Analytics is the discovery, interpretation, and communication of meaningful patterns in the data. Particularly valuable in areas rich with recorded information, analytics relies on simultaneous application of statistics, computer programming, and operations research to measure performance.
Organizations can apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within the analytics include predictive analytics, prescriptive analytics, corporate decision management, descriptive analytics, cognitive analysis, Big Data Analytics, retail analytics, store stores and inventory unit optimization, marketing optimization and marketing mix modeling, web analytics, call analysis , speech analysis, sales force size and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Because analytics can require extensive computing (see large data), algorithms and software used for analysis make use of current methods in computer science, statistics, and mathematics.
Video Analytics
Analitik vs. analisis
Data analysis is multidisciplinary. There is extensive use of Computer Skills, math and statistics, the use of descriptive techniques and prediction models to gain valuable knowledge of data analysis. Insights from data are used to recommend actions or to guide deep-rooted decision making within the business context. Thus, the analysis is not so concerned with individual analysis or analytical steps, but with the whole methodology. There is a pronounced tendency to use the term analytics in business settings, e.g. text analytics vs. text mining is more common to emphasize this broader perspective. There is an increasing use of the term advanced analytics , typically used to describe the technical aspects of analytics, especially in emerging fields such as the use of machine learning techniques such as neural networks, decision trees, logistic regression, multiple linear regression analysis, doing predictive modeling. It also includes Unattended Machine learning techniques such as cluster analysis, Major Component Analysis, segmentation profile analysis and association analysis.
Maps Analytics
Apps analytics
Marketing optimization
Marketing has evolved from a creative process to a process that relies heavily on data. Marketing organizations use analysis to determine the outcome of a campaign or effort and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis, and other techniques enable marketers to use a large number of consumer purchases, surveys, and panel data to understand and communicate marketing strategies.
Web Analytics allows marketers to gather session-level information about interactions on websites using an operation called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. These interactions provide web analytic information systems with the information needed to track referrers, search keywords, identify IP addresses, and track visitor activity. With this information, a marketer can improve marketing campaigns, creative website content, and information architecture.
Analytical techniques often used in marketing include marketing mix modeling, pricing and promotion analysis, sales force optimization and customer analytics, for example: segmentation. Web analytics and website optimization and online campaigns now often work together with more traditional marketing analysis techniques. The focus on digital media has slightly altered the vocabulary so that modeling the marketing mix is usually referred to as the attribution model in the context of mixed digital or marketing modeling.
These tools and techniques support both strategic marketing decisions (like how much overall is spent on marketing, how to allocate budgets across brand portfolios and marketing mix) and more tactical campaign support, in terms of targeting the best potential customers with optimal messages in the media the most cost effective at the ideal time.
Analytical person
Analytic People use behavioral data to understand how people work and change how companies manage.
People's analytics are also known as workforce analysis, human resource analytics, talent analysis, people's insight, talent insight, colleague insight, human resource analysis, and HRIS analysis. HR analysis is an analytics application to help companies manage human resources. The objective is to know which employees to hire, which to reward or promote, what responsibilities should be given, and similar human resource issues. HR analysis is becoming increasingly important to understand what behavioral profiles will work and fail. For example, analysis may find that individuals who fit certain types of profiles are those who are most likely to succeed in a particular role, making them the best employees to employ.
However, there are major differences between people's analytics and HR analytics. "Analytics people solve business problems HR analysis solves HR problems People Analytics looks at their work and social organizations HR Analytics measures and integrates data about the HR administration process," said Ben Waber, MIT Media Lab Ph.D. and CEO Humanyze. Josh Bersin, founder and principle at Bersin by Deloitte agrees that people's analytics are an industry bigger than HR Analytics, explaining, "... over time, I believe it's not even included in HR. are in HR to begin with, over time this team is responsible for sales productivity analysis, turnover, retention, accidents, fraud, and even the problems of people who drive customer retention and customer satisfaction... These are all real-problems world business , not a human resource . "
Portfolio analysis
A common application of business analytics is portfolio analysis. In this case, the bank or lending institution has a collection of accounts with various values ​​and risks. Accounts may vary by social status (rich, middle, poor, etc.) of holders, geographical location, net worth, and many other factors. The lender must balance the loan repayment with the risk of default for each loan. The question is how to evaluate the portfolio as a whole.
The smallest possible risk loan for a very rich person, but the number of the rich is very limited. On the other hand, there are many poor people who can be lent, but at greater risk. Some balance must be achieved that maximizes returns and minimizes risk. The analytics solution can combine time series analysis with many other issues to make decisions about when to lend money to different segments of borrowers, or interest rate decisions charged to members of the portfolio segment to cover losses among members in that segment.
Analytics risk
Predictive models in the banking industry were developed to bring assurance across risk scores for each customer. A credit score is created to predict the behavior of individual misbehavior and is widely used to evaluate each applicant's creditworthiness. Furthermore, risk analysis is conducted in the scientific and insurance industries. It is also widely used in financial institutions such as Online Payment Gateway companies to analyze whether a transaction is genuine or a fraud. For this purpose they use a customer transaction history. This is more often used in credit card purchases, when there is a sudden surge in the volume of customer transactions, the customer gets a confirmation call if the transaction was initiated by it. This helps in reducing losses due to such circumstances.
Digital analysis
Digital Analytics is a set of business and technical activities that define, create, collect, verify, or convert digital data into reporting, research, analysis, recommendations, optimization, prediction, and automation. It also includes SEO (Search Engine Optimization) where keyword searches are tracked and the data is used for marketing purposes. Even banner and click ads are under digital analysis. More brands and marketing companies rely on digital analysis for their digital marketing tasks, where MROI (Return on Investment Marketing) is an important key performance indicator (KPI).
Security analytics
Security analytics refers to information technology (IT) to collect and analyze security events to understand and analyze events that pose the greatest risk. Products in this area include information security and event management and user behavior analytics.
Analytical software
Software analysis is the process of gathering information about how software is used and produced.
Challenges
In the commercial analytics software industry, emphasis has arisen on solving the challenge of analyzing complex and massive data sets, often when the data is in a state of constant change. Such data sets are usually referred to as large data. Yet once the problems caused by large data are found only in the scientific community, today's big data is a problem for many businesses that operate online transactional systems and, as a result, collect large volumes of data quickly.
Unstructured data type analysis is another challenge that gets attention in the industry. Unstructured data differs from structured data in highly variable formats and can not be stored in traditional relational databases without significant effort on data transformation. Unstructured data sources, such as emails, word processing documents, PDFs, geospatial data, etc., are quickly becoming sources of business intelligence relevant to businesses, governments and universities. For example, in Britain, the discovery that one company illegally sells a fraudulent doctor's record to assist people in cheating employers and insurance companies is an opportunity for insurance companies to increase their unstructured data analysis awareness. The McKinsey Global Institute estimates that large data analysis could save the American health care system $ 300 billion per year and the European public sector EUR250 billion.
These challenges are the latest inspiration for many innovations in modern analytic information systems, which gave rise to relatively recent machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentations. One such innovation is the introduction of architectures such as gratings in machine analysis, which allow for massive increase of parallel processing speed by distributing workloads to multiple computers all with equal access to the complete data set.
Businesses are beginning to recognize the limitations of current analytical implementation; according to analyst Dave Menninger of Ventana Research, fewer than half of the organizations (42%) feel comfortable letting business users work with data not put up by IT. To reduce these challenges, analytics are increasingly embedded in business processes and applications.
Analytics is increasingly used in education, especially at the district and government office level. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to differentiate patterns in student performance, predict the likelihood of graduation, increase student likelihood of success, etc. For example, in a study involving districts known for strong data use. , 48% of teachers had difficulty asking questions requested by the data, 36% did not understand the data provided, and 52% of the data were misinterpreted. To address this, some analytic tools for educators follow the format of over-the-counter data (embedding labels, supplementary documentation, and assistance systems, and creating key packages/content decisions) to improve the understanding and use of educators from analytics are shown.
Another challenge that arises is the need for dynamic regulation. For example, in the banking industry, Basel III and future capital adequacy needs tend to make smaller banks adopt an internal risk model. In such cases, cloud computing and open source R programming languages ​​can help smaller banks to adopt risk analytics and support branch-level monitoring by applying predictive analysis.
Risk
The main risks to the people are discrimination such as price discrimination or statistical discrimination. See Scientific American's book review on "The weapons of mathematical destruction"
There is also a risk that a developer can benefit from user ideas or work, such as this example: Users can write new ideas in a note-taking app, which can then be sent as special events, and developers can benefit of those ideas. This can happen because ownership of the content is usually unclear in legislation.
If the user's identity is not protected, there is more risk; for example, the risk that personal information about the user is published on the internet.
In the extreme, there is a risk that the government may collect too much personal information, now the government gives more power to access citizen information.
See also
References
External links
- INFORMS's bi-monthly digital magazine on the analytics profession
Source of the article : Wikipedia
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