Business analytics is defined as the exploration of business data with the help of statistical software tools.
The business data may be generated by machines (like sales by Point of Sale electronic registrars), filled by humans (in spreadsheets) or a combination of both.
Statistical software used to analyze the business data may vary in complexity from a simple spreadsheet program (like Excel or Open Office) to highly evolved specialized statistical programs like IBM SPSS, SAS Institute ‘s software like Base Stats , SAS/Stats and SAS/Graph or programs like R. The SAS Institute is generallyconsidered the market leader in business analytics solutions.
Modeling is a term generally associated with predictive analytics in which we use historic data to predict probability or odds of certain events happening in the future (like customer buying a credit card or customer defaulting on loan). Descriptive analytics is more synonymous with reporting and generation of statistics about existing information rather than future insights.
More specifically business analytics is differentiated from web analytics, as web analytics is concerned with analyzing data regarding
Internet, social media and websites, whereas business analytics is concerned with the data generated by any kind of online as well as
The distinction between business analytics and business intelligence is not as clear, and both have a certain overlap. While Business Intelligence is defined as the seamless dissemination of information throughout an enterprise, it often involves solutions to store, retrieve as well as report in dashboards and templates the business data. Business analytics is much more exploratory and perceived as requiring more specialized tools and personnel than business intelligence. Business intelligence is much more of reporting of information while business analytics is more exploratory and requires some insight to be derived from information.
Sometimes a common term heard alongside business analytics is data mining. Data Mining or Knowledge discovery is the art and science of excavating patterns, trends as well as information from given set of data. When the process is automated through computer generated processes or algorithms, this can also be called as Machine Learning.
In order to enable business analytics we need to have the following objects-
1) Business generated data
2) Computer science or statistical tool to convert data into
information and insights
3) Human Analyst to convert insights into decision supporting actions.
A business decision is integral to the concept of business analytics. In addition to this data is stored in databases and data warehouses, and retrieved through querying tools. Querying tools are generally used before analytical tools are applied to data, and used to retrieve data as well as aggregate them along certain specifications. While SQL (or Structured Querying Language) and Relational Databases are the most commonly used data storage platforms, with the increasing use of Internet, column oriented and noSQL databases are also used to store big data (or data in Peta bytes). This is because the data generated and recorded by machines is getting bigger leading to development of big data analytics with emphasis on both statistical rigor as well as computing efficiency. As a career, business analysts, or data scientists are much in demand through the world because of much more rapid growth of the market demand than trained manpower supply. A trained analyst is expected to apply a mix of computer science programming, statistical training , analytical views and business domain knowledge while executing business analytics project.