Analytics

From Wikipedia for FEVERv2
Jump to navigation Jump to search

For the ice hockey term, see Analytics (ice hockey). Analytics_sentence_0

Analytics is the systematic computational analysis of data or statistics. Analytics_sentence_1

It is used for the discovery, interpretation, and communication of meaningful patterns in data. Analytics_sentence_2

It also entails applying data patterns towards effective decision making. Analytics_sentence_3

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. Analytics_sentence_4

Organizations may apply analytics to business data to describe, predict, and improve business performance. Analytics_sentence_5

Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Analytics_sentence_6

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. Analytics_sentence_7

Analytics vs. analysis Analytics_section_0

Analysis is focused on understanding the past; what happened and why it happened. Analytics_sentence_8

Analytics focuses on why it happened and what will happen in the future. Analytics_sentence_9

Data analytics is a multidisciplinary field. Analytics_sentence_10

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.. Analytics_sentence_11

The insights from data are used to recommend action or to guide decision making rooted in the business context. Analytics_sentence_12

Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. Analytics_sentence_13

There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. Analytics_sentence_14

There is an 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 tree, logistic regression, linear to multiple regression analysis, classification to do predictive modeling. Analytics_sentence_15

It also includes Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis. Analytics_sentence_16

Applications Analytics_section_1

Marketing optimization Analytics_section_2

Marketing has evolved from a creative process into a highly data-driven process. Analytics_sentence_17

Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Analytics_sentence_18

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. Analytics_sentence_19

Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions in relation to brand and revenue outcomes. Analytics_sentence_20

The process involves predictive modelling, marketing experimentation, automation and real-time sales communications. Analytics_sentence_21

The data enables companies to make predictions and alter strategic execution to maximize performance results. Analytics_sentence_22

Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Analytics_sentence_23

Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Analytics_sentence_24

Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify IP address, and track activities of the visitor. Analytics_sentence_25

With this information, a marketer can improve marketing campaigns, website creative content, and information architecture. Analytics_sentence_26

Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics e.g.: segmentation. Analytics_sentence_27

Web analytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. Analytics_sentence_28

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. Analytics_sentence_29

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. Analytics_sentence_30

People analytics Analytics_section_3

People Analytics is using behavioral data to understand how people work and change how companies are managed. Analytics_sentence_31

People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. Analytics_sentence_32

HR analytics is the application of analytics to help companies manage human resources. Analytics_sentence_33

Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools. Analytics_sentence_34

The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. Analytics_sentence_35

HR analytics is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail. Analytics_sentence_36

For example, an analysis may find that individuals that fit a certain type of profile are those most likely to succeed at a particular role, making them the best employees to hire. Analytics_sentence_37

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. Analytics_sentence_38

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. Analytics_sentence_39

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. Analytics_sentence_40

Portfolio analytics Analytics_section_4

A common application of business analytics is portfolio analysis. Analytics_sentence_41

In this, a bank or lending agency has a collection of accounts of varying value and risk. Analytics_sentence_42

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. Analytics_sentence_43

The lender must balance the return on the loan with the risk of default for each loan. Analytics_sentence_44

The question is then how to evaluate the portfolio as a whole. Analytics_sentence_45

The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. Analytics_sentence_46

On the other hand, there are many poor that can be lent to, but at greater risk. Analytics_sentence_47

Some balance must be struck that maximizes return and minimizes risk. Analytics_sentence_48

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. Analytics_sentence_49

Risk analytics Analytics_section_5

Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Analytics_sentence_50

Credit scores are built to predict individual's delinquency behavior and widely used to evaluate the credit worthiness of each applicant. Analytics_sentence_51

Furthermore, risk analyses are carried out in the scientific world and the insurance industry. Analytics_sentence_52

It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud. Analytics_sentence_53

For this purpose they use the transaction history of the customer. Analytics_sentence_54

This is more commonly used in Credit Card purchase, 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. Analytics_sentence_55

This helps in reducing loss due to such circumstances. Analytics_sentence_56

Digital analytics Analytics_section_6

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 automations. Analytics_sentence_57

This also includes the SEO (search engine optimization) where the keyword search is tracked and that data is used for marketing purposes. Analytics_sentence_58

Even banner ads and clicks come under digital analytics. Analytics_sentence_59

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). Analytics_sentence_60

Security analytics Analytics_section_7

Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest risk. Analytics_sentence_61

Products in this area include security information and event management and user behavior analytics. Analytics_sentence_62

Software analytics Analytics_section_8

Main article: Software analytics Analytics_sentence_63

Software analytics is the process of collecting information about the way a piece of software is used and produced. Analytics_sentence_64

Challenges Analytics_section_9

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. Analytics_sentence_65

Such data sets are commonly referred to as big data. Analytics_sentence_66

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. Analytics_sentence_67

The analysis of unstructured data types is another challenge getting attention in the industry. Analytics_sentence_68

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. Analytics_sentence_69

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. Analytics_sentence_70

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. Analytics_sentence_71

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. Analytics_sentence_72

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. Analytics_sentence_73

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_sentence_74

Analytics is increasingly used in education, particularly at the district and government office levels. Analytics_sentence_75

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. Analytics_sentence_76

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. Analytics_sentence_77

One more emerging challenge is dynamic regulatory needs. Analytics_sentence_78

For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. Analytics_sentence_79

In such cases, cloud computing and open source programming language R can help smaller banks to adopt risk analytics and support branch level monitoring by applying predictive analytics. Analytics_sentence_80

Risks Analytics_section_10

See also Analytics_section_11

Credits to the contents of this page go to the authors of the corresponding Wikipedia page: en.wikipedia.org/wiki/Analytics.