- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Business Analytics - Key Concepts and Terminologies
The term business intelligence includes some technical terms; for those who wish to learn and do their mastering in the field of business intelligence; it is advised to them to understand the meaning of its key terminologies first. Some of the key terminologies of business intelligence are described below −
Data Mining
It is a process of extracting data from different sources systematically and then analysing it to produce data insights and identify hidden patterns, and correlations.
Data Warehouse
It is a huge data repository which stores data gathered from multiple sources. Data is stored and organised as per business standards; it provides consistent, quality-rich, and reliable information.
Business Intelligence
Business Intelligence (BI) refers to the advanced techniques, and practices to collect, integrate, analyse, and present business information. The purpose of business intelligence is to assist organisations in presenting business data in a graphical form, reports, charts, dashboards etc to make fruitful decisions.
ETL (Extract, Transform, and Load)
ETL stand for extract, transform, and load; it follows business protocols to clean and organize raw data to process using machine learning, data analytics, and business intelligence applications.
KPI (Key Performance Indicator)
It measures performance over a certain time for a specific objective.
Descriptive Analytics
Descriptive analytics analyses historical data to determine what happened. It monitors key performance indicators to deliver effective outcomes. Descriptive analytics includes data aggregation, data mining, data visualization, dashboards, and reports.
Diagnostic Analytics
Diagnostic analytics answers why happened. A user may understand the driving causes using correlations, data mining, drill-downs, and data discovery. Diagnostic analytics is most widely used in marketing, finance, cyber security, and other areas, this advanced analytics technique is typically used as a step before Descriptive Analytics.
Predictive Analytics
It uses mathematical models and algorithms to determine future trends based on historical data. The outcomes of predictive analytics help to frame business decisions. Overall, predictive analytics processes historical data to get to know future outcomes. It supports organizations by anticipating events, trends, and behaviours and gives a direction to them to make informed decisions and proactive strategies.
Prescriptive Analytics
To attain desired results and recommend strategies to achieve desired outcomes or optimize processes; prescriptive analytics suggests courses of action and tactics. It uses historical data to make suggestions on how to manage comparable future circumstances. It gives you insights into what may happen, when, and why.
Big Data
Big data is a large size and complex data set that may contain structured, semi-structured and unstructured data. Big Data includes large datasets that cannot be easily managed, processed or analyzed using traditional data processing tools and techniques. Big datasets are characterized by volume, velocity, variety, value and veracity; these are also known as the 5 V's of Big Data. Big Data has revolutionized how organizations approach data and decision-making, enabling more informed, data-driven strategies and innovations.
Data Visualization
Data visualization is a process of presenting processed data using graphs, tables, charts, or dashboards. Tools like Tableau, Power BI, and D3.js help present data in a visual format for easier interpretation and decision-making.
Machine Learning
It is an advanced technology which uses methods to process even complex data efficiently and produces useful results. Machine Learning (ML) is a subset of artificial intelligence (AI) that entails the development of algorithms and statistical models to learn from data and make predictions based on data. ML systems learn from data and use it to find patterns and improve their performance over time.
Artificial Intelligence
Artificial intelligence (AI) is the imitation of human intelligence. AI is used to develop algorithms that allow computers to accomplish tasks like reasoning, learning, problem-solving, perception, language comprehension, and others.