Analytics is the discovery and communication of meaningful
patterns in data. Especially valuable in areas rich with recorded
information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.
Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, store assortment and SKU optimization, marketing optimization and marketing mix analytics, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation , the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Analytics is a two-sided coin. On one side, it uses descriptive and predictive models to gain valuable knowledge from data - data analysis. On the other, analytics uses this insight to recommend action or to guide decision making - communication. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective.
Mobile Business Intelligence (Mobile BI or Mobile Intelligence) refers to the distribution of business data to mobile devices such as smartphones and tablet computers.
Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.
Although the concept of mobile computing has been prevalent for over a decade, Mobile BI has shown a momentum/growth only very recently.This change has been partly encouraged by a change from the ‘wired world’ to a wireless world with the advantage of smartphones which has led to a new era of mobile computing, especially in the field of BI.
According to the Aberdeen Group, a large number of companies are rapidly undertaking mobile BI owing to a large number of market pressures such as the need for higher efficiency in business processes, improvement in employee productivity (e.g., time spent looking for information), better and faster decision making, better customer service, and delivery of real-time bi-directional data access to make decisions anytime and anywhere.
Source : en.wikipedia.org/wiki/Analytics
Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, store assortment and SKU optimization, marketing optimization and marketing mix analytics, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation , the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Analytics is a two-sided coin. On one side, it uses descriptive and predictive models to gain valuable knowledge from data - data analysis. On the other, analytics uses this insight to recommend action or to guide decision making - communication. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective.
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| A sample Google Analytics dashboard. Tools like this help businesses identify trends and make decisions. |
Risk Analytics
Predictive Models in banking industry is widely developed to bring certainty across the risk scores for individual customers. Credit Scores are built to predict individual’s delinquency behaviour and also scores are widely used to evaluate the credit worthiness of each applicant and rated while processing loan applications.Mobile Business Intelligence (Mobile BI or Mobile Intelligence) refers to the distribution of business data to mobile devices such as smartphones and tablet computers.
Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.
Although the concept of mobile computing has been prevalent for over a decade, Mobile BI has shown a momentum/growth only very recently.This change has been partly encouraged by a change from the ‘wired world’ to a wireless world with the advantage of smartphones which has led to a new era of mobile computing, especially in the field of BI.
According to the Aberdeen Group, a large number of companies are rapidly undertaking mobile BI owing to a large number of market pressures such as the need for higher efficiency in business processes, improvement in employee productivity (e.g., time spent looking for information), better and faster decision making, better customer service, and delivery of real-time bi-directional data access to make decisions anytime and anywhere.
Source : en.wikipedia.org/wiki/Analytics
Pratika, this is very Informative. Thanks for sharing.
ReplyDeleteCould you also share about the scope of Business Intelligence in Mobile Applications