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Difference between revisions of "Analytics"

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'''Business Value of Analytics'''<ref>The business value of analytics [https://www.oracle.com/business-analytics/what-is-analytics/ Oracle]</ref><br />
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*A new way to work: The nature of [[Business|business]] is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for [[Data Quality|data quality]] and [[Data Security|security]]. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your [[Business Analytics|business analytics]] [[Strategy|strategy]]. The combination of both business-led and IT-led initiatives is the sweet spot for [[Innovation|innovation]].
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*Uncover new Opportunities: Advancements in analytics technology are creating new opportunities for you to capitalize on your [[Data|data]]. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.
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*Visualize your data: You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.
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'''Risks and Challenges of Analytics'''<ref>Risks and Challenges of Analytics [https://en.wikipedia.org/wiki/Analytics Wikipedia]</ref><br />
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''Risks''<br />
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The main [[Risk|risk]] for the people is discrimination like price discrimination or statistical discrimination. See Scientific American book review of "Weapons of math destruction"
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There is also the risk that a developer could [[Profit|profit]] from the ideas or work done by users, like this example: Users could write new ideas in a note taking app, which could then be sent as a custom event, and the developers could profit from those ideas. This can happen because the ownership of content is usually unclear in the law.
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If a user's identity is not protected, there are more risks; for example, the risk that private information about users is made public on the [[Internet|internet]]. In the extreme, there is the risk that governments could gather too much private information, now that the governments are giving themselves more powers to access citizens' information.
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''Challenges''<br />
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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|big data]] is a problem for many [[Business|businesses]] that operate transactional systems online and, as a result, amass large volumes of data quickly.
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The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from [[Structured Data|structured data]] in that its format varies widely and cannot be stored in traditional relational [[Database (DB)|databases]] without significant effort at [[Data Transformation|data transformation]]. Sources of [[Unstructured Data|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]] 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. 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.
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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|innovation]] is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively [[Parallel Processing|parallel processing]] by distributing the workload to many computers all with equal access to the complete data set.
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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. 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.
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One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, [[Basel III]] and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, [[Cloud Computing|cloud computing]] and [[Open Source|open source]] [[Programming Language|programming language]] R can help smaller banks to adopt [[Risk Analysis|risk analytics]] and support branch level monitoring by applying [[Predictive Analytics|predictive analytics]].
  
  

Revision as of 19:18, 9 March 2021

Business Value of Analytics[1]

  • A new way to work: The nature of business is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for data quality and security. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.
  • Uncover new Opportunities: Advancements in analytics technology are creating new opportunities for you to capitalize on your data. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.
  • Visualize your data: You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.


Risks and Challenges of Analytics[2]

Risks
The main risk for the people is discrimination like price discrimination or statistical discrimination. See Scientific American book review of "Weapons of math destruction"

There is also the risk that a developer could profit from the ideas or work done by users, like this example: Users could write new ideas in a note taking app, which could then be sent as a custom event, and the developers could profit from those ideas. This can happen because the ownership of content is usually unclear in the law.

If a user's identity is not protected, there are more risks; for example, the risk that private information about users is made public on the internet. In the extreme, there is the risk that governments could gather too much private information, now that the governments are giving themselves more powers to access citizens' information.

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 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. 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.

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. 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.

One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. 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.



See Also

  1. The business value of analytics Oracle
  2. Risks and Challenges of Analytics Wikipedia