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Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in
data In the pursuit of knowledge, data (; ) is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpret ...
. It 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 Computer programming is the process of performing a particular computation (or more generally, accomplishing a specific computing result), usually by designing and building an executable computer program. Programming involves tasks such as anal ...
, and
operations research Operations research ( en-GB, operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a discipline that deals with the development and application of analytical methods to improve dec ...
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 Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. In busin ...
, prescriptive analytics, and cognitive analytics. 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. According to IDC, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. As per
Gartner Gartner, Inc is a technological research and consulting firm based in Stamford, Connecticut that conducts research on technology and shares this research both through private consulting as well as executive programs and conferences. Its clients ...
, the overall analytic platforms software market grew by $25.5 billion in 2020.


Analytics vs analysis

Data analysis Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, en ...
focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. 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. Data analytics is used to formulate larger organization decisions. Data analytics is a
multidisciplinary Interdisciplinarity or interdisciplinary studies involves the combination of multiple academic disciplines into one activity (e.g., a research project). It draws knowledge from several other fields like sociology, anthropology, psychology, ec ...
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. 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 Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
techniques like
neural networks A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
, decision trees, logistic regression, linear to multiple
regression analysis In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one ...
, and classification to do predictive modeling. It also includes unsupervised machine learning techniques like
cluster analysis Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of ...
, Principal Component Analysis, segmentation profile analysis and association analysis.


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. 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. 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. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify the IP address, and track activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture. 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. 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. People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. HR analytics is the application of analytics to help companies manage
human resources Human resources (HR) is the set of people who make up the workforce of an organization, business sector, industry, or economy. A narrower concept is human capital, the knowledge and skills which the individuals command. Similar terms include ...
. Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. For example, inspection of the strategic phenomenon of employee turnover utilizing People Analytics Tools may serve as an important analysis at times of disruption. It has been suggested that People Analytics is a separate discipline to HR analytics, representing a greater focus on business issues rather than administrative processes, and that People Analytics may not really belong within Human Resources in organizations. However, experts disagree on this, with many arguing that Human Resources will need to develop People Analytics as a key part of a more capable and strategic business function in the changing world of work brought on by automation. Instead of moving People Analytics outside HR, some experts argue that it belongs in HR, albeit enabled by a new breed of HR professional who is more data-driven and business savvy.


Portfolio analytics

A common application of business analytics is portfolio analysis. In this, a
bank A bank is a financial institution that accepts deposits from the public and creates a demand deposit while simultaneously making loans. Lending activities can be directly performed by the bank or indirectly through capital markets. Becau ...
or lending agency has a collection of accounts of varying value and
risk In simple terms, risk is the possibility of something bad happening. Risk involves uncertainty about the effects/implications of an activity with respect to something that humans value (such as health, well-being, wealth, property or the environme ...
. 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 In finance, a loan is the lending of money by one or more individuals, organizations, or other entities to other individuals, organizations, etc. The recipient (i.e., the borrower) incurs a debt and is usually liable to pay interest on that ...
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 In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. E ...
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.


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. Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like online payment gateway companies to analyse if a transaction was genuine or fraud. 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.


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. This also includes the SEO (
search engine optimization Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. SEO targets unpaid traffic (known as "natural" or " organic" results) rather than di ...
) where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics. A growing number of brands and marketing firms rely on digital analytics for their
digital marketing Digital marketing is the component of marketing that uses the Internet and online based digital technologies such as desktop computers, mobile phones and other digital media and platforms to promote products and services. Its development dur ...
assignments, where MROI (Marketing Return on Investment) is an important key performance indicator (KPI).


Security analytics

Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest risk. Products in this area include
security information and event management Security information and event management (SIEM) is a field within the field of computer security, where software products and services combine security information management (SIM) and security event management (SEM). They provide real-time a ...
and user behavior analytics.


Software analytics

Software analytics is the process of collecting information about the way a piece of
software Software is a set of computer programs and associated software documentation, documentation and data (computing), data. This is in contrast to Computer hardware, hardware, from which the system is built and which actually performs the work. ...
is used and produced.


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. The analysis of
unstructured data Unstructured data (or unstructured information) 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, n ...
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. 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. 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 is an opportunity for insurance firms to increase the vigilance of their unstructured
data analysis Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, en ...
. 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. 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. Analytics is increasingly used in
education Education is a purposeful activity directed at achieving certain aims, such as transmitting knowledge or fostering skills and character traits. These aims may include the development of understanding, rationality, kindness, and honesty. ...
, 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. 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. 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.


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.


See also


References


External links

* {{Wiktionary-inline * Financial data analysis Formal sciences Business terms Business intelligence Big data