Analytics

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Analytics is the systematic computational analysis of data or statistics. [1] 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. [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 of statistics, computer programming, and operations research to quantify performance.

Contents

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 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. [4] According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. [5] [6] As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020. [7]

Analytics vs analysis

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 a 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.[ citation needed ] 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 neural networks, decision trees, logistic regression, linear to multiple regression analysis, and classification to do predictive modeling. [10] [8] It also includes unsupervised machine learning techniques like cluster analysis, principal component analysis, segmentation profile analysis and association analysis.[ citation needed ]

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. [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 called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. [12] Those interactions provide web 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 that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing 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

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, and human resources information system (HRIS) analytics. HR analytics is the application of analytics to help companies manage human 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 include employee lifetime value (ELTV), labour cost expense percent, union percentage, etc.[ citation needed ]

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 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. [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 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 ]

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 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 like online 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

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] Even banner ads and clicks come under digital analytics. [32] A growing number of brands and marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is an important key performance indicator (KPI).[ citation needed ]

Security analytics

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 include security information and event management and user behavior analytics.

Software analytics

Software analytics is the process of collecting information about the way a piece of software is used and produced. [35]

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. [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 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. [38] 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. [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 unstructured data 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 as complex 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 of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set. [44]

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. [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 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. [47]

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. [48]

See also

Related Research Articles

Customer relationship management (CRM) is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information.

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

A management information system (MIS) is an information system used for decision-making, and for the coordination, control, analysis, and visualization of information in an organization. The study of the management information systems involves people, processes and technology in an organizational context. In other words, it serves, as the functions of controlling, planning, decision making in the management level setting.

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005), there are three perspectives of text mining: information extraction, data mining, and knowledge discovery in databases (KDD). Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.

Design for Six Sigma (DFSS) is a collection of best-practices for the development of new products and processes. It is sometimes deployed as an engineering design process or business process management method. DFSS originated at General Electric to build on the success they had with traditional Six Sigma; but instead of process improvement, DFSS was made to target new product development. It is used in many industries, like finance, marketing, basic engineering, process industries, waste management, and electronics. It is based on the use of statistical tools like linear regression and enables empirical research similar to that performed in other fields, such as social science. While the tools and order used in Six Sigma require a process to be in place and functioning, DFSS has the objective of determining the needs of customers and the business, and driving those needs into the product solution so created. It is used for product or process design in contrast with process improvement. Measurement is the most important part of most Six Sigma or DFSS tools, but whereas in Six Sigma measurements are made from an existing process, DFSS focuses on gaining a deep insight into customer needs and using these to inform every design decision and trade-off.

<span class="mw-page-title-main">Data management</span> Disciplines related to managing data as a resource

Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making.

<span class="mw-page-title-main">SAS (software)</span> Statistical software

SAS is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics. SAS' analytical software is built upon artificial intelligence and utilizes machine learning, deep learning and generative AI to manage and model data. The software is widely used in industries such as finance, insurance, health care and education.

<span class="mw-page-title-main">Unstructured data</span> Information without a formal data model

Unstructured data is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated in documents.

<span class="mw-page-title-main">Google Analytics</span> Web analytics service from Google

Google Analytics is a web analytics service offered by Google that tracks and reports website traffic and also mobile app traffic & events, currently as a platform inside the Google Marketing Platform brand. Google launched the service in November 2005 after acquiring Urchin.

Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription.

HP Information Management Software is a software from the HP Software Division, used to organize, protect, retrieve, acquire, manage, and maintain information. The HP Software Division also offers information analytics software. The amount of data that companies have to deal with has grown tremendously over the past decade, making the management of this information more difficult. The University of California at Berkeley claims the amount of information produced globally increases by 30 percent annually. An April 2010 Information Management article cited a survey in which nearly 90 percent of businesses blame poor performance on data growth. The survey concluded that for many businesses their applications and databases are growing by 50 percent or more annually, making it difficult to manage the rapid expansion of information.

<span class="mw-page-title-main">Big data</span> Extremely large or complex datasets

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.

Prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from descriptive and predictive analytics.

Cloud analytics is a marketing term for businesses to carry out analysis using cloud computing. It uses a range of analytical tools and techniques to help companies extract information from massive data and present it in a way that is easily categorised and readily available via a web browser.

<span class="mw-page-title-main">Social media analytics</span> Process of gathering and analyzing data from social media networks

Social media analytics or social media monitoring is the process of gathering and analyzing data from social networks such as Facebook, Instagram, LinkedIn, or Twitter. A part of social media analytics is called social media monitoring or social listening. It is commonly used by marketers to track online conversations about products and companies. One author defined it as "the art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making."

An intelligence engine is a type of enterprise information management that combines business rule management, predictive, and prescriptive analytics to form a unified information access platform that provides real-time intelligence through search technologies, dashboards and/or existing business infrastructure. Intelligence Engines are process and/or business problem specific, resulting in industry and/or function-specific marketing trademarks associated with them. They can be differentiated from enterprise resource planning (ERP) software in that intelligence engines include organization-level business rules and proactive decision management functionality.

Data-driven marketing is a process where marketers employ a process to gain insights into consumer behavior, including purchasing patterns, advert effectiveness, and browsing habits. Contemporary methods utilize big data strategies to collect and analyze information on consumer interactions and engagements, aiming to predict future behaviors. This analysis involves evaluating existing data, acquiring new data and systematically organizing and interpreting it to improve marketing strategies. The primary objective is to better understand and address customer needs. Market research provides a detailed understanding of consumer preferences

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