Analytics is the discovery, interpretation, and communication of meaningful patterns in data . Especially valuable in areas rich with recorded information, analytics relating to the simultaneous application of statistics , computer programming and operations research to quantify performance.

Organizations can apply analytics to business data to describe, predict, and improve business performance. SPECIFICALLY, areas Within analytics include predictive analytics , prescriptive analytics , enterprise management decision, descriptive analytics, cognitive analytics, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling , web analytics , call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis , and fraud analytics . Since analytics can require extensive computation (seebig data ), the algorithms and software used for analytics harness the most current methods in computer sciences, statistics, and mathematics. [1]

Analytics vs. analysis

Analytics is multidisciplinary . There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data-data analysis. The insights from the data are used to recommend action or to guide decision making in business context. Thus, analytics is not so much concerned with individual analyzes or analysis steps, but with the entire methodology . There is a pronounced tendency to use the term analytics in business settings eg text analytics vs. the more generic text mining to emphasize this perspective. Citation needed ] . There is an increasing use of the termadvanced analytics , citation needed ] typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to predictive modeling .

Application of analytics

Marketing optimization

Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the results of campaigns or efforts. Demographic studies, customer segmentation, joint analysis and other techniques allow marketers to use large amounts of consumer procurement, survey and data panel to understand and communicate marketing strategy.

Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization . Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. These are the information that you need to know about the Internet, search keywords, identify IP address, and track activities of the visitor. With this information, a marketer can improve marketing campaigns, creative content website, and information architecture.

Analysis techniques frequently used in marketing include marketing modeling, pricing and promotion analysis, sales force optimization and customer analytics eg segmentation. Web analytics and optimization of web sites and online campaigns nowadays. A focus is digital media HAS Slightly changed the vocabulary so That marketing mix modeling is Commonly Referred to as attribution modeling in the digital gold marketing mix modeling context.

These tools and techniques support both strategic marketing decisions and more tactical campaign support, in terms of targeting the best potential customer. optimal message in the most cost effective medium at the ideal time.

People analytics

This application for analytics helps companies manage human resources . What is the responsibility to assign, and what to do? [2] For example, an analysis may be made that is most likely to succeed in a particular role, making them the best employees to hire. profiles would be successful and fail. While HR analytics is done for employees within the organization, Customer Segmentation techniques are used on the market to study customer profiles and identify which customers most likely form the target market.

Portfolio analytics

A common application of business analytics is portfolio analysis . In this, a bank or leasing agency has a collection of accounts of varying value and risk . The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.

The least risk loan is a very wealthy, but there is a very limited number of wealthy people. There are many people who can be slow, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution May combines time series analysis with Many other issues in order to make decisions we When to lend money to thesis different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover Any Losses Among members fait que segment .

Risk analytics

Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the creditworthiness of each applicant. Furthermore, risk analyzes are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions such as online payment gateway companies. For this purpose they use the transaction history of the customer. This is more commonly used in Credit Card Purchase, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him / her. This helps in reducing the loss of such circumstances.

Digital analytics

Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyzes, recommendations, optimizations, predictions, and automations. [3] This also includes SEO (Search Engine Optimization) where the keyword is used for marketing purposes. Even banner ads and clicks like under digital analytics. MROI (Marketing Return on Investment) is an important key performance indicator (KPI), a growing number of brands and marketing firms relying on digital analytics for their digital marketing assignments.

Security analytics

Security analytics refers to information technology (IT) to gather and analyze security events to understand and analyze events that pose the greatest risk. [4] Products in this area include security information and event managementand user behavior analytics.

Software analytics

Software analytics is the process of collecting information about the way a piece of software is used and produced.


In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of massive analysis, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data . <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> The problem of big data is that of a problem for many businesses, and a result, amass large volumes of data quickly. [5]

The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in its various formats and can not be stored in traditional databases. [6] Sources of data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are becoming a source of business intelligence for businesses, governments and universities. [7] For example, in the United States, the company was illegally selling fraudulent doctor’s notes in order to assist people in defrauding employers and insurance companies, [8]is an opportunity for insurance companies to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American $ 300 billion per year and the European public sector € 250 billion. [9]

These challenges are the source of innovation in the field of innovation in the field of information processing . [10] One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers with equal access to the complete data set. [11]

Analytics is used in education , particularly at the district and government office levels. However, the complexity of student performance measures in the context of student performance, predictability, probability of student success, etc. For example, 48% of teachers had difficulty asking questions, 36% did not understand given data, and 52% incorrectly interpreted data. [12] To fight this, some analytics tools for educators adhere to an over-the-counter dataformat (embedding labels, supplemental documentation, and a help system, and making key package / display and content decisions) to improve educators’ understanding and use of the analytics being displayed. [13]

One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source R (programming language) can help smaller banks to adopt risk analytics and support level monitoring by applying predictive analytics. quote needed ]


This article may contain original research . Please improve it by verifying the claims made and adding inline quotes . Statements consisting of only original research should be removed. (March 2015) ( Learn how to remove this template message )

The main risk for the People is discrimination like price discrimination or statistical discrimination . See Scientific American book review of “Weapons of Math Destruction”

There is also a possibility for the development of a business model by a user of a business model, which could be used as a business tool. those ideas. This can happen because of the ownership of content is usually unclear in the law. [14]

If a user’s identity is not protected, there are more risks; for example, the risk that private information is made public on the internet.

In the extreme, there is more information than ever before.

Further information: Telecommunications data retention

See also

  • Analysis
  • Analytic applications
  • Architectural analytics
  • Behavioral analytics
  • Business analytics
  • Business intelligence
  • Cloud analytics
  • Complex event processing
  • Continuous analytics
  • Cultural analytics
  • Customer analytics
  • Dashboard
  • Data mining
  • Data presentation architecture
  • Embedded analytics
  • Learning analytics
  • List of software engineering topics
  • Mobile Location Analytics
  • News analytics
  • Online analytical processing
  • Online video analytics
  • Operational reporting
  • Operations research
  • Predictive analytics
  • Predictive engineering analytics
  • Prescriptive analytics
  • Semantic analytics
  • Smart grid
  • Social analytics
  • Software analytics
  • Speech analytics
  • Statistics
  • User behavior analytics
  • Visual analytics
  • Web analytics
  • Win-loss analytics


  1. Jump up^ Kohavi, Rothleder and Simoudis (2002). “Emerging Trends in Business Analytics”. Communications of the ACM . 45 (8): 45-48. doi : 10.1145 / 545151.545177 .
  2. Jump up^ “People Analytics – University of Pennsylvania” . Coursera.
  3. Jump up^ Phillips, Judah “Building a Digital Analytics Organization” Financial Times Press, 2013, pp 7-8.
  4. Jump up^ “Security analytics shores up hope for breach detection” . Enterprise Innovation . Retrieved April 27, 2015 .
  5. Jump up^ Naone, Erica. “The New Big Data” . Technology Review, MIT . Retrieved August 22, 2011 .
  6. Jump up^ Inmon, Bill; Nesavich, Anthony (2007). Tapping Into Unstructured Data . Prentice-Hall. ISBN  978-0-13-236029-6 .
  7. Jump up^ Wise, Lyndsay. “Data Analysis and Unstructured Data” . Dashboard Insight . Retrieved February 14, 2011 .
  8. Jump up^ “Fake doctors’ sick notes for Sale for £ 25, NHS fraud squad warns” . London: The Telegraph. August 26, 2008 . Retrieved 16 September 2011 .
  9. Jump up^ “Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data” . The Economist . May 26, 2011.Archived from the original on 3 June 2011 . Retrieved May 26, 2011 .
  10. Jump up^ Ortega, Dan. “Mobililty: Fueling a Brainier Business Intelligence” . IT Business Edge. Archived from the original on July 5, 2011 . Retrieved June 21, 2011 .
  11. Jump up^ Khambadkone, Krish. “Are You Ready for Big Data?” . Infogain. Archived from the original on March 14, 2011 . Retrieved February 10, 2011 .
  12. Jump up^ US Department of Education’s Office of Planning, Evaluation and Policy Development (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. United States Department of Education (ERIC Document Reproduction Service No. ED504191)
  13. Jump up^ Rankin, J. (2013, March 28). How Data Systems & Reports can help you learn more? Presentation Leadership from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.
  14. Jump up^ Alan Norton (9 July 2012). “10 reasons why I avoid social networking services” . TechRepublic . Retrieved 4 January 2016 .