W8 D566 “Emerging Trends and Impacts”

Project description
Write a one page essay using proper APA format, citations and use 3 sources ( one good source is Business Intelligence and Analytics tenth edition by Ramesh Sharda,

Dursun Delen and Efraim Turban).
1.Is cloud computing just an old wine in a new bottle? From your perspective, how is it similar to other initiatives? How is it different?
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Added on 28.12.2014 13:48
Chapter 14

CHAPTER OVERVIEW

This chapter introduces several emerging technologies that are likely to have major impacts on the development and use of business intelligence applications. Many

other interesting technologies are also emerging, but we have focused on some trends that have already been realized and others that are about to impact analytics

further. Using a crystal ball is always a risky proposition, but this chapter provides a framework for analysis of emerging trends. We introduce and explain some

emerging technologies and explore their current applications. We then discuss the organizational, personal, legal, ethical, and societal impacts of support systems

that may affect their implementation. We conclude with a description of the analytics ecosystem. This section should help readers appreciate different career

possibilities within the realm of analytics.

LOCATION-BASED ANALYTICS FOR ORGANIZATIONS

The goal of this chapter is to illustrate the potential of new technologies when innovative uses are developed by creative minds. Most of the technologies described in

this chapter are promising but have yet to see widespread adoption. Therein lies the opportunity to create the next killer application. Thus far, we have seen many

examples of organizations employing analytical techniques to gain insights into their existing processes through informative reporting, predictive analytics,

forecasting, and optimization techniques. We will explore the future of analytics and consider the impacts of incorporating location-based data. Figure 1 illustrates

the classification of Location-Based Analytics Applications.

Fig. 1 Classification of Location-Based Analytics Applications

Fig. 1 Classification of Location-Based Analytics Applications

GEOSPATIAL ANALYTICS

A consolidated view of the overall performance of an organization is usually represented through visualization tools that provide actionable information. The

information may include current and forecasted values of various business factors and key performance indicators (KPIs). Looking at the key performance indicators as

overall numbers via various graphs and charts can be overwhelming. There is a high risk of missing potential growth opportunities or of not identifying the problematic

areas. As an alternative to simply viewing reports, organizations employ visual maps that are geographically mapped and based on traditional location data, usually

grouped by the postal codes.

By incorporating demographic details into locations, retailers can determine how sales vary by population level and proximity to other competitors. Additionally, they

can assess the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint

locations, and easily trace them back to the produces. Finally, sales reps can better target their prospects by analyzing their geography.

REAL-TIME LOCATION INTELLIGENCE

Many devices in use by consumers and professionals are constantly sending out their location information. Cars, buses, taxis, mobile phones, cameras, and personal

navigation devices all transmit their locations thanks to network-connected positioning technologies such as GPS, WiFi, and cell tower triangulation. Millions of

consumers and businesses use location-enabled devices for finding nearby services, engaging in sports, games and hobbies. This surge in location-enabled services has

resulted in a massive database of historical and real-time streaming location information. It is, of course, scattered and by itself not very useful. Indeed, a new

name has been given to this type of data mining reality mining. Reality mining builds on the idea that these location-enabled data sets could provide remarkable real-

time insight into aggregate human activity trends.

By analyzing and learning from these large-scale patterns of movement, it is possible to identify distinct classes of behavior within specific contexts. This approach

allows a business to better understand its customer patterns and to make more informed decisions about promotions, pricing, and so on. By applying algorithms that

reduce the dimensionality of location data, one can characterize places according to the activity and movement between them. From massive amounts of high-dimensional

location data, these algorithms uncover trends, meaning, and relationships to eventually produce human-understandable representations. It then becomes possible to use

such data to automatically make intelligent predictions and find important matches and similarities between places and people.

ANALYTICS APPLICATIONS FOR CONSUMERS

The explosive growth of the apps industry for smartphone platforms and the use of analytics are also creating tremendous opportunities for developing apps that the

consumers can use directly. These apps differ from the previous category in that these are meant for direct use by a consumer rather than an organization that is

trying to mine a consumers usage/purchase data to create a profile for marketing specific products or services to them. Predictably, these apps are meant for enabling

consumers to do their job better. One key concern in employing these technologies is the loss of privacy. If someone can track the movement of a cell phone, the

privacy of that customer is a big issue. Some of the app developers claim that they only need to gather aggregate flow information, not individually identifiable

information. But many stories appear in the media that highlight violations of this general principal Both users and developers of such apps have to be aware of the

harmful effect of giving out private information as well as collecting such information.

RECOMMENDATION ENGINES

In most decision situations, people rely on recommendations gathered either directly from other people or indirectly through the aggregated recommendations made by

others in the form of reviews and ratings posted either in newspapers, product guides, or online. Such information sharing is considered one of the major reasons for

the success of online retailers such as Amazon.com.

The term recommender systems refers to a Web-based information filtering system that takes the inputs from users and then aggregates the inputs to provide

recommendations for other users in their product or service selection choices. Some recommender systems now even try to predict the rating or preference that a user

would give for a particular product or service. The data necessary to build a recommendation system are collected by Web-based systems where each user is specifically

asked to rate an item on a rating scale, rank the items from most favorite to least favorite, and asked to list the attributes of the items that they like the best.

Two basic approaches that are employed in the development of recommendation systems are collaborative filtering and content filtering. In collaborative filtering, the

recommendation system is built based on the individual users past behavior by keeping track of the previous history of all purchased items. This includes products,

items that are viewed most often, and ratings that are given by the users to the items they purchased. These individual profile histories with items preferences are

grouped with other similar user-item profile histories to build a comprehensive set of relations between users and items, which are then used to predict what the user

will like and recommend items accordingly.

Content-based recommender systems overcome one of the disadvantages of collaborative filtering recommender systems by considering specifications and characteristic of

items. In the content-based filtering approach, the

Chapter 14

CHAPTER OVERVIEW

This chapter introduces several emerging technologies that are likely to have major impacts on the development and use of business intelligence applications. Many

other interesting technologies are also emerging, but we have focused on some trends that have already been realized and others that are about to impact analytics

further. Using a crystal ball is always a risky proposition, but this chapter provides a framework for analysis of emerging trends. We introduce and explain some

emerging technologies and explore their current applications. We then discuss the organizational, personal, legal, ethical, and societal impacts of support systems

that may affect their implementation. We conclude with a description of the analytics ecosystem. This section should help readers appreciate different career

possibilities within the realm of analytics.

LOCATION-BASED ANALYTICS FOR ORGANIZATIONS

The goal of this chapter is to illustrate the potential of new technologies when innovative uses are developed by creative minds. Most of the technologies described in

this chapter are promising but have yet to see widespread adoption. Therein lies the opportunity to create the next �killer� application. Thus far, we have seen many

examples of organizations employing analytical techniques to gain insights into their existing processes through informative reporting, predictive analytics,

forecasting, and optimization techniques. We will explore the future of analytics and consider the impacts of incorporating location-based data. Figure 1 illustrates

the classification of Location-Based Analytics Applications.

Fig. 1 Classification of Location-Based Analytics Applications

Fig. 1 Classification of Location-Based Analytics Applications

GEOSPATIAL ANALYTICS

A consolidated view of the overall performance of an organization is usually represented through visualization tools that provide actionable information. The

information may include current and forecasted values of various business factors and key performance indicators (KPIs). Looking at the key performance indicators as

overall numbers via various graphs and charts can be overwhelming. There is a high risk of missing potential growth opportunities or of not identifying the problematic

areas. As an alternative to simply viewing reports, organizations employ visual maps that are geographically mapped and based on traditional location data, usually

grouped by the postal codes.

By incorporating demographic details into locations, retailers can determine how sales vary by population level and proximity to other competitors. Additionally, they

can assess the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint

locations, and easily trace them back to the produces. Finally, sales reps can better target their prospects by analyzing their geography.

REAL-TIME LOCATION INTELLIGENCE

Many devices in use by consumers and professionals are constantly sending out their location information. Cars, buses, taxis, mobile phones, cameras, and personal

navigation devices all transmit their locations thanks to network-connected positioning technologies such as GPS, WiFi, and cell tower triangulation. Millions of

consumers and businesses use location-enabled devices for finding nearby services, engaging in sports, games and hobbies. This surge in location-enabled services has

resulted in a massive database of historical and real-time streaming location information. It is, of course, scattered and by itself not very useful. Indeed, a new

name has been given to this type of data mining � reality mining. Reality mining builds on the idea that these location-enabled data sets could provide remarkable

real-time insight into aggregate human activity trends.

By analyzing and learning from these large-scale patterns of movement, it is possible to identify distinct classes of behavior within specific contexts. This approach

allows a business to better understand its customer patterns and to make more informed decisions about promotions, pricing, and so on. By applying algorithms that

reduce the dimensionality of location data, one can characterize places according to the activity and movement between them. From massive amounts of high-dimensional

location data, these algorithms uncover trends, meaning, and relationships to eventually produce human-understandable representations. It then becomes possible to use

such data to automatically make intelligent predictions and find important matches and similarities between places and people.

ANALYTICS APPLICATIONS FOR CONSUMERS

The explosive growth of the apps industry for smartphone platforms and the use of analytics are also creating tremendous opportunities for developing apps that the

consumers can use directly. These apps differ from the previous category in that these are meant for direct use by a consumer rather than an organization that is

trying to mine a consumer�s usage/purchase data to create a profile for marketing specific products or services to them. Predictably, these apps are meant for enabling

consumers to do their job better. One key concern in employing these technologies is the loss of privacy. If someone can track the movement of a cell phone, the

privacy of that customer is a big issue. Some of the app developers claim that they only need to gather aggregate flow information, not individually identifiable

information. But many stories appear in the media that highlight violations of this general principal Both users and developers of such apps have to be aware of the

harmful effect of giving out private information as well as collecting such information.

RECOMMENDATION ENGINES

In most decision situations, people rely on recommendations gathered either directly from other people or indirectly through the aggregated recommendations made by

others in the form of reviews and ratings posted either in newspapers, product guides, or online. Such information sharing is considered one of the major reasons for

the success of online retailers such as Amazon.com.

The term �recommender systems� refers to a Web-based information filtering system that takes the inputs from users and then aggregates the inputs to provide

recommendations for other users in their product or service selection choices. Some recommender systems now even try to predict the rating or preference that a user

would give for a particular product or service. The data necessary to build a recommendation system are collected by Web-based systems where each user is specifically

asked to rate an item on a rating scale, rank the items from most favorite to least favorite, and asked to list the attributes of the items that they like the best.

Two basic approaches that are employed in the development of recommendation systems are collaborative filtering and content filtering. In collaborative filtering, the

recommendation system is built based on the individual user�s past behavior by keeping track of the previous history of all purchased items. This includes products,

items that are viewed most often, and ratings that are given by the users to the items they purchased. These individual profile histories with items preferences are

grouped with other similar user-item profile histories to build a comprehensive set of relations between users and items, which are then used to predict what the user

will like and recommend items accordingly.

Content-based recommender systems overcome one of the disadvantages of collaborative filtering recommender systems by considering specifications and characteristic of

items. In the content-based filtering approach, the characteristics of an item are profiled first and then content-based individual user profiles are built to store

the information about the characteristics of specific items that the user has rated in the past. In the recommendation process, a comparison is made by filtering the

item information from the user profile for which the user has rated positively and compares these characteristics with any new products that the user has not rated

yet. Recommendations are then offered when products are identified that have similar characteristics.

WEB 2.0 AND ONLINE SOCIAL NETWORKING

Web 2.0 is the popular term for describing advanced Web technologies and applications, including blogs, wikis, RSS, mashups, user-generated content and social

networks. A major objective of Web 2.0 is to enhance creativity, information sharing, and collaboration. One of the most significant differences between Web 2.0 and

the traditional Web is the increased collaboration among Internet users and other users, content providers, and enterprises.

SOCIAL NETWORKING

Social networking is built on the idea that there is structure to how people know each other and interact. The basic premise is that social networking gives people the

power to share, making the world more open and connected. Although social networking is usually practiced in social networks such as LinkedIn, Facebook, or Google+,

aspects of it are also found in Wikipedia and YouTube.

CLOUD COMPUTING AND BI

Another emerging technology trend that business intelligence users should be aware of is cloud computing. This is a style of computing in which dynamically scalable

and often virtualized resources are provided over the Internet. Users need not have knowledge of, experience in, or control over the technology infrastructures in the

cloud that supports them. Cloud computing represents an evolution of all previously shared / centralized computing trends.

Although we do not typically look at Web-based e-mail as an example of cloud computing, it can be considered a basic cloud application. Typically, the user logs into

their account via a web browser. The e-mail server stores the data and manages the user�s front-end experience (software). It is accessible from multiple systems and

platforms, thus providing a �cloud� experience. Thus, any Web-based application is a general example of cloud-based technologies. Many other opportunities to harness

the cloud exist in various services that are usually described as -aaS. (e.g. ANALYTICS-as-a-Service, INFORMATION-as-a-Service, etc.) These types of cloud-based

offerings are continuing to grow in popularity. A major advantage of these offerings is the rapid diffusion of advanced analytical tools among the users, without

significant investment in technology acquisition. However, a number of concerns have been raised about cloud computing, including the loss of control and privacy,

legal liabilities, cross-border political issues, and so on. Nonetheless, cloud computing is an important initiative for BI Professionals to watch.

ISSUES OF LEGALITY, PRIVACY, AND ETHICS

The introduction of analytics may compound a host of legal issues already relevant to computer systems. For example, questions concerning liability for the actions of

advice provided by intelligent machines are just beginning to be considered. In addition to resolving disputes about the unexpected and possibly damaging results of

some analytics, other complex issues may surface. For instance, who is liable if an enterprise finds itself bankrupt as a result of using the advice of an analytic

application? Will the enterprise itself be held responsible for not testing the system adequately before entrusting it with sensitive issues? Will auditing and

accounting firms share the liability for failing to apply adequate auditing rests? Will the software developers of intelligent systems be jointly liable? Consider the

following specific legal issues:
�What is the value of an expert opinion in court when the expertise is encoded in a computer?
�Who is liable for wrong advice provided by an intelligent application?
�What happens if a manger enters an incorrect judgment value into an analytic application and the result is damage or a disaster?
�Who owns the knowledge in a knowledge base?
�Can management force experts to contribute their expertise?

Privacy means different things to different people. In general, privacy is the right to be left alone and the right to be free from unreasonable personal intrusions.

Privacy has long been a legal, ethical, and social issue in many countries. The right to privacy is recognized today in every state of the United States and by the

federal government, either by statute or by common law. The definition of privacy can be interpreted quite broadly. However, the following two rules have been followed

fairly closely in past court decisions. These two rules show why it is difficult, in some cases, to determine and enforce privacy regulations.
1.The right of privacy is not absolute. Privacy must be balanced against the needs of society.
2.The public�s right to know is superior to the individual�s right to privacy.

As we can see, these emerging technologies offer the promise of fantastic and even unheard of capabilities. However, this same �progress� must be managed and considered

to ensure that people are protected as we march towards an automated and robotic infrastructure and lifestyle

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