicator): It measures performance over a certain time for a specific objective. KPI offers a team of organizations to aim for milestones to monitor progress and insights for making better decisions.
  11. Data Warehouse: It is a highly centralized data repository modeled from multiple sources. Data is stored in business language, providing consistent, quality-rich, and reliable information.
  12. ETL (Extract, Transform, Load): It brings together data from multiple resources into a large repository called a data warehouse. ETL, which stands for extract, transform, and load, utilizes a set of business rules for cleaning, organizing raw data, and preparing for machine learning, data analytics, and storage.
  13. OLAP (Online Analytical Processing): It is a software technology that can be used to analyze business data from multiple points of view. Organizations collect and store data from various data resources, such as applications, internal systems, and websites.
  14. Dashboard: It is a user interface element in software applications that provides users with an overview of a software application. Dashboards present the summary of information from reports in various visuals, such as labels, graphs, and charts in data applications.
  15. Data Modeling visually represents the entire information system or its parts to craft connections between structures and data points.
  16. Statistical Analysis: It is an application of statistics and math to data. It is used in predictive analytics, data science, and forecasting.
  17. Decision Trees: They are nonparametric supervised methods of learning used for regression and classification. The aim is to create a model that predicts the value of the targeted variable by learning simple decision rules from the date of features.
  18. Cluster Analysis: It is a technique of data analysis to explore the naturally occurring groups within a data set called clusters.
  19. Time Series Analysis: It is a way to analyze a sequence of data points collected over time intervals. Under time series analysis, data points are recorded by analysts at a particular interval over a certain period instead of recording data points randomly.
  20. Regression Analysis: It is a reliable and efficient method to identify which variable impacts the topic of interest. The process of performing a regression allows you to determine exactly which factors are of high importance, which factors influence one another, and which factors you can ignore.
  21. Data Governance: It is a process through which an organization makes sure that its practices, processes, and data policies are implemented and followed. When data governance is executed, a proper governance program must also define exactly who owns the data, who edits it when a correction is required, and who utilizes it to ensure that changes are monitored. The framework of data governance defines how you implement a data governance program.
  22. Data Cleaning: It is a process to remove or fix incorrect, duplicate, incorrectly formatted, corrupted, or incomplete data in a data set.
  23. Sentiment Analysis: it is a process of analyzing digital text to monitor if the emotional tone of the message is negative, neutral, or positive.
  24. Natural Language Processing (NLP): natural language processing is a machine learning technology that allows computers to manipulate, comprehend, and interpret human language.
  25. Business Process Modeling: This process provides organizations an easy way to acknowledge and optimize workflows by creating visual representations of the business process.
  26. Data Security: Data Security is a process to safeguard digital information throughout its complete lifecycle to protect it from theft, unauthorized access, and corruption. It covers almost everything, from hardware storage devices, software, and user devices to organizations, policies, procedures, and administrative controls.
  27. Data Integration: The process of combining data from various resources across an organization to provide an accurate, complete, and updated data set for BI, applications, data analysis, and other business processes is called data integration.
  28. Cloud Analytics: Cloud Analytics is the application of analytical algorithms against data in a public or private cloud to deliver results of interest. It involves scalable cloud computing deployment with robust analytic software for identifying patterns and extracting new insights.
  29. CRM Analytics (Customer Relationship Management Analytics): CRM analytics collects and organizes consumer data from the organization to help healthcare payers solve business problems through models, reporting tools, and dashboards.
  30. ERP (Enterprise Resource Planning) Analytics: ERP analytics includes methods and processes that collect, store, and analyze data gained from business operations for optimizing performance. 
  31. Anomaly Detection: Anomaly detection involves examining specific data points to identify rare occurrences that deviate from established patterns. As data grows, manual tracking becomes impractical, necessitating automated methods.
  32. Churn Analysis: Churn analysis utilizes historical customer data to predict customer churn. Customer lifetime value (LTV) analysis offers insights into customers at different lifecycle stages, understanding who remains loyal to a product.
  33. Cohort Analysis: Cohort analysis, a form of behavioral analytics, groups data into cohorts based on shared characteristics or experiences within a defined time frame before analysis.
  34. Cost-Benefit Analysis: Cost-benefit analysis (CBA) compares intervention costs and benefits in monetary terms, while cost-effectiveness analysis (CEA) includes health outcomes in the evaluation.
  35. Data Aggregation: Data aggregation involves searching, gathering, and presenting data in a summarized, report-based form to help organizations achieve specific business objectives or conduct analyses.
  36. Data Blending: Data blending is a method that combines data from multiple sources, incorporating additional information from a secondary source into the primary data source for a comprehensive view.
  37. Data Mart: A data mart contains information specific to an organization's business unit, representing a selected part of data stored in a larger system.
  38. Data Quality: Data quality measures how well a dataset meets criteria like accuracy, completeness, validity, and consistency, playing a crucial role in data governance initiatives. 
  39. Data Science: Data science is an interdisciplinary field using statistics, scientific computing, and algorithms to extract insights from potentially noisy, structured, or unstructured data.
  40. Dimensionality Reduction: Dimensionality reduction transforms high-dimensional data into a low-dimensional space while retaining meaningful properties, ideally close to the original data's intrinsic dimension.
  41. Forecasting: Forecasting is a technique employing historical data to make predictive estimates for determining future trends. Businesses use forecasting to allocate budgets and plan for anticipated expenses in upcoming periods.
  42. Geospatial Analysis: Geospatial analytics enhances traditional data by adding timing and location, creating visualizations like maps and graphs. These visualizations offer a comprehensive view, incorporating historical changes and current shifts.
  43. Heatmap: A heatmap is a graphical representation of data using color to depict values. Heatmaps are essential for website analysis to show user engagement with different page elements, helping identify effective and ineffective areas.
  44. Hypothesis Testing: Statistical hypothesis tests are used for statistical inference, deciding whether data sufficiently supports a particular hypothesis by calculating a test statistic.
  45. IoT Analytics (Internet of Things Analytics): IoT analytics processes massive volumes of data for actionable insights, categorized into historical analytics (understanding past events) and real-time or streaming analytics (understanding current events).
  46. Logistic Regression: Logistic Regression, borrowed from statistics to machine learning, is used when the dependent variable is dichotomous or binary, such as predicting survival in an accident or a student passing an exam.
  47. Market Basket Analysis: Market Basket Analysis is a business strategy that designs store layouts based on customer shopping behavior. It is also applicable to machine learning algorithms, aiding e-commerce businesses.
  48. Multivariate Analysis: Multivariate analysis involves analyzing multiple variables for each individual or object, providing a comprehensive understanding of datasets.
  49. Neural Networks: A neural network, a method in artificial intelligence, mimics the human brain's interconnected nodes to process data. It is a form of deep learning.
  50. Performance Metrics: Performance metrics (financial, operational, or customer-related) measure business behavior and activities, providing data within a range to support overall business goal achievement.
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Conclusion

This business analytics terminology provides easy-to-understand definitions of almost every critical business analytics term. To progress as a business analyst, you must have a keen knowledge of the terms mentioned above. Even if you're a fresher in this field, worry not, as Simplilearn brings you its Business Analytics course to help you become a skilled and expert business analyst with the proper knowledge and skills.

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