Analytics
Encyclopedia
Analytics is the application of computer technology, operational research, and statistics
to solve problems in business and industry. Analytics is carried out within an information system
: while, in the past, statistics and mathematics could be studied without computers and software, analytics has evolved from the application of computers to the analysis of data and this takes place within an information system or software environment. Mathematics underpins the algorithms used in analytics - the science of analytics is concerned with extracting useful properties of data using computable functions (see Church-Turing thesis), and typically will involve extracting properties from large data bases (see data mining
). Analytics therefore bridges the disciplines of computer science, statistics, and mathematics.
A simple definition of analytics is "the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.
Common applications of analytics include the study of business data using statistical analysis in order to discover and understand historical patterns with an eye to predicting and improving business performance in the future. Also, some people use the term to denote the use of mathematics in business. Others hold that the field of analytics includes the use of operations research, statistics and probability. However, it would be erroneous to limit the field of analytics to only statistics and mathematics.
Analytics closely resembles statistical analysis and data mining
, but tends to be based on modeling involving extensive computation. Some fields within the area of analytics are enterprise decision management
, marketing analytics, predictive science, strategy science, credit risk analysis and fraud analytics.
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.
For instance, 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.
Web analytics allows marketers to collect session-level information about interactions on a website. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture.
. 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
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 legally 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. 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.
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.
Statistics
Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....
to solve problems in business and industry. Analytics is carried out within an information system
Information system
An information system - or application landscape - is any combination of information technology and people's activities that support operations, management, and decision making. In a very broad sense, the term information system is frequently used to refer to the interaction between people,...
: while, in the past, statistics and mathematics could be studied without computers and software, analytics has evolved from the application of computers to the analysis of data and this takes place within an information system or software environment. Mathematics underpins the algorithms used in analytics - the science of analytics is concerned with extracting useful properties of data using computable functions (see Church-Turing thesis), and typically will involve extracting properties from large data bases (see data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
). Analytics therefore bridges the disciplines of computer science, statistics, and mathematics.
A simple definition of analytics is "the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.
Common applications of analytics include the study of business data using statistical analysis in order to discover and understand historical patterns with an eye to predicting and improving business performance in the future. Also, some people use the term to denote the use of mathematics in business. Others hold that the field of analytics includes the use of operations research, statistics and probability. However, it would be erroneous to limit the field of analytics to only statistics and mathematics.
Analytics closely resembles statistical analysis and data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
, but tends to be based on modeling involving extensive computation. Some fields within the area of analytics are enterprise decision management
Enterprise Decision Management
Enterprise decision management, commonly abbreviated "EDM", entails all aspects of managing automated decision design and deployment that an organization uses to manage its interactions with customers, employees and suppliers...
, marketing analytics, predictive science, strategy science, credit risk analysis and fraud analytics.
Example: Portfolio analysis
A common application of business analytics is portfolioPortfolio (finance)
Portfolio is a financial term denoting a collection of investments held by an investment company, hedge fund, financial institution or individual.-Definition:The term portfolio refers to any collection of financial assets such as stocks, bonds and cash...
analysis. In this, a bank
Bank
A bank is a financial institution that serves as a financial intermediary. The term "bank" may refer to one of several related types of entities:...
or lending agency has a collection of accounts of varying value
Value (economics)
An economic value is the worth of a good or service as determined by the market.The economic value of a good or service has puzzled economists since the beginning of the discipline. First, economists tried to estimate the value of a good to an individual alone, and extend that definition to goods...
and risk
Risk
Risk is the potential that a chosen action or activity will lead to a loss . The notion implies that a choice having an influence on the outcome exists . Potential losses themselves may also be called "risks"...
. 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
Loan
A loan is a type of debt. Like all debt instruments, a loan entails the redistribution of financial assets over time, between the lender and the borrower....
with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.
For instance, 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
Time series
In statistics, signal processing, econometrics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the...
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.
Example: Marketing optimization
Marketing has evolved from a creative process into a highly data-driven process. 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.Web analytics allows marketers to collect session-level information about interactions on a website. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture.
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 dataBig data
Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing...
. 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
Unstructured Data refers to information that either does not have a pre-defined data model and/or does not fit well into relational tables. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well...
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
Business intelligence
Business intelligence mainly refers to computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes....
for businesses, governments and universities. For example, in Britain the discovery that one company was legally 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. 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.
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
Complex Event Processing
Complex event processing consists of processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time....
, 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.
See also
- AnalysisAnalysisAnalysis is the process of breaking a complex topic or substance into smaller parts to gain a better understanding of it. The technique has been applied in the study of mathematics and logic since before Aristotle , though analysis as a formal concept is a relatively recent development.The word is...
- Big dataBig dataBig data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing...
- Business analyticsBusiness analyticsBusiness analytics refers to the skills, technologies, applications and practices for continuous 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...
- Business intelligenceBusiness intelligenceBusiness intelligence mainly refers to computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes....
- Complex event processingComplex Event ProcessingComplex event processing consists of processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time....
- Data miningData miningData mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...
- Data presentation architectureData Presentation ArchitectureData presentation architecture is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proffer knowledge.-Origin and context:...
- Learning analyticsLearning analyticsLearning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs . A related field is educational data mining....
- List of software engineering topics
- Machine learningMachine learningMachine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
- Mobile analytics
- Online analytical processing
- Online video analyticsOnline video analyticsOnline video analytics, also known as web video analytics, is way of measuring how viewers get to an online video and what they do when they watch it. A video is any length of video stream, such as a movie clip, video advertisement, movie trailer, television show or full-length video...
- Operations researchOperations researchOperations research is an interdisciplinary mathematical science that focuses on the effective use of technology by organizations...
- Predictive analyticsPredictive analyticsPredictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events....
- StatisticsStatisticsStatistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....
- Web analyticsWeb analyticsWeb analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage....