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{{Short description|Discovery, interpretation, and communication of meaningful patterns in data}}
{{others}}
{{Use mdy dates|date=January 2021}}{{multiple issues|{{More footnotes needed|date=December 2021}}
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[[File:English Wikipedia's traffic by browser family.png|thumb|right|Traffic analysis of Wikipedia]]

'''Analytics''' is the systematic computational analysis of data or [[statistics]].<ref>{{Cite web|title=Oxford definition of analytics|url=https://www.lexico.com/en/definition/analytics|archive-url=https://web.archive.org/web/20200810063114/https://www.lexico.com/en/definition/analytics|url-status=dead|archive-date=August 10, 2020}}</ref> It is used for the discovery, interpretation, and communication of meaningful patterns in [[data]], which also falls under and directly relates to the umbrella term, [[data science]].<ref>{{Cite journal |last1=Agarwal |first1=Ritu |last2=Dhar |first2=Vasant |date=2014-09-25 |title=Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research |url=https://pubsonline.informs.org/doi/10.1287/isre.2014.0546 |journal=Information Systems Research |language=en |volume=25 |issue=3 |pages=443–448 |doi=10.1287/isre.2014.0546 |issn=1047-7047|url-access=subscription }}</ref> Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of [[statistics]], [[computer programming]], and [[operations research]] to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, [[predictive analytics]], [[prescriptive analytics]], and cognitive analytics.<ref>{{Cite web|title=Cognitive Analytics - combining Artificial Intelligence (AI) and Data Analytics|url=https://www.ulster.ac.uk/cognitive-analytics-research/cognitive-analytics|access-date=2022-01-07|website=www.ulster.ac.uk|date=March 8, 2017|language=en-GB|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110151505/https://www.ulster.ac.uk/cognitive-analytics-research/cognitive-analytics|url-status=live}}</ref> Analytics may apply to a variety of fields such as [[marketing]], [[management]], [[finance]], online systems, [[information security]], and [[software services]]. Since analytics can require extensive computation (see [[big data]]), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.<ref>{{cite journal|last=Kohavi, Rothleder and Simoudis|year=2002|title=Emerging Trends in Business Analytics|journal=Communications of the ACM|volume=45|issue=8|pages=45–48|citeseerx=10.1.1.13.3005|doi=10.1145/545151.545177|s2cid=15938729}}</ref> According to [[International Data Corporation]], global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021.<ref>{{cite web | url=https://www.idc.com/getdoc.jsp?containerId=prUS48165721 | title=Global Spending on Big Data and Analytics Solutions Will Reach $215.7 Billion in 2021, According to a New IDC Spending Guide | access-date=July 24, 2022 | archive-date=July 23, 2022 | archive-url=https://web.archive.org/web/20220723034825/https://www.idc.com/getdoc.jsp?containerId=prUS48165721 | url-status=live }}</ref><ref>{{cite web | url=https://www.statista.com/statistics/551501/worldwide-big-data-business-analytics-revenue/ | title=Big data and business analytics revenue 2022 | access-date=July 24, 2022 | archive-date=July 20, 2022 | archive-url=https://web.archive.org/web/20220720234346/https://www.statista.com/statistics/551501/worldwide-big-data-business-analytics-revenue/ | url-status=live }}</ref> As per [[Gartner]], the overall analytic platforms software market grew by $25.5 billion in 2020.<ref>{{cite web | url=https://www.gartner.com/en/documents/4001844 | title=Market Share: Data and Analytics Software, Worldwide, 2020 | access-date=July 24, 2022 | archive-date=October 3, 2022 | archive-url=https://web.archive.org/web/20221003072519/https://www.gartner.com/en/documents/4001844 | url-status=live }}</ref>  

== Analytics vs analysis ==
{{Confusing section|reason=it is still not clear what the difference between analytics and analysis is|small=no|date=March 2018}}[[Data analysis]] focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment.<ref name=":0">{{Cite book|last=Kelleher|first=John D.|url=https://www.worldcat.org/oclc/1162184998|title=Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies|date=2020|others=Brian Mac Namee, Aoife D'Arcy|isbn=978-0-262-36110-1|edition=2|location=Cambridge, Massachusetts|pages=16|oclc=1162184998}}</ref> It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.<ref>{{cite web|last1=Park|first1=David|title=Analysis vs. Analytics: Past vs. Future|url=https://www.eetimes.com/analysis-vs-analytics-past-vs-future/|access-date=January 20, 2021|website=EE Times|date=August 28, 2017 |archive-date=January 29, 2021|archive-url=https://web.archive.org/web/20210129075027/https://www.eetimes.com/analysis-vs-analytics-past-vs-future/|url-status=live}}</ref>{{Unreliable source?|date=January 2022}} Data analytics is used to formulate larger organizational decisions. {{Citation needed|date=January 2022}}

Data analytics is a [[Academic discipline|multidisciplinary]] field. There is extensive use of computer skills, mathematics, statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data through analytics.{{cn|date=September 2023}} There is increasing use of the term ''advanced analytics'', typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of [[machine learning]] techniques like [[Artificial neural network|neural networks]], decision trees, logistic regression, linear to multiple [[regression analysis]], and classification to do [[predictive modeling]].<ref name="forbes2">{{cite web|title=AI, Big Data & Advanced Analytics In The Supply Chain|url=https://www.forbes.com/sites/yasamankazemi/2019/01/29/ai-big-data-advanced-analytics-in-the-supply-chain/#13da8727244f|access-date=April 16, 2020|work=[[Forbes.com]]|archive-date=June 23, 2022|archive-url=https://web.archive.org/web/20220623114612/https://www.forbes.com/sites/yasamankazemi/2019/01/29/ai-big-data-advanced-analytics-in-the-supply-chain/#13da8727244f|url-status=live}}</ref><ref name=":0" /> It also includes [[Unsupervised learning|unsupervised machine learning techniques]] like [[cluster analysis]], [[principal component analysis]], segmentation profile analysis and association analysis.{{cn|date=August 2023}}

== Applications ==
=== Marketing optimization ===
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.<ref name=":1">{{Cite journal|last1=Wedel|first1=Michel|last2=Kannan|first2=P.K.|date=November 1, 2016|title=Marketing Analytics for Data-Rich Environments|url=https://journals.sagepub.com/doi/10.1509/jm.15.0413|journal=Journal of Marketing|volume=80|issue=6|pages=97–121|doi=10.1509/jm.15.0413|s2cid=168410284|issn=0022-2429|access-date=January 10, 2022|archive-date=March 31, 2022|archive-url=https://web.archive.org/web/20220331114129/https://journals.sagepub.com/doi/10.1509/jm.15.0413|url-status=live|url-access=subscription}}</ref>

Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions about brand and revenue outcomes. The process involves predictive modelling, marketing experimentation, automation and real-time sales communications. The data enables companies to make predictions and alter strategic execution to maximize performance results.<ref name=":1" />

[[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.<ref>{{Cite web|title=Session - Analytics Help|url=https://support.google.com/analytics/answer/6086069|access-date=2022-01-09|website=support.google.com|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110161138/https://support.google.com/analytics/answer/6086069|url-status=live}}</ref> Those interactions provide [[web analytics]] information systems with the information necessary to track the referrer, search keywords, identify the IP address,<ref>{{Cite web|title=IP address - Analytics Help|url=https://support.google.com/analytics/answer/6322282|access-date=2022-01-09|website=support.google.com|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110152845/https://support.google.com/analytics/answer/6322282|url-status=live}}</ref> and track the activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.<ref>{{Cite web|title=Analytics Tools & Solutions for Your Business - Google Analytics|url=https://marketingplatform.google.com/about/analytics/|access-date=2022-01-09|website=Google Marketing Platform|language=en|archive-date=October 2, 2022|archive-url=https://web.archive.org/web/20221002000102/https://marketingplatform.google.com/about/analytics/|url-status=live}}</ref>

Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics, e.g., segmentation. Web analytics and optimization of websites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that ''marketing mix modeling'' is commonly referred to as ''attribution modeling'' in the digital or [[marketing mix modeling]] context.{{Citation needed|date=January 2022}}

These tools and techniques support both strategic marketing decisions (such as how much overall to spend on marketing, how to allocate budgets across a portfolio of brands and the marketing mix) and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost-effective medium at the ideal time.

=== People analytics ===
People analytics uses behavioral data to understand how people work and change how companies are managed.<ref>{{Cite news|last=lukem|date=November 4, 2016|title=People Analytics: Transforming Management with Behavioral Data|language=en|work=Programs for Professionals {{!}} MIT Professional Education|url=http://professional.mit.edu/programs/short-programs/people-analytics|access-date=April 3, 2018|archive-date=September 8, 2018|archive-url=https://web.archive.org/web/20180908215628/http://professional.mit.edu/programs/short-programs/people-analytics|url-status=live}}</ref> It can be referred to by various names, depending on the context, the purpose of the analytics, or the specific focus of the analysis. Some examples include workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and [[Human resources information systems|human resources information system]] (HRIS) analytics. HR analytics is the application of analytics to help companies manage [[human resources]].<ref>{{cite web|author=Chalutz Ben-Gal, Hila|year=2019|title=An ROI-based review of HR analytics: practical implementation tools|url=http://www.eng.tau.ac.il/~bengal/Chalutz_ROI.pdf|publisher=Personnel Review, Vol. 48 No. 6, pp. 1429-1448|access-date=February 9, 2020|archive-date=October 30, 2021|archive-url=https://web.archive.org/web/20211030030759/http://www.eng.tau.ac.il/~bengal/Chalutz_ROI.pdf|url-status=dead}}</ref> 

HR analytics has become a strategic tool in analyzing and forecasting human-related trends in the changing labor markets, using career analytics tools.<ref>{{cite web|author=Sela, A., Chalutz Ben-Gal, Hila|year=2018|title=Career Analytics: data-driven analysis of turnover and career paths in knowledge-intensive firms: Google, Facebook and others.|url=http://www.eng.tau.ac.il/~bengal/Chalutz_Career_Analytics.pdf|publisher=In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE). IEEE.|access-date=February 9, 2020|archive-date=March 31, 2022|archive-url=https://web.archive.org/web/20220331114136/http://www.eng.tau.ac.il/~bengal/Chalutz_Career_Analytics.pdf|url-status=dead}}</ref> The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems.<ref>{{cite web|title=People analytics - University of Pennsylvania|url=https://www.coursera.org/learn/wharton-people-analytics|publisher=Coursera|access-date=May 3, 2017|archive-date=April 19, 2019|archive-url=https://web.archive.org/web/20190419050653/https://www.coursera.org/learn/wharton-people-analytics|url-status=live}}</ref> For example, inspection of the strategic phenomenon of employee turnover utilizing people analytics tools may serve as an important analysis at times of disruption. <ref>{{cite web|last1=Avrahami |first1=D. |last2=Pessach |first2=D. |last3=Singer |first3=G. |last4=Chalutz Ben-Gal |first4=Hila|year=2022|title=A human resources analytics and machine-learning examination of turnover: implications for theory and practice|url=https://english.afeka.ac.il/media/2103973/10-1108_ijm-12-2020-0548.pdf|publisher=International Journal of Manpower, Vol. ahead-of-print No. ahead-of-print.|access-date=July 27, 2022|archive-date=April 2, 2022|archive-url=https://web.archive.org/web/20220402205805/https://english.afeka.ac.il/media/2103973/10-1108_ijm-12-2020-0548.pdf|url-status=dead}}</ref> 

It has been suggested that people analytics is a separate discipline to HR analytics, with a greater focus on addressing business issues, while HR Analytics is more concerned with metrics related to HR processes.<ref>{{Cite news |date=August 2, 2017 |title=People Analytics: MIT July 24, 2017 |url=https://www.hrexaminer.com/people-analytics-mit-july-24-2017/ |url-status=live |archive-url=https://web.archive.org/web/20190428144809/https://www.hrexaminer.com/people-analytics-mit-july-24-2017/ |archive-date=April 28, 2019 |access-date=April 3, 2018 |work=HR Examiner |language=en |quote=Waber makes a key distinction between People Analytics and HR Analytics. “People Analytics solves business problems. HR Analytics solves HR problems,” he says. People Analytics looks at the work and its social organization. HR Analytics measures and integrates data about HR administrative processes.}}</ref> Additionally, people analytics may now extend beyond the human resources function in organizations.<ref>{{Cite news|last=Bersin|first=Josh|title=The Geeks Arrive In HR: People Analytics Is Here|language=en|work=Forbes|url=https://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/|access-date=April 3, 2018|archive-date=September 20, 2019|archive-url=https://web.archive.org/web/20190920081915/https://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/|url-status=live}}</ref> However, experts find that many HR departments are burdened by operational tasks and need to prioritize people analytics and automation to become a more strategic and capable business function in the evolving world of work, rather than producing basic reports that offer limited long-term value.<ref>{{Cite web|title=The CEO's guide to competing through HR|url=https://www.mckinsey.com/business-functions/organization/our-insights/the-ceos-guide-to-competing-through-hr|access-date=July 24, 2020|language=en|archive-date=July 24, 2020|archive-url=https://web.archive.org/web/20200724100542/https://www.mckinsey.com/business-functions/organization/our-insights/the-ceos-guide-to-competing-through-hr|url-status=live}}</ref> Some experts argue that a change in the way HR departments operate is essential. Although HR functions were traditionally centered on administrative tasks, they are now evolving with a new generation of data-driven HR professionals who serve as strategic business partners.<ref>{{Cite news|last=McNulty|first=Keith|title=It's Time for HR 3.0|language=en|work=Talent Economy|url=https://www.chieflearningofficer.com/2018/04/23/its-time-for-hr-3-0/|access-date=July 24, 2020|archive-date=July 3, 2020|archive-url=https://web.archive.org/web/20200703031310/https://www.chieflearningofficer.com/2018/04/23/its-time-for-hr-3-0/|url-status=live}}</ref>

Examples of HR analytic metrics include [[employee lifetime value]] (ELTV), labour cost expense percent, union percentage, etc.{{Citation needed|date=October 2024}}

=== Portfolio analytics ===
A common application of business analytics is [[portfolio analysis]]. In this, a [[bank]] or lending agency has a collection of accounts of varying [[Value (economics)|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.<ref>{{Citation|last=Pilbeam|first=Keith|title=Portfolio Analysis: Risk and Return in Financial Markets|date=2005|url=https://doi.org/10.1007/978-1-349-26273-1_7|work=Finance and Financial Markets|pages=156–187|editor-last=Pilbeam|editor-first=Keith|place=London|publisher=Macmillan Education UK|language=en|doi=10.1007/978-1-349-26273-1_7|isbn=978-1-349-26273-1|access-date=2022-01-09|url-access=subscription}}</ref>

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.{{Citation needed|date=January 2022}}

=== Risk analytics ===
Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. [[Credit score]]s are built to predict an individual's delinquency behavior and are widely used to evaluate the credit worthiness of each applicant.<ref>{{Cite web|title=Credit Reports and Scores {{!}} USAGov|url=https://www.usa.gov/credit-reports|access-date=2022-01-09|website=www.usa.gov|language=en|archive-date=January 8, 2022|archive-url=https://web.archive.org/web/20220108192256/https://www.usa.gov/credit-reports|url-status=live}}</ref> Furthermore, risk analyses are carried out in the scientific world<ref>{{Cite journal|last1=Mayernik|first1=Matthew S.|last2=Breseman|first2=Kelsey|last3=Downs|first3=Robert R.|last4=Duerr|first4=Ruth|last5=Garretson|first5=Alexis|last6=Hou|first6=Chung-Yi (Sophie)|last7=Committee|first7=Environmental Data Governance Initiative (EDGI) and Earth Science Information Partners (ESIP) Data Stewardship|date=2020-03-12|title=Risk Assessment for Scientific Data|journal=Data Science Journal|language=en|volume=19|issue=1|pages=10|doi=10.5334/dsj-2020-010|s2cid=215873228|issn=1683-1470|doi-access=free}}</ref> and the insurance industry.<ref>{{Cite web|date=2020-10-28|title=Predictive Analytics in Insurance: Types, Tools, and the Future|url=https://online.maryville.edu/blog/predictive-analytics-in-insurance/|access-date=2022-01-09|website=Maryville Online|language=en-US|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110151505/https://online.maryville.edu/blog/predictive-analytics-in-insurance/|url-status=live}}</ref> It is also extensively used in financial institutions like [[online payment]] gateway companies to analyse if a transaction was genuine or fraud.<ref>{{Cite journal|last1=Liébana-Cabanillas|first1=Francisco|last2=Singh|first2=Nidhi|last3=Kalinic|first3=Zoran|last4=Carvajal-Trujillo|first4=Elena|date=2021-06-01|title=Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach|url=https://doi.org/10.1007/s10799-021-00328-6|journal=Information Technology and Management|language=en|volume=22|issue=2|pages=133–161|doi=10.1007/s10799-021-00328-6|s2cid=234834347|issn=1573-7667|url-access=subscription}}</ref> For this purpose, they use the transaction history of the customer. This is more commonly used in Credit Card purchases, 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 loss due to such circumstances.<ref>{{Cite web|last=Crail|first=Chauncey|date=2021-03-09|title=How to Enable Mobile Credit Card Alerts for Purchases and Fraud|url=https://www.forbes.com/advisor/credit-cards/how-to-enable-mobile-credit-card-alerts-for-purchases-and-fraud/|access-date=2022-01-09|website=Forbes Advisor|language=en-US|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110153005/https://www.forbes.com/advisor/credit-cards/how-to-enable-mobile-credit-card-alerts-for-purchases-and-fraud/|url-status=live}}</ref>

=== Digital analytics ===
Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automation.<ref>Phillips, Judah "Building a Digital Analytics Organization" Financial Times Press, 2013, pp 7–8.</ref> This also includes the SEO ([[search engine optimization]]) where the keyword search is tracked and that data is used for marketing purposes.<ref>{{Cite web|title=SEO Starter Guide: The Basics {{!}} Google Search Central|url=https://developers.google.com/search/docs/beginner/seo-starter-guide|access-date=2022-01-09|website=Google Developers|language=en|archive-date=January 12, 2022|archive-url=https://web.archive.org/web/20220112013705/https://developers.google.com/search/docs/beginner/seo-starter-guide|url-status=live}}</ref> Even banner ads and clicks come under digital analytics.<ref>{{Cite web|title=Clickthrough rate (CTR): Definition - Google Ads Help|url=https://support.google.com/google-ads/answer/2615875|access-date=2022-01-09|website=support.google.com|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110152723/https://support.google.com/google-ads/answer/2615875|url-status=live}}</ref> A growing number of brands and marketing firms rely on digital analytics for their [[digital marketing]] assignments, where [[Return on marketing investment|marketing return on investment (MROI)]] is an important [[Performance indicator|key performance indicator]] (KPI).{{Citation needed|date=January 2022}}

=== Security analytics ===
Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest security risks.<ref>{{cite web|title=Security analytics shores up hope for breach detection|url=http://enterpriseinnovation.net/article/security-analytics-shores-hope-breach-detection-192448485|url-status=dead|archive-url=https://web.archive.org/web/20190212184120/https://www.enterpriseinnovation.net/article/security-analytics-shores-hope-breach-detection-192448485|archive-date=February 12, 2019|access-date=April 27, 2015|publisher=Enterprise Innovation}}</ref><ref>{{Cite book|last=Talabis|first=Mark Ryan M.|url=https://www.worldcat.org/oclc/910911974|title=Information security analytics : finding security insights, patterns, and anomalies in big data|date=2015|others=Robert McPherson, I Miyamoto, Jason L. Martin|isbn=978-0-12-800506-4|location=Waltham, MA|pages=1|oclc=910911974}}</ref> Products in this area include [[security information and event management]] and user behavior analytics.

=== Software analytics ===
{{main|Software analytics}}
Software analytics is the process of collecting information about the way a piece of [[software]] is used and produced.<ref>{{Cite web|title=Software Analytics - an overview {{!}} ScienceDirect Topics|url=https://www.sciencedirect.com/topics/computer-science/software-analytics|access-date=2022-01-09|website=www.sciencedirect.com|archive-date=January 11, 2022|archive-url=https://web.archive.org/web/20220111091707/https://www.sciencedirect.com/topics/computer-science/software-analytics|url-status=live}}</ref>

== 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]].<ref name=":2">{{Cite web|title=2.3 Ten common characteristics of big data|url=https://www.bitbybitbook.com/en/1st-ed/observing-behavior/characteristics/|access-date=2022-01-10|website=www.bitbybitbook.com|language=en|archive-date=March 31, 2022|archive-url=https://web.archive.org/web/20220331114208/https://www.bitbybitbook.com/en/1st-ed/observing-behavior/characteristics/|url-status=live}}</ref> 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.<ref>{{cite web|last=Naone|first=Erica|title=The New Big Data|url=https://www.technologyreview.com/2011/08/22/192225/the-new-big-data/|access-date=August 22, 2011|publisher=Technology Review, MIT|archive-date=May 20, 2022|archive-url=https://web.archive.org/web/20220520143457/https://www.technologyreview.com/2011/08/22/192225/the-new-big-data/|url-status=live}}</ref><ref name=":2"/>

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.<ref>{{cite book|last1=Inmon|first1=Bill|title=Tapping Into Unstructured Data|last2=Nesavich|first2=Anthony|publisher=Prentice-Hall|year=2007|isbn=978-0-13-236029-6}}</ref> 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.<ref>{{cite web|last=Wise|first=Lyndsay|title=Data Analysis and Unstructured Data|url=http://www.dashboardinsight.com/articles/business-performance-management/data-analysis-and-unstructured-data.aspx|url-status=dead|archive-url=https://web.archive.org/web/20140105045015/http://www.dashboardinsight.com/articles/business-performance-management/data-analysis-and-unstructured-data.aspx|archive-date=January 5, 2014|access-date=February 14, 2011|publisher=Dashboard Insight}}</ref><ref>{{Cite web|title=Tapping the power of unstructured data|url=https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data|access-date=2022-01-10|website=MIT Sloan|date=February 2021 |language=en|archive-date=January 10, 2022|archive-url=https://web.archive.org/web/20220110151504/https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data|url-status=live}}</ref> 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<ref>{{cite news|date=August 26, 2008|title=Fake doctors' sick notes for Sale for £25, NHS fraud squad warns|newspaper=The Telegraph|location=London|url=https://www.telegraph.co.uk/news/uknews/2626120/Fake-doctors-sick-notes-for-sale-on-web-for-25-NHS-fraud-squad-warns.html |archive-url=https://ghostarchive.org/archive/20220112/https://www.telegraph.co.uk/news/uknews/2626120/Fake-doctors-sick-notes-for-sale-on-web-for-25-NHS-fraud-squad-warns.html |archive-date=January 12, 2022 |url-access=subscription |url-status=live|access-date=September 16, 2011}}{{cbignore}}</ref> is an opportunity for insurance firms to increase the vigilance of their unstructured [[data analysis]].<ref>{{cite news|date=May 26, 2011|title=Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data|newspaper=The Economist|url=http://www.economist.com/node/18741392|url-status=live|archive-url=https://web.archive.org/web/20110603031738/http://www.economist.com/node/18741392|archive-date=June 3, 2011}}</ref>{{Original research inline|date=January 2022}}

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]],<ref>{{Cite journal|last1=Flouris|first1=Ioannis|last2=Giatrakos|first2=Nikos|last3=Deligiannakis|first3=Antonios|last4=Garofalakis|first4=Minos|last5=Kamp|first5=Michael|last6=Mock|first6=Michael|date=2017-05-01|title=Issues in complex event processing: Status and prospects in the Big Data era|url=https://www.sciencedirect.com/science/article/pii/S0164121216300802|journal=Journal of Systems and Software|language=en|volume=127|pages=217–236|doi=10.1016/j.jss.2016.06.011|issn=0164-1212|access-date=January 10, 2022|archive-date=April 14, 2019|archive-url=https://web.archive.org/web/20190414070609/http://www.sciencedirect.com/science/article/pii/S0164121216300802|url-status=live|url-access=subscription}}</ref> full text search and analysis, and even new ideas in presentation. 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.<ref>{{Cite journal|last1=Yang|first1=Ning|last2=Liu|first2=Diyou|last3=Feng|first3=Quanlong|last4=Xiong|first4=Quan|last5=Zhang|first5=Lin|last6=Ren|first6=Tianwei|last7=Zhao|first7=Yuanyuan|last8=Zhu|first8=Dehai|last9=Huang|first9=Jianxi|date=2019-06-25|title=Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids|journal=Remote Sensing|volume=11|issue=12|pages=1500|doi=10.3390/rs11121500|bibcode=2019RemS...11.1500Y |issn=2072-4292|doi-access=free}}</ref>

Analytics is increasingly used in [[education]], particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc.<ref>{{Cite book|last1=Prinsloo|first1=Paul|last2=Slade|first2=Sharon|title=Proceedings of the Seventh International Learning Analytics & Knowledge Conference |chapter=An elephant in the learning analytics room |date=2017-03-13|chapter-url=https://doi.org/10.1145/3027385.3027406|series=LAK '17|location=New York, NY, USA|publisher=Association for Computing Machinery|pages=46–55|doi=10.1145/3027385.3027406|isbn=978-1-4503-4870-6|s2cid=9490514|url=http://oro.open.ac.uk/48944/2/LAK_2017_paper_89.pdf }}</ref> For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data.<ref>U.S. Department of Education 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)</ref> To combat this, some analytics tools for educators adhere to an [[over-the-counter data]] format (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.<ref>Rankin, J. (March 28, 2013). [https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2 How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.] {{Webarchive|url=https://web.archive.org/web/20190326201414/https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2|date=March 26, 2019}} ''Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.''</ref>

=== Risks ===
Risks for the general population include [[discrimination]] on the basis of characteristics such as gender, skin colour, ethnic origin or political opinions, through mechanisms such as [[price discrimination]] or [[Statistical discrimination (economics)|statistical discrimination]].<ref>{{Cite journal|last1=Favaretto|first1=Maddalena|last2=De Clercq|first2=Eva|last3=Elger|first3=Bernice Simone|date=2019-02-05|title=Big Data and discrimination: perils, promises and solutions. A systematic review|journal=[[Journal of Big Data]]|volume=6|issue=1|pages=12|doi=10.1186/s40537-019-0177-4|s2cid=59603476|issn=2196-1115|doi-access=free}}</ref>

== See also ==
{{columns-list|*[[Analysis]]
*[[Analytic applications]]
*[[Architectural analytics]]
*[[Behavioral analytics]]
*[[Business analytics]]
*[[Business intelligence]]
*[[Cloud analytics]]
*[[Complex event processing]]
*[[Continuous analytics]]
*[[Cultural analytics]]
*[[Customer analytics]]
*[[Dashboard (business)]]
*[[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]]
*[[Prediction]]
*[[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]]|colwidth=20em}}

== References ==
{{Reflist}}


== External links ==
* {{Wiktionary-inline}}

[[Category:Analytics| ]]
[[Category:Financial data analysis]]
[[Category:Formal sciences]]
[[Category:Business intelligence terms]]
[[Category:Big data]]