Business Analytics Definition
Business Analytics is the process by which businesses use statistical methods and technologies for analyzing historical data in order to gain new insight and improve strategic decision-making.
What is Business Analytics?
Business analytics, a data management solution and business intelligence subset, refers to the use of methodologies such as data mining, predictive analytics, and statistical analysis in order to analyze and transform data into useful information, identify and anticipate trends and outcomes, and ultimately make smarter, data-driven business decisions.
The main components of a typical business analytics dashboard include:
- Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered, either through volunteered data or transactional records
- Data Mining: data mining for business analytics sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships
- Association and Sequence Identification: the identification of predictable actions that are performed in association with other actions or sequentially
- Text Mining: explores and organizes large, unstructured text datasets for the purpose of qualitative and quantitative analysis
- Forecasting: analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviors
- Predictive Analytics: predictive business analytics uses a variety of statistical techniques to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes
- Optimization: once trends have been identified and predictions have been made, businesses can engage simulation techniques to test out best-case scenarios
- Data Visualization: provides visual representations such as charts and graphs for easy and quick data analysis
The essentials of business analytics are typically categorized as either descriptive analytics, which analyzes historical data to determine how a unit may respond to a set of variables; predictive analytics, which looks at historical data to determine the likelihood of particular future outcomes; or prescriptive analytics, the combination of the descriptive analytics process, which provides insight on what happened, and predictive analytics process, which provides insight on what might happen, providing a process by which users can anticipate what will happen, when it will happen, and why it will happen.
Some business analytics examples include the operation and management of clinical information systems in the healthcare industry, the tracking of player spending and development of retention efforts in casinos, and the streamlining of fast food restaurants by monitoring peak customer hours and identifying when certain food items should be prepared based on assembly time.
Modern, high quality business analytics software solutions and platforms are developed to ingest and process the enormous datasets that businesses encounter and can exploit for optimal business operations.
Business Analytics vs Data Analytics
Data analytics is a broad umbrella term that refers to the science of analyzing raw data in order to transform that data into useful information from which trends and metrics can be revealed. While both business analytics and data analytics aim to improve operational efficiency, business analytics is specifically oriented to business uses and data analytics has a broader focus -- both business intelligence and reporting and online analytical processing (OLAP) fall under the data analytics umbrella.
Data scientists, data analysts, and data engineers work together in the data analytics process to collect, integrate, and prepare data for the development, testing, and revision of analytical models, ensuring accurate results. Data analytics for business purposes is characterized by its focus on specific, business operations questions.
Business Analytics vs Data Science
Data science is a multidisciplinary field that uses scientific systems, methods, and algorithms to study structured and unstructured data in order to determine where information comes from, what it means, and how it can be transformed into a valuable resource in the development of information technology strategies.
Data science combines data analysis, statistics, machine learning, and related methodology in order to manage and understand the data deluge associated with the emergence of information technology. Data scientists are tasked with presenting digital information in a way that depicts its practical value in data-driven decision-making; however, they don’t typically endeavor to solve specific questions in the way that business analysts do when seeking out business analytics insights.
Business Intelligence vs Business Analytics
While business intelligence and business analytics serve similar purposes, and the terms may be used interchangeably, these practices differ in their fundamental focus. Business intelligence analytics focuses on descriptive analytics, combining data gathering, data storage, and knowledge management with data analysis to evaluate past data and providing new perspectives into currently known information.
Business analytics focuses on prescriptive analytics, using data mining, modeling, and machine learning to determine the likelihood of future outcomes. Essentially, business intelligence answers the questions, “What happened?” and “What needs to change?” and business analytics answers the questions, “Why is this happening?”, “What if this trend continues?”, “What will happen next?”, and “What will happen if we change something?” Business analytics and business intelligence solutions tend to overlap in structure and purpose.
Does OmniSci Offer a Business Analytics Solution?
Modern business data analytics requires business analytics tools that can facilitate massively accelerated analytics and data science. OmniSci, the pioneer in accelerated analytics, harnesses the massive parallelism of modern CPU and GPU hardware, enabling users to interactively query, visualize, and power data science workflows over billions of records.