 | This article has multiple issues. Please helpimprove it or discuss these issues on thetalk page.(Learn how and when to remove these messages) (Learn how and when to remove this message)
|
Analytics is the systematic computational analysis of data orstatistics.[1] It is used for the discovery, interpretation, and communication of meaningful patterns indata, which also falls under and directly relates to the umbrella term,data science.[2] 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 ofstatistics,computer programming, andoperations 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.[3] Analytics may apply to a variety of fields such asmarketing,management,finance, online systems,information security, andsoftware services. Since analytics can require extensive computation (seebig data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.[4] According toInternational Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021.[5][6] As perGartner, the overall analytic platforms software market grew by $25.5 billion in 2020.[7]
 | This sectionmay beconfusing or unclear to readers. In particular, it is still not clear what the difference between analytics and analysis is. Please helpclarify the section. There might be a discussion about this onthe talk page.(March 2018) (Learn how and when to remove this message) |
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment.[8] 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.[9][unreliable source?] Data analytics is used to formulate larger organizational decisions.[citation needed]
Data analytics is amultidisciplinary 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.[citation needed] There is increasing use of the termadvanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use ofmachine learning techniques likeneural networks, decision trees, logistic regression, linear to multipleregression analysis, and classification to dopredictive modeling.[10][8] It also includesunsupervised machine learning techniques likecluster analysis,principal component analysis, segmentation profile analysis and association analysis.[citation needed]
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 andpanel data to understand and communicate marketing strategy.[11]
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.[11]
Web analytics allows marketers to collect session-level information about interactions on a website using an operation calledsessionization.Google Analytics is an example of a popular free analytics tool that marketers use for this purpose.[12] Those interactions provideweb analytics information systems with the information necessary to track the referrer, search keywords, identify the IP address,[13] and track the activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.[14]
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 thatmarketing mix modeling is commonly referred to asattribution modeling in the digital ormarketing mix modeling context.[citation needed]
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 uses behavioral data to understand how people work and change how companies are managed.[15] 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, andhuman resources information system (HRIS) analytics. HR analytics is the application of analytics to help companies managehuman resources.[16]
HR analytics has become a strategic tool in analyzing and forecasting human-related trends in the changing labor markets, using career analytics tools.[17] The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems.[18] For example, inspection of the strategic phenomenon of employee turnover utilizing people analytics tools may serve as an important analysis at times of disruption.[19]
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.[20] Additionally, people analytics may now extend beyond the human resources function in organizations.[21] 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.[22] 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.[23]
Examples of HR analytic metrics includeemployee lifetime value (ELTV), labour cost expense percent, union percentage, etc.[citation needed]
A common application of business analytics isportfolio analysis. In this, abank or lending agency has a collection of accounts of varyingvalue andrisk. 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 theloan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.[24]
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 combinetime 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]
Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers.Credit scores are built to predict an individual's delinquency behavior and are widely used to evaluate the credit worthiness of each applicant.[25] Furthermore, risk analyses are carried out in the scientific world[26] and the insurance industry.[27] It is also extensively used in financial institutions likeonline payment gateway companies to analyse if a transaction was genuine or fraud.[28] 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.[29]
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.[30] This also includes the SEO (search engine optimization) where the keyword search is tracked and that data is used for marketing purposes.[31] Banner ads, clicks, and social media metrics track bysocial media analytics, a part of digital analytics.[32] A growing number of brands and marketing firms rely on digital analytics for theirdigital marketing assignments, wheremarketing return on investment (MROI) is an importantkey performance indicator (KPI).[citation needed]
Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest security risks.[33][34] Products in this area includesecurity information and event management and user behavior analytics.
Software analytics is the process of collecting information about the way a piece ofsoftware is used and produced.[35]
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 asbig data.[36] 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.[37][36]
The analysis ofunstructured data types is another challenge getting attention in the industry. Unstructured data differs fromstructured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.[38] Sources of unstructured data, such as email, the contents of word processor documents, PDFs,geospatial data, etc., are rapidly becoming a relevant source ofbusiness intelligence for businesses, governments and universities.[39][40] 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[41] is an opportunity for insurance firms to increase the vigilance of their unstructureddata analysis.[42][original research?]
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 ascomplex event processing,[43] 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 ofmassively parallel processing by distributing the workload to many computers all with equal access to the complete data set.[44]
Analytics is increasingly used ineducation, 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.[45] 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.[46] To combat this, some analytics tools for educators adhere to anover-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.[47]
Risks for the general population includediscrimination on the basis of characteristics such as gender, skin colour, ethnic origin or political opinions, through mechanisms such asprice discrimination orstatistical discrimination.[48]
{{cite book}}: CS1 maint: location missing publisher (link)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.
{{cite book}}: CS1 maint: location missing publisher (link)[page needed]
The dictionary definition ofanalytics at Wiktionary