Analytics
Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. Analytics is carried out within an information system: while, in the past, statistics and mathematics could be studied without computers and software, analytics has evolved from the application of computers to the analysis of data and this takes place within an information system or software environment. Mathematics underpins the algorithms used in analytics - the science of analytics is concerned with extracting useful properties of data using computable functions (see Church-Turing thesis), and typically will involve extracting properties from large data bases (see data mining). Analytics therefore bridges the disciplines of computer science, statistics, and mathematics.[1]
A simple definition of analytics is "the science of analysis". A practical definition, however, would be that analytics is the process of developing optimal or realistic decision recommendations based on insights derived through the application of statistical models and analysis against existing and/or simulated future data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.
Common applications of analytics include the study of business data using statistical analysis in order to discover and understand historical patterns with an eye to predicting and improving business performance in the future.[2] Also, some people use the term to denote the use of mathematics in business. Others hold that the field of analytics includes the use of operations research, statistics, and probability. However, it would be erroneous to limit the field of analytics to only statistics and mathematics.
Analytics closely resembles statistical analysis and data mining, but tends to be based on modeling involving extensive computation. Some fields within the area of analytics are enterprise decision management, retail analytics, marketing analytics, predictive science, strategy science, credit risk analysis, and fraud analytics.
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[edit] Example: Portfolio analysis
A common application of business analytics is portfolio analysis. In this, a bank or lending 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 may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis, with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.
[edit] Example: Marketing optimization
Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.
Web analytics allows marketers to collect session-level information about interactions on a website. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture.
[edit] Challenges
In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.[3]
The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.[4] Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities.[5] For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies,[6] is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.[7]
These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.[8] 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 all with equal access to the complete data set.[9]
[edit] See also
[edit] References
- ^ Kohavi, Rothleder and Simoudis (2002). "Emerging Trends in Business Analytics". Communications of the ACM 45 (8): 45–48.
- ^ Davenport, T.H. (2006). "Competing on Analytics". Harvard Business Review.
- ^ Naone, Erica. "The New Big Data". Technology Review, MIT. http://www.technologyreview.com/computing/38397/. Retrieved August 22, 2011.
- ^ Inmon, Bill (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN 978-0132360296.
- ^ Wise, Lyndsay. "Data Analysis and Unstructured Data". Dashboard Insight. http://www.dashboardinsight.com/articles/business-performance-management/data-analysis-and-unstructured-data.aspx. Retrieved February 14, 2011.
- ^ "Fake doctors' sick notes for Sale for £25, NHS fraud squad warns". London: The Telegraph. http://www.telegraph.co.uk/news/uknews/2626120/Fake-doctors-sick-notes-for-sale-on-web-for-25-NHS-fraud-squad-warns.html. Retrieved August 2008.
- ^ "Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data". The Economist. May 26, 2011. http://www.economist.com/node/18741392. Retrieved May 26, 2011.
- ^ Ortega, Dan. "Mobililty: Fueling a Brainier Business Intelligence". IT Business Edge. http://www.itbusinessedge.com/cm/community/features/guestopinions/blog/mobility-fueling-a-brainier-business-intelligence/?cs=47491. Retrieved June 21, 2011.
- ^ Khambadkone, Krish. "Are You Ready for Big Data?". InfoGain. http://www.infogain.com/company/perspective-big-data.jsp. Retrieved February 10, 2011.
[edit] External links
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