Analytics

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Analytics

Analytics
Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with
recorded information, analytics rely on the simultaneous application of statistics, computer programming and
operations research to quantify performance. Analytics often favors data visualization to communicate insight.
The most common application of analytics is the study of business data with an eye to predicting and improving
business performance in the future.[1] Other fields within the area of analytics are enterprise decision management,
retail analytics, marketing and web analytics, predictive science, credit risk analysis, and fraud analytics.
Analytics can require extensive computation (See Big Data) and thus the algorithms and software used for analytics
harness the most current methods in computer science, statistics, and mathematics.[2]

Analytics vs Analysis
A commonplace distinction between analytics and analysis - in particular data analysis - is that the former tends to
employ methods from the latter #cf. analytical chemistry#. 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, presumably to lay emphasis on the
interdisciplinary and integrative aspect.

Examples
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.

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.

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Analytics

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]

References
[1] Davenport, T.H. (2006). "Competing on Analytics". Harvard Business Review.
[2] Kohavi, Rothleder and Simoudis (2002). "Emerging Trends in Business Analytics". Communications of the ACM 45 (8): 45–48.
[3] Naone, Erica. "The New Big Data" (http:/ / www#technologyreview#com/ computing/ 38397/ ). Technology Review, MIT. . Retrieved
August 22, 2011.
[4] Inmon, Bill (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN 978-0-13-236029-6.
[5] Wise, Lyndsay. "Data Analysis and Unstructured Data" (http:/ / www#dashboardinsight#com/ articles/ business-performance-management/
data-analysis-and-unstructured-data#aspx). Dashboard Insight. . Retrieved February 14, 2011.
[6] "Fake doctors' sick notes for Sale for £25, NHS fraud squad warns" (http:/ / www#telegraph#co#uk/ news/ uknews/ 2626120/
Fake-doctors-sick-notes-for-sale-on-web-for-25-NHS-fraud-squad-warns#html). The Telegraph. . Retrieved August 2008.
[7] "Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data" (http:/ /
www#economist#com/ node/ 18741392). The Economist. May 26, 2011. Archived (http:/ / web#archive#org/ web/ 20110603031738/ http:/ /
www#economist#com/ node/ 18741392) from the original on 3 June 2011. . Retrieved May 26, 2011.
[8] Ortega, Dan. "Mobililty: Fueling a Brainier Business Intelligence" (http:/ / www#itbusinessedge#com/ cm/ community/ features/
guestopinions/ blog/ mobility-fueling-a-brainier-business-intelligence/ ?cs=47491). IT Business Edge. . Retrieved June 21, 2011.
[9] Khambadkone, Krish. "Are You Ready for Big Data?" (http:/ / www#infogain#com/ company/ perspective-big-data#jsp). InfoGain. .
Retrieved February 10, 2011.

External links
• INFORMS' bi-monthly, digital magazine on the analytics profession (http://analyticsmagazine.com/)
• Northwestern University Master of Science in Analytics (http://www.analytics.northwestern.edu/)
• NC State University Institute for Advanced Analytics (http://analytics.ncsu.edu/)

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Article Sources and Contributors

Article Sources and Contributors
Analytics  Source: https://en.wikipedia.org/w/index.php?oldid=506363297  Contributors: 16@r, Aharol, Analytically, Bansipatel, BarryList, Barticus88, Beetstra, BlaineKohl, Brandoneus,
C.Fred, Charles Matthews, CommodiCast, Cyberjacob, DeadEyeArrow, Deineka, Deli nk, Dysprosia, Elringo, Emcien, Ethansdad, Falcon8765, Freakmighty, Gogo Dodo, Gregory787,
Hanswaarle, HikeBandit, Hobophobe, Idea Farm, IvanLanin, James Doehring, Jeff3000, Jimmaths, Jonkerz, Julesd, Kadambarid, Kellylautt, Kerberus13, Kerenb, Kku, Kuru, KyleAraujo,
LittleBenW, Loripiquet, MagneticFlux, Maralia, Melcombe, Michael Hardy, MikeLampaBI, Mkennedy1981, MrOllie, NishithSingh, Ocatecir, Ottawahitech, Paolo787, PitOfBabel, Planbhups,
Prabhu137, Quasipalm, RichardF, Rick lightburn, Ronz, Sanya r, Sergio.ballestrero, SimonP, Simplyuttam, Spugsley, Stephenpace, TFinn734, TheAdamEvans, Tmguru, Trevorallred,
Vishal.dani, Visviva, WhartonCAI, Wikiolap, Zgemignani, 102 anonymous edits

License
Creative Commons Attribution-Share Alike 3.0 Unported
//creativecommons.org/licenses/by-sa/3.0/

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