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Internet of Things (IoT) - Very Big Business/Value Opportunity 2025

Information technology and management - Hi Mom How To Make Cake, On this share Information technology and management, I have provide any thing about cake

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Information technology and management


$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016


$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016


$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016

A to Z of Digital Transformation of Business - Marketing, Production, Sales, Supply and Service

Information technology and management - Hi Mom How To Make Cake, On this share Information technology and management, I have provide any thing about cake

you must see


Information technology and management


Digital Transformation Articles - Collection from Blogosphere



Digital Transformation - 2016  articles

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation in the Age of Customer  - Full Report
Digital Transformation of Industries - World Economic Forum White Paper January 2016
Digital transformation: The three steps to success - MKinsey Article
Using IoT Data to Understand How Your Products Perform
What does ‘digital transformation’ really mean? - Marketing Week Article
6 Predictions About The Future Of Digital Transformation


A

B

Browse 100+ Books on Internet of Things - IoT Books
Business Analytics and Marketing Applications - 2016

C

D


Data Analytics - Driving Digital Transformation of Organization

Digital Oilfield of the Future

Digital Printing - Engineering Economic and Cost Analysis

Digital Transformation at Daimler Benz

Digital Transformation Books

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation (DT) is a capstone course integrating the technical and managerial
aspects surrounding increased levels of digitization. As data starts to play a larger role in
managerial decision-making, what are the unique ramifications of these actions for
organizational dynamics? How can new information and communication technologies
(ICT) be deployed across an enterprise? What role does culture, organizational structure
and even adoption patterns play in understanding technology selection, user design and
how to derive value from technology? When analyzing DT, we need to examine change
from two perspectives:
• From a technology perspective: integration of new technologies, normalization of
data, and digitization of business processes.
• From a managerial perspective: new coordination and communication within and
across entities, new organizational forms, changing the information environment
underlying the business, and new incentive structures.
Successful efforts at digitization have to keep both technical and managerial perspectives in
mind. Using a collection of cases, this course will study how the deployment of ICT changes
interactions and processes within organizations, across organizations, within industries,
and across society

Cases discussed in the earlier Term

Case – ITC eChoupal (as a class)
Case – Dubai Port Authority (as a class)
Case –Security Breach at TJX
Case – VW in America
Case – Starbucks Mobile Payments
Case – Threadless, The Business of Community
Case - Project Hugo
Case – Newspapers
Case – Open vs. Closed Ecosystems – Nokia in 2010
Case – TV Disruption – Comcast Corporation

Digital Transformation in the Age of Customer - Full Report

Digital Transformation of Industries - World Economic Forum White Paper January 2016

Digital transformation: The three steps to success - MKinsey Article

E

F

G

H

I


Internet of Things - System Components
Internet of Things (IoT) - Very Big Business/Value Opportunity 2025
Introduction to Data Mining



J

K


L

M

Manufacturing System Digital Transformation and Reengineering


N

O

P

Q


R


S


T


The A-Z of digital transformation


U

Understanding Your Products Through IoT and Data Analytics
Using IoT Data to Understand How Your Products Perform

V

W

What does ‘digital transformation’ really mean? - Marketing Week Article
What is Big Data and What are its Applications? - IBM Experts Explanation


X


Y


Z


Numerals 1 to 100

6 Predictions About The Future Of Digital Transformation


Digital Transformation Articles - Collection from Blogosphere



Digital Transformation - 2016  articles

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation in the Age of Customer  - Full Report
Digital Transformation of Industries - World Economic Forum White Paper January 2016
Digital transformation: The three steps to success - MKinsey Article
Using IoT Data to Understand How Your Products Perform
What does ‘digital transformation’ really mean? - Marketing Week Article
6 Predictions About The Future Of Digital Transformation


A

B

Browse 100+ Books on Internet of Things - IoT Books
Business Analytics and Marketing Applications - 2016

C

D


Data Analytics - Driving Digital Transformation of Organization

Digital Oilfield of the Future

Digital Printing - Engineering Economic and Cost Analysis

Digital Transformation at Daimler Benz

Digital Transformation Books

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation (DT) is a capstone course integrating the technical and managerial
aspects surrounding increased levels of digitization. As data starts to play a larger role in
managerial decision-making, what are the unique ramifications of these actions for
organizational dynamics? How can new information and communication technologies
(ICT) be deployed across an enterprise? What role does culture, organizational structure
and even adoption patterns play in understanding technology selection, user design and
how to derive value from technology? When analyzing DT, we need to examine change
from two perspectives:
• From a technology perspective: integration of new technologies, normalization of
data, and digitization of business processes.
• From a managerial perspective: new coordination and communication within and
across entities, new organizational forms, changing the information environment
underlying the business, and new incentive structures.
Successful efforts at digitization have to keep both technical and managerial perspectives in
mind. Using a collection of cases, this course will study how the deployment of ICT changes
interactions and processes within organizations, across organizations, within industries,
and across society

Cases discussed in the earlier Term

Case – ITC eChoupal (as a class)
Case – Dubai Port Authority (as a class)
Case –Security Breach at TJX
Case – VW in America
Case – Starbucks Mobile Payments
Case – Threadless, The Business of Community
Case - Project Hugo
Case – Newspapers
Case – Open vs. Closed Ecosystems – Nokia in 2010
Case – TV Disruption – Comcast Corporation

Digital Transformation in the Age of Customer - Full Report

Digital Transformation of Industries - World Economic Forum White Paper January 2016

Digital transformation: The three steps to success - MKinsey Article

E

F

G

H

I


Internet of Things - System Components
Internet of Things (IoT) - Very Big Business/Value Opportunity 2025
Introduction to Data Mining



J

K


L

M

Manufacturing System Digital Transformation and Reengineering


N

O

P

Q


R


S


T


The A-Z of digital transformation


U

Understanding Your Products Through IoT and Data Analytics
Using IoT Data to Understand How Your Products Perform

V

W

What does ‘digital transformation’ really mean? - Marketing Week Article
What is Big Data and What are its Applications? - IBM Experts Explanation


X


Y


Z


Numerals 1 to 100

6 Predictions About The Future Of Digital Transformation


Digital Transformation Articles - Collection from Blogosphere



Digital Transformation - 2016  articles

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation in the Age of Customer  - Full Report
Digital Transformation of Industries - World Economic Forum White Paper January 2016
Digital transformation: The three steps to success - MKinsey Article
Using IoT Data to Understand How Your Products Perform
What does ‘digital transformation’ really mean? - Marketing Week Article
6 Predictions About The Future Of Digital Transformation


A

B

Browse 100+ Books on Internet of Things - IoT Books
Business Analytics and Marketing Applications - 2016

C

D


Data Analytics - Driving Digital Transformation of Organization

Digital Oilfield of the Future

Digital Printing - Engineering Economic and Cost Analysis

Digital Transformation at Daimler Benz

Digital Transformation Books

Digital Transformation - CMU Course Page - Syllabus

Digital Transformation (DT) is a capstone course integrating the technical and managerial
aspects surrounding increased levels of digitization. As data starts to play a larger role in
managerial decision-making, what are the unique ramifications of these actions for
organizational dynamics? How can new information and communication technologies
(ICT) be deployed across an enterprise? What role does culture, organizational structure
and even adoption patterns play in understanding technology selection, user design and
how to derive value from technology? When analyzing DT, we need to examine change
from two perspectives:
• From a technology perspective: integration of new technologies, normalization of
data, and digitization of business processes.
• From a managerial perspective: new coordination and communication within and
across entities, new organizational forms, changing the information environment
underlying the business, and new incentive structures.
Successful efforts at digitization have to keep both technical and managerial perspectives in
mind. Using a collection of cases, this course will study how the deployment of ICT changes
interactions and processes within organizations, across organizations, within industries,
and across society

Cases discussed in the earlier Term

Case – ITC eChoupal (as a class)
Case – Dubai Port Authority (as a class)
Case –Security Breach at TJX
Case – VW in America
Case – Starbucks Mobile Payments
Case – Threadless, The Business of Community
Case - Project Hugo
Case – Newspapers
Case – Open vs. Closed Ecosystems – Nokia in 2010
Case – TV Disruption – Comcast Corporation

Digital Transformation in the Age of Customer - Full Report

Digital Transformation of Industries - World Economic Forum White Paper January 2016

Digital transformation: The three steps to success - MKinsey Article

E

F

G

H

I


Internet of Things - System Components
Internet of Things (IoT) - Very Big Business/Value Opportunity 2025
Introduction to Data Mining



J

K


L

M

Manufacturing System Digital Transformation and Reengineering


N

O

P

Q


R


S


T


The A-Z of digital transformation


U

Understanding Your Products Through IoT and Data Analytics
Using IoT Data to Understand How Your Products Perform

V

W

What does ‘digital transformation’ really mean? - Marketing Week Article
What is Big Data and What are its Applications? - IBM Experts Explanation


X


Y


Z


Numerals 1 to 100

6 Predictions About The Future Of Digital Transformation

The Management of Information Systems - Dickson and Wetherbe - Book Information

Information technology and management - Hi Mom How To Make Cake, On this share Information technology and management, I have provide any thing about cake

you must see


Information technology and management


Gary W. Dickinson and James C. Wetherbe
1985 Book
McGraw Hill Book Company



Part I. Introduction
Ch. 1  The MIS Executive

Part 2. MIS Organization

Ch. 2 The Organizational Use of Computers
       3 Organizing and Staffing the MIS Function

Part 3. Managing MIS Personnel
       4. Contingency Management and the MIS Function
       5. Achieving Job Productivity and Satisfaction

Part 4. MIS Planning and Control
       6. Strategic Planning for MIS
       7. Management Assessment and Evaluation of MIS


Part 5. Key Technology Trends and Implications
       8. Database Management Systems
       9. Decision Support Systems
      10. Data Communication Systems
      11. Distributed Data Processing
      12. Advanced Office Systems
      13. Robotics and MIS

Part 6. Managing MIS Development
      14. Systems Analysis and Design Strategies and Procedures
      15. Software Development
      16. Implementation

Part 7. Management of Production and Computer Operations
      17. Computer Capacity Planning
      18. Hardware and Software Acquisition
      19. Computer Operations Management


MISM Course at CMU, Pittsburgh


Master of Information Systems Management (MISM) program was developed from the ground up as a blended business-technology program. Through our program, students will develop better planning, management and technical abilities that focus on the application of technology to create business value.

Quantitative management and technology: Our information systems courses offer a unique blend of technology, management, and strategy.


Information Systems Management (MISM) - 16-Month Track
Unlike our competition, the Master of Information Systems Management (MISM) program was developed from the ground-up as a blended business-technology program. Through our program, you'll develop better planning, management and technical abilities necessary for leading a thriving organization in today's complex, digital world.

The MISM degree requires you to demonstrate proficiency in technology management, IT Strategy, and fundamental business skills.

MISM Course Requirements
The required courses are designed to build core competencies in integrating technology management with business expertise with courses ranging from Economic for IT to Object Oriented Programming in Java. As a student in the MISM program, you also have the flexibility to choose from a range of challenging elective courses designed to help you excel in an increasingly digital world.


Important Points from the Chapters


Ch. 1  The MIS Executive

The emergence of business and management information systems (MIS) in organizations has created an intense demand for well-trained, capable information systems managers to plan, organize, direct and control the powerful technology of computer-based information systems.

The topics presented in the book provide a managerial, organizational, behavioral, and technical treatment of MIS management.

MIS executives must blend management, business, technical, and interpersonal relations.

Ch. 2 The Organizational Use of Computers


Ch 4. Contingency Management and the MIS Function

The basic foundation of the contingency theory is that the effectiveness of a management approach is contingent upon the organizational environment in which it is applied. This abandons the concept that there is a "best way" to manage in all environments.

IS function is divided into three important functions: Systems development, production, technical services.

Ch 5. Achieving Job Productivity and Satisfaction

The most highly regarded of motivational theories are as follows:


  • Maslow's need hierarchy  -  A.G. Maslow 1943
  • Reinforcement  - B.F. Skinner  1953
  • Attribution theory - F. Heider 1958
  • Herzberg's dual factory theory - F. Herzberg 1959
  • Expectancy theory - V. Vroom 1964
  • Goal Setting - E.A. Locke 1976
Practical Guidelines

1. Efforts to increase motivation must first focus on the  employee's needs (Maslow, Herzberg)
2. work assignments and goals should be realistic and clearly defined;  rewards for performance should be practical and fulfill the  motivational needs of the employee (Locke, Vroom). 
3. Consequences or outcomes of good performance must approximate the expectations of the employee (Skinner, Heider)

Positive versus Negative Control of Behavior

Behavioral Management Tasks

Communicating
Imitation
Shaping
Reinforcement (Scheduling)

Ch. 6. Strategic Planning for MIS

A four stage model of MIS planning consisting of strategic planning, organization information requirements analysis, resource planning and allocation, and project planning is discussed in this chapter.

The challenges of MIS planning are:

1. Alignment of  the MIS plan with the overall strategies and objectives of the organization.
2. Design of  an information system structure or architecture for the organization.
3. Allocation of information  system development and operations resources among competing applications.
4. Completing information system projects on time and on schedule.

       


Gary W. Dickinson and James C. Wetherbe
1985 Book
McGraw Hill Book Company



Part I. Introduction
Ch. 1  The MIS Executive

Part 2. MIS Organization

Ch. 2 The Organizational Use of Computers
       3 Organizing and Staffing the MIS Function

Part 3. Managing MIS Personnel
       4. Contingency Management and the MIS Function
       5. Achieving Job Productivity and Satisfaction

Part 4. MIS Planning and Control
       6. Strategic Planning for MIS
       7. Management Assessment and Evaluation of MIS


Part 5. Key Technology Trends and Implications
       8. Database Management Systems
       9. Decision Support Systems
      10. Data Communication Systems
      11. Distributed Data Processing
      12. Advanced Office Systems
      13. Robotics and MIS

Part 6. Managing MIS Development
      14. Systems Analysis and Design Strategies and Procedures
      15. Software Development
      16. Implementation

Part 7. Management of Production and Computer Operations
      17. Computer Capacity Planning
      18. Hardware and Software Acquisition
      19. Computer Operations Management


MISM Course at CMU, Pittsburgh


Master of Information Systems Management (MISM) program was developed from the ground up as a blended business-technology program. Through our program, students will develop better planning, management and technical abilities that focus on the application of technology to create business value.

Quantitative management and technology: Our information systems courses offer a unique blend of technology, management, and strategy.


Information Systems Management (MISM) - 16-Month Track
Unlike our competition, the Master of Information Systems Management (MISM) program was developed from the ground-up as a blended business-technology program. Through our program, you'll develop better planning, management and technical abilities necessary for leading a thriving organization in today's complex, digital world.

The MISM degree requires you to demonstrate proficiency in technology management, IT Strategy, and fundamental business skills.

MISM Course Requirements
The required courses are designed to build core competencies in integrating technology management with business expertise with courses ranging from Economic for IT to Object Oriented Programming in Java. As a student in the MISM program, you also have the flexibility to choose from a range of challenging elective courses designed to help you excel in an increasingly digital world.


Important Points from the Chapters


Ch. 1  The MIS Executive

The emergence of business and management information systems (MIS) in organizations has created an intense demand for well-trained, capable information systems managers to plan, organize, direct and control the powerful technology of computer-based information systems.

The topics presented in the book provide a managerial, organizational, behavioral, and technical treatment of MIS management.

MIS executives must blend management, business, technical, and interpersonal relations.

Ch. 2 The Organizational Use of Computers


Ch 4. Contingency Management and the MIS Function

The basic foundation of the contingency theory is that the effectiveness of a management approach is contingent upon the organizational environment in which it is applied. This abandons the concept that there is a "best way" to manage in all environments.

IS function is divided into three important functions: Systems development, production, technical services.

Ch 5. Achieving Job Productivity and Satisfaction

The most highly regarded of motivational theories are as follows:


  • Maslow's need hierarchy  -  A.G. Maslow 1943
  • Reinforcement  - B.F. Skinner  1953
  • Attribution theory - F. Heider 1958
  • Herzberg's dual factory theory - F. Herzberg 1959
  • Expectancy theory - V. Vroom 1964
  • Goal Setting - E.A. Locke 1976
Practical Guidelines

1. Efforts to increase motivation must first focus on the  employee's needs (Maslow, Herzberg)
2. work assignments and goals should be realistic and clearly defined;  rewards for performance should be practical and fulfill the  motivational needs of the employee (Locke, Vroom). 
3. Consequences or outcomes of good performance must approximate the expectations of the employee (Skinner, Heider)

Positive versus Negative Control of Behavior

Behavioral Management Tasks

Communicating
Imitation
Shaping
Reinforcement (Scheduling)

Ch. 6. Strategic Planning for MIS

A four stage model of MIS planning consisting of strategic planning, organization information requirements analysis, resource planning and allocation, and project planning is discussed in this chapter.

The challenges of MIS planning are:

1. Alignment of  the MIS plan with the overall strategies and objectives of the organization.
2. Design of  an information system structure or architecture for the organization.
3. Allocation of information  system development and operations resources among competing applications.
4. Completing information system projects on time and on schedule.

       


Gary W. Dickinson and James C. Wetherbe
1985 Book
McGraw Hill Book Company



Part I. Introduction
Ch. 1  The MIS Executive

Part 2. MIS Organization

Ch. 2 The Organizational Use of Computers
       3 Organizing and Staffing the MIS Function

Part 3. Managing MIS Personnel
       4. Contingency Management and the MIS Function
       5. Achieving Job Productivity and Satisfaction

Part 4. MIS Planning and Control
       6. Strategic Planning for MIS
       7. Management Assessment and Evaluation of MIS


Part 5. Key Technology Trends and Implications
       8. Database Management Systems
       9. Decision Support Systems
      10. Data Communication Systems
      11. Distributed Data Processing
      12. Advanced Office Systems
      13. Robotics and MIS

Part 6. Managing MIS Development
      14. Systems Analysis and Design Strategies and Procedures
      15. Software Development
      16. Implementation

Part 7. Management of Production and Computer Operations
      17. Computer Capacity Planning
      18. Hardware and Software Acquisition
      19. Computer Operations Management


MISM Course at CMU, Pittsburgh


Master of Information Systems Management (MISM) program was developed from the ground up as a blended business-technology program. Through our program, students will develop better planning, management and technical abilities that focus on the application of technology to create business value.

Quantitative management and technology: Our information systems courses offer a unique blend of technology, management, and strategy.


Information Systems Management (MISM) - 16-Month Track
Unlike our competition, the Master of Information Systems Management (MISM) program was developed from the ground-up as a blended business-technology program. Through our program, you'll develop better planning, management and technical abilities necessary for leading a thriving organization in today's complex, digital world.

The MISM degree requires you to demonstrate proficiency in technology management, IT Strategy, and fundamental business skills.

MISM Course Requirements
The required courses are designed to build core competencies in integrating technology management with business expertise with courses ranging from Economic for IT to Object Oriented Programming in Java. As a student in the MISM program, you also have the flexibility to choose from a range of challenging elective courses designed to help you excel in an increasingly digital world.


Important Points from the Chapters


Ch. 1  The MIS Executive

The emergence of business and management information systems (MIS) in organizations has created an intense demand for well-trained, capable information systems managers to plan, organize, direct and control the powerful technology of computer-based information systems.

The topics presented in the book provide a managerial, organizational, behavioral, and technical treatment of MIS management.

MIS executives must blend management, business, technical, and interpersonal relations.

Ch. 2 The Organizational Use of Computers


Ch 4. Contingency Management and the MIS Function

The basic foundation of the contingency theory is that the effectiveness of a management approach is contingent upon the organizational environment in which it is applied. This abandons the concept that there is a "best way" to manage in all environments.

IS function is divided into three important functions: Systems development, production, technical services.

Ch 5. Achieving Job Productivity and Satisfaction

The most highly regarded of motivational theories are as follows:


  • Maslow's need hierarchy  -  A.G. Maslow 1943
  • Reinforcement  - B.F. Skinner  1953
  • Attribution theory - F. Heider 1958
  • Herzberg's dual factory theory - F. Herzberg 1959
  • Expectancy theory - V. Vroom 1964
  • Goal Setting - E.A. Locke 1976
Practical Guidelines

1. Efforts to increase motivation must first focus on the  employee's needs (Maslow, Herzberg)
2. work assignments and goals should be realistic and clearly defined;  rewards for performance should be practical and fulfill the  motivational needs of the employee (Locke, Vroom). 
3. Consequences or outcomes of good performance must approximate the expectations of the employee (Skinner, Heider)

Positive versus Negative Control of Behavior

Behavioral Management Tasks

Communicating
Imitation
Shaping
Reinforcement (Scheduling)

Ch. 6. Strategic Planning for MIS

A four stage model of MIS planning consisting of strategic planning, organization information requirements analysis, resource planning and allocation, and project planning is discussed in this chapter.

The challenges of MIS planning are:

1. Alignment of  the MIS plan with the overall strategies and objectives of the organization.
2. Design of  an information system structure or architecture for the organization.
3. Allocation of information  system development and operations resources among competing applications.
4. Completing information system projects on time and on schedule.

       

Information Systems Management in the Big Data Era - Lake and Drake - 2014 - Book Information

Information technology and management - Hi Mom How To Make Cake, On this share Information technology and management, I have provide any thing about cake

you must see


Information technology and management


https://books.google.co.in/books?id=absoBgAAQBAJ

http://www.springer.com/us/book/9783319135021

Authors:  Lake, Peter, Drake, Robert
Publisher Springer


Table of Contents

1 Introducing Big Data .............................................................................. 1
1.1 What the Reader Will Learn......................................................... 1
1.2 Big Data: So What Is All the Fuss About?................................... 1
1.2.1 Defining “Big Data”...................................................... 2
1.2.2 Big Data: Behind the Hype ........................................... 3
1.2.3 Google and a Case of the Flu........................................ 6
1.3 Big Data: The Backlash Begins ................................................... 6
1.3.1 Big Data Catches a Cold............................................... 6
1.3.2 It’s My Data – So What’s in It for Me?......................... 8
1.3.3 Bucking the Backlash – The Hype Cycle ..................... 9
1.4 A Model for Big Data .................................................................. 11
1.4.1 Strategy ......................................................................... 12
1.4.2 Structure........................................................................ 13
1.4.3 Style .............................................................................. 13
1.4.4 Staff............................................................................... 13
1.4.5 Statistical Thinking ....................................................... 14
1.4.6 Synthesis....................................................................... 14
1.4.7 Systems......................................................................... 15
1.4.8 Sources.......................................................................... 15
1.4.9 Security ......................................................................... 16
1.5 Summary ...................................................................................... 16
1.6 Review Questions......................................................................... 16
1.7 Group Work/Research Activity .................................................... 17
1.7.1 Discussion Topic 1 ........................................................ 17
1.7.2 Discussion Topic 2 ........................................................ 17
References................................................................................................. 17


2 Strategy .................................................................................................... 19
2.1 What the Reader Will Learn......................................................... 19
2.2 Introduction.................................................................................. 19
2.3 What is Strategy? ......................................................................... 20
2.4 Strategy and ‘Big Data’................................................................ 22
2.5 Strategic Analysis......................................................................... 26
2.5.1 Analysing the Business Environment ........................... 26
2.5.2 Strategic Capability – The Value Chain ........................ 32
2.5.3 The SWOT ‘Analysis’................................................... 38
2.6 Strategic Choice ........................................................................... 43
2.6.1 Introduction................................................................... 43
2.6.2 Type, Direction and Criteria
of Strategic Development.............................................. 44
2.6.3 Aligning Business and IT/IS Strategy........................... 47
2.7 Summary ...................................................................................... 51
2.8 Review Questions......................................................................... 51
2.9 Group Work Research Activities.................................................. 51
2.9.1 Discussion Topic 1 ........................................................ 51
2.9.2 Discussion Topic 2 ........................................................ 51
References................................................................................................. 52


3 Structure .................................................................................................. 53
3.1 What the Reader Will Learn......................................................... 53
3.2 Introduction.................................................................................. 53
3.3 What Is ‘Structure’? ..................................................................... 55
3.3.1 What Do We Mean by ‘Structure’?............................... 55
3.4 Formal Structures......................................................................... 56
3.4.1 The “Organisational Chart”: What Does
It Tell Us?...................................................................... 56
3.4.2 Structure, Systems and Processes................................. 61
3.4.3 Formal Structure: What Does This Mean
for Big Data?................................................................. 65
3.4.4 Information Politics ...................................................... 66
3.5 Organisational Culture: The Informal Structure .......................... 69
3.5.1 What Do We Mean by ‘Culture’? ................................. 69
3.5.2 Culture and Leadership................................................. 69
3.5.3 The “Cultural Web”....................................................... 71
3.6 Summary ...................................................................................... 78
3.7 Review Questions......................................................................... 78
3.8 Group Work/Research Activity .................................................... 78
3.8.1 Discussion Topic 1 ........................................................ 78
3.8.2 Discussion Topic 2 ........................................................ 79
References................................................................................................. 79


4 Style .......................................................................................................... 81
4.1 What the Reader Will Learn......................................................... 81
4.2 Introduction.................................................................................. 81
4.3 Management in the Big Data Era................................................. 82
4.3.1 Management or Leadership?......................................... 82
4.3.2 What Is ‘Management’?................................................ 83
4.3.3 Styles of Management................................................... 86
4.3.4 Sources of Managerial Power ....................................... 87
4.4 The Challenges of Big Data (the Four Ds)................................... 90
4.4.1 Data Literacy................................................................. 90
4.4.2 Domain Knowledge ...................................................... 92
4.4.3 Decision-Making........................................................... 93
4.4.4 Data Scientists............................................................... 96
4.4.5 The Leadership Imperative ........................................... 98
4.5 Summary ...................................................................................... 99
4.6 Review Questions......................................................................... 100
4.7 Group Work/Research Activity .................................................... 100
4.7.1 Discussion Topic 1 ........................................................ 100
4.7.2 Discussion Topic 2 ........................................................ 100
References................................................................................................. 100


5 Staff .......................................................................................................... 103
5.1 What the Reader Will Learn......................................................... 103
5.2 Introduction.................................................................................. 103
5.3 Data Scientists: The Myth of the ‘Super Quant’.......................... 104
5.3.1 What’s in a Name? ........................................................ 104
5.3.2 Data “Science” and Data “Scientists”........................... 105
5.3.3 We’ve Been Here Before…........................................... 110
5.4 It Takes a Team…......................................................................... 111
5.4.1 What Do We Mean by “a Team”?................................. 111
5.4.2 Building High-Performance Teams .............................. 113
5.5 Team Building as an Organisational Competency ....................... 117
5.6 Summary ...................................................................................... 121
5.7 Review Questions......................................................................... 121
5.8 Group Work/Research Activity .................................................... 122
5.8.1 Discussion Topic 1 ........................................................ 122
5.8.2 Discussion Topic 2 ........................................................ 122
References................................................................................................. 122


6 Statistical Thinking................................................................................. 125
6.1 What the Reader Will Learn......................................................... 125
6.2 Introduction: Statistics Without Mathematics.............................. 125
6.3 Does “Big Data” Mean “Big Knowledge”? ................................. 126
6.3.1 The DIKW Hierarchy ................................................... 127
6.3.2 The Agent-in-the-World................................................ 128
6.4 Statistical Thinking – Introducing System 1 and System 2 ......... 129
6.4.1 Short Circuiting Rationality.......................................... 132
6.5 Causality, Correlation and Conclusions....................................... 133
6.6 Randomness, Uncertainty and the Search for Meaning............... 135
6.6.1 Sampling, Probability and the Law
of Small Numbers......................................................... 137
6.7 Biases, Heuristics and Their Implications for Judgement............ 138
6.7.1 Non-heuristic Biases..................................................... 142
6.8 Summary ...................................................................................... 144
6.9 Review Questions......................................................................... 144
6.10 Group Work Research Activities.................................................. 145
6.10.1 Discussion Topic 1 – The Linda Problem..................... 145
6.10.2 Discussion Topic 2 – The Birthday Paradox................. 145
References................................................................................................. 146


7 Synthesis................................................................................................... 147
7.1 What the Reader Will Learn......................................................... 147
7.2 From Strategy to Successful Information Systems...................... 147
7.2.1 The Role of the Chief Information Officer (CIO)......... 148
7.2.2 Management of IS Projects........................................... 149
7.3 Creating Requirements That Lead to Successful
Information Systems.................................................................... 151
7.4 Stakeholder Buy-In ...................................................................... 154
7.5 How Do We Measure Success...................................................... 155
7.6 Managing Change ........................................................................ 156
7.7 Cost Benefits and Total Cost of Ownership ................................. 158
7.7.1 Open Source.................................................................. 158
7.7.2 Off the Shelf vs Bespoke .............................................. 159
7.7.3 Gauging Benefits........................................................... 160
7.8 Insourcing or Outsourcing?.......................................................... 161
7.9 The Effect of Cloud...................................................................... 163
7.10 Implementing ‘Big Data’.............................................................. 164
7.11 Summary ...................................................................................... 165
7.12 Review Questions......................................................................... 166
7.13 Group Work Research Activities.................................................. 166
7.13.1 Discussion Topic 1 ........................................................ 166
7.13.2 Discussion Topic 2 ........................................................ 166
References................................................................................................. 166


8 Systems..................................................................................................... 169
8.1 What the Reader Will Learn......................................................... 169
8.2 What Does Big Data Mean for Information Systems?................. 169
8.3 Data Storage and Database Management Systems...................... 170
8.3.1 Database Management Systems.................................... 172
8.3.2 Key-Value Databases .................................................... 173
8.3.3 Online Transactional Processing (OLTP) ..................... 173
8.3.4 Decision Support Systems (DSS) ................................. 175
8.3.5 Column-Based Databases ............................................. 176
8.3.6 In Memory Systems...................................................... 176
8.4 What a DBA Worries About......................................................... 177
8.4.1 Scalability ..................................................................... 177
8.4.2 Performance .................................................................. 178
8.4.3 Availability.................................................................... 179
8.4.4 Data Migration.............................................................. 179
8.4.5 Not All Systems Are Data Intensive ............................. 182
8.4.6 And There Is More to Data than Storage ...................... 183
8.5 Open Source................................................................................. 183
8.6 Application Packages................................................................... 184
8.6.1 Open Source vs Vendor Supplied?................................ 186
8.7 The Cloud and Big Data............................................................... 187
8.8 Hadoop and NoSQL..................................................................... 189
8.9 Summary ...................................................................................... 190
8.10 Review Questions......................................................................... 190
8.11 Group Work Research Activities.................................................. 191
8.11.1 Discussion Topic 1 ........................................................ 191
8.11.2 Discussion Topic 2 ........................................................ 191
References................................................................................................. 191


9 Sources..................................................................................................... 193
9.1 What the Reader Will Learn......................................................... 193
9.2 Data Sources for Data – Both Big and Small............................... 193
9.3 The Four Vs – Understanding What Makes Data Big Data ......... 194
9.4 Categories of Data........................................................................ 196
9.4.1 Classification by Purpose.............................................. 196
9.4.2 Data Type Classification and Serialisation
Alternatives................................................................... 198
9.5 Data Quality ................................................................................. 202
9.5.1 Extract, Transform and Load (ETL) ............................. 205
9.6 Meta Data..................................................................................... 206
9.6.1 Internet of Things (IoT) ................................................ 206
9.7 Data Ownership............................................................................ 209
9.8 Crowdsourcing ............................................................................. 210
9.9 Summary ...................................................................................... 212
9.10 Review Questions......................................................................... 212
9.11 Group Work Research Activities.................................................. 212
9.11.1 Discussion Topic 1 ........................................................ 213
9.11.2 Discussion Topic 2 ........................................................ 213
References................................................................................................. 213


10 IS Security................................................................................................ 215
10.1 What the Reader Will Learn......................................................... 215
10.2 What This Chapter Could Contain but Doesn’t ........................... 215
10.3 Understanding the Risks .............................................................. 216
10.3.1 What Is the Scale of the Problem?................................ 216
10.4 Privacy, Ethics and Governance ................................................... 218
10.4.1 The Ethical Dimension of Security............................... 218
10.4.2 Data Protection.............................................................. 221
10.5 Securing Systems......................................................................... 224
10.5.1 Hacking......................................................................... 224
10.5.2 Denial of Service........................................................... 225
10.5.3 Denial of Service Defence Mechanisms....................... 226
10.5.4 Viruses and Worms and Trojan Horses
(Often Collectively Referred to as Malware)................ 227
10.5.5 Spyware......................................................................... 228
10.5.6 Defences Against Malicious Attacks ............................ 228
10.6 Securing Data............................................................................... 230
10.6.1 Application Access Control .......................................... 233
10.6.2 Physical Security........................................................... 234
10.6.3 Malicious Insiders and Careless Employees................. 235
10.7 Does Big Data Make for More Vulnerability? ............................. 235
10.8 Summary ...................................................................................... 235
10.9 Review Questions......................................................................... 236
10.10 Group Work Research Activities.................................................. 236
10.10.1 Discussion Topic 1 ........................................................ 236
10.10.2 Discussion Topic 2 ........................................................ 236
References................................................................................................. 237


11 Technical Insights.................................................................................... 239
11.1 What the Reader Will Learn......................................................... 239
11.2 What You Will Need for This Chapter ......................................... 239
11.3 Hands-on with Hadoop ................................................................ 240
11.3.1 The Sandbox ................................................................. 240
11.3.2 Hive............................................................................... 248
11.3.3 Pig ................................................................................. 251
11.3.4 Sharing Your Data with the Outside World................... 256
11.3.5 Visualization ................................................................. 258
11.3.6 Life Is Usually More Complicated!.............................. 261
11.4 Hadoop Is Not the Only NoSQL Game in Town!........................ 262
11.5 Summary ...................................................................................... 263
11.6 Review Questions......................................................................... 263
11.7 Extending the Tutorial Activities.................................................. 263
11.7.1 Extra Question 1 ........................................................... 263
11.7.2 Extra Question 2 ........................................................... 264
11.7.3 Extra Question 3 ........................................................... 264
11.8 Hints for the Extra Questions....................................................... 264
11.9 Answer for Extra Questions......................................................... 264
11.9.1 Question 1 ..................................................................... 264
11.9.2 Question 2 ..................................................................... 265
11.9.3 Question 3 ..................................................................... 265
Reference .................................................................................................. 266

12 The Future of IS in the Era of Big Data................................................ 267
12.1 What the Reader Will Learn......................................................... 267
12.2 The Difficulty of Future Gazing with IT...................................... 267
12.3 The Doubts................................................................................... 268
12.4 The Future of Information Systems (IS)...................................... 269
12.4.1 Making Decisions About Technology........................... 271
12.5 So What Will Happen in the Future? ........................................... 275
12.5.1 The Future for Big Data................................................ 275
12.5.2 Ethics of Big Data......................................................... 279
12.5.3 Big Data and Business Intelligence .............................. 280
12.5.4 The Future for Data Scientists ...................................... 281
12.5.5 The Future for IS Management..................................... 282
12.5.6 Keeping Your Eye on the Game .................................... 285
12.6 Summary ...................................................................................... 286
12.7 Review Questions......................................................................... 286
12.8 Group Work Research Activities.................................................. 287
12.8.1 Discussion Topic 1 ........................................................ 287
12.8.2 Discussion Topic 2 ........................................................ 287
References................................................................................................. 287
Index................................................................................................................. 289


http://www.springer.com/us/book/9783319135021



Big Data System Development
2015 Presentation by Chen et al.
https://sse.uni-due.de/bigdse15/BIGDSE2015-Chen_et_al.pdf


Addressing the Software Engineering Challenges of Big Data
POSTED ON OCTOBER 21, 2013 BY IAN GORTON IN ARCHITECTURE
https://insights.sei.cmu.edu/sei_blog/2013/10/addressing-the-software-engineering-challenges-of-big-data.html

Article - McKinsey Quarterly October 2011
Are you ready for the era of ‘big data’?
By Brad Brown, Michael Chui, and James Manyika
http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-ready-for-the-era-of-big-data





https://books.google.co.in/books?id=absoBgAAQBAJ

http://www.springer.com/us/book/9783319135021

Authors:  Lake, Peter, Drake, Robert
Publisher Springer


Table of Contents

1 Introducing Big Data .............................................................................. 1
1.1 What the Reader Will Learn......................................................... 1
1.2 Big Data: So What Is All the Fuss About?................................... 1
1.2.1 Defining “Big Data”...................................................... 2
1.2.2 Big Data: Behind the Hype ........................................... 3
1.2.3 Google and a Case of the Flu........................................ 6
1.3 Big Data: The Backlash Begins ................................................... 6
1.3.1 Big Data Catches a Cold............................................... 6
1.3.2 It’s My Data – So What’s in It for Me?......................... 8
1.3.3 Bucking the Backlash – The Hype Cycle ..................... 9
1.4 A Model for Big Data .................................................................. 11
1.4.1 Strategy ......................................................................... 12
1.4.2 Structure........................................................................ 13
1.4.3 Style .............................................................................. 13
1.4.4 Staff............................................................................... 13
1.4.5 Statistical Thinking ....................................................... 14
1.4.6 Synthesis....................................................................... 14
1.4.7 Systems......................................................................... 15
1.4.8 Sources.......................................................................... 15
1.4.9 Security ......................................................................... 16
1.5 Summary ...................................................................................... 16
1.6 Review Questions......................................................................... 16
1.7 Group Work/Research Activity .................................................... 17
1.7.1 Discussion Topic 1 ........................................................ 17
1.7.2 Discussion Topic 2 ........................................................ 17
References................................................................................................. 17


2 Strategy .................................................................................................... 19
2.1 What the Reader Will Learn......................................................... 19
2.2 Introduction.................................................................................. 19
2.3 What is Strategy? ......................................................................... 20
2.4 Strategy and ‘Big Data’................................................................ 22
2.5 Strategic Analysis......................................................................... 26
2.5.1 Analysing the Business Environment ........................... 26
2.5.2 Strategic Capability – The Value Chain ........................ 32
2.5.3 The SWOT ‘Analysis’................................................... 38
2.6 Strategic Choice ........................................................................... 43
2.6.1 Introduction................................................................... 43
2.6.2 Type, Direction and Criteria
of Strategic Development.............................................. 44
2.6.3 Aligning Business and IT/IS Strategy........................... 47
2.7 Summary ...................................................................................... 51
2.8 Review Questions......................................................................... 51
2.9 Group Work Research Activities.................................................. 51
2.9.1 Discussion Topic 1 ........................................................ 51
2.9.2 Discussion Topic 2 ........................................................ 51
References................................................................................................. 52


3 Structure .................................................................................................. 53
3.1 What the Reader Will Learn......................................................... 53
3.2 Introduction.................................................................................. 53
3.3 What Is ‘Structure’? ..................................................................... 55
3.3.1 What Do We Mean by ‘Structure’?............................... 55
3.4 Formal Structures......................................................................... 56
3.4.1 The “Organisational Chart”: What Does
It Tell Us?...................................................................... 56
3.4.2 Structure, Systems and Processes................................. 61
3.4.3 Formal Structure: What Does This Mean
for Big Data?................................................................. 65
3.4.4 Information Politics ...................................................... 66
3.5 Organisational Culture: The Informal Structure .......................... 69
3.5.1 What Do We Mean by ‘Culture’? ................................. 69
3.5.2 Culture and Leadership................................................. 69
3.5.3 The “Cultural Web”....................................................... 71
3.6 Summary ...................................................................................... 78
3.7 Review Questions......................................................................... 78
3.8 Group Work/Research Activity .................................................... 78
3.8.1 Discussion Topic 1 ........................................................ 78
3.8.2 Discussion Topic 2 ........................................................ 79
References................................................................................................. 79


4 Style .......................................................................................................... 81
4.1 What the Reader Will Learn......................................................... 81
4.2 Introduction.................................................................................. 81
4.3 Management in the Big Data Era................................................. 82
4.3.1 Management or Leadership?......................................... 82
4.3.2 What Is ‘Management’?................................................ 83
4.3.3 Styles of Management................................................... 86
4.3.4 Sources of Managerial Power ....................................... 87
4.4 The Challenges of Big Data (the Four Ds)................................... 90
4.4.1 Data Literacy................................................................. 90
4.4.2 Domain Knowledge ...................................................... 92
4.4.3 Decision-Making........................................................... 93
4.4.4 Data Scientists............................................................... 96
4.4.5 The Leadership Imperative ........................................... 98
4.5 Summary ...................................................................................... 99
4.6 Review Questions......................................................................... 100
4.7 Group Work/Research Activity .................................................... 100
4.7.1 Discussion Topic 1 ........................................................ 100
4.7.2 Discussion Topic 2 ........................................................ 100
References................................................................................................. 100


5 Staff .......................................................................................................... 103
5.1 What the Reader Will Learn......................................................... 103
5.2 Introduction.................................................................................. 103
5.3 Data Scientists: The Myth of the ‘Super Quant’.......................... 104
5.3.1 What’s in a Name? ........................................................ 104
5.3.2 Data “Science” and Data “Scientists”........................... 105
5.3.3 We’ve Been Here Before…........................................... 110
5.4 It Takes a Team…......................................................................... 111
5.4.1 What Do We Mean by “a Team”?................................. 111
5.4.2 Building High-Performance Teams .............................. 113
5.5 Team Building as an Organisational Competency ....................... 117
5.6 Summary ...................................................................................... 121
5.7 Review Questions......................................................................... 121
5.8 Group Work/Research Activity .................................................... 122
5.8.1 Discussion Topic 1 ........................................................ 122
5.8.2 Discussion Topic 2 ........................................................ 122
References................................................................................................. 122


6 Statistical Thinking................................................................................. 125
6.1 What the Reader Will Learn......................................................... 125
6.2 Introduction: Statistics Without Mathematics.............................. 125
6.3 Does “Big Data” Mean “Big Knowledge”? ................................. 126
6.3.1 The DIKW Hierarchy ................................................... 127
6.3.2 The Agent-in-the-World................................................ 128
6.4 Statistical Thinking – Introducing System 1 and System 2 ......... 129
6.4.1 Short Circuiting Rationality.......................................... 132
6.5 Causality, Correlation and Conclusions....................................... 133
6.6 Randomness, Uncertainty and the Search for Meaning............... 135
6.6.1 Sampling, Probability and the Law
of Small Numbers......................................................... 137
6.7 Biases, Heuristics and Their Implications for Judgement............ 138
6.7.1 Non-heuristic Biases..................................................... 142
6.8 Summary ...................................................................................... 144
6.9 Review Questions......................................................................... 144
6.10 Group Work Research Activities.................................................. 145
6.10.1 Discussion Topic 1 – The Linda Problem..................... 145
6.10.2 Discussion Topic 2 – The Birthday Paradox................. 145
References................................................................................................. 146


7 Synthesis................................................................................................... 147
7.1 What the Reader Will Learn......................................................... 147
7.2 From Strategy to Successful Information Systems...................... 147
7.2.1 The Role of the Chief Information Officer (CIO)......... 148
7.2.2 Management of IS Projects........................................... 149
7.3 Creating Requirements That Lead to Successful
Information Systems.................................................................... 151
7.4 Stakeholder Buy-In ...................................................................... 154
7.5 How Do We Measure Success...................................................... 155
7.6 Managing Change ........................................................................ 156
7.7 Cost Benefits and Total Cost of Ownership ................................. 158
7.7.1 Open Source.................................................................. 158
7.7.2 Off the Shelf vs Bespoke .............................................. 159
7.7.3 Gauging Benefits........................................................... 160
7.8 Insourcing or Outsourcing?.......................................................... 161
7.9 The Effect of Cloud...................................................................... 163
7.10 Implementing ‘Big Data’.............................................................. 164
7.11 Summary ...................................................................................... 165
7.12 Review Questions......................................................................... 166
7.13 Group Work Research Activities.................................................. 166
7.13.1 Discussion Topic 1 ........................................................ 166
7.13.2 Discussion Topic 2 ........................................................ 166
References................................................................................................. 166


8 Systems..................................................................................................... 169
8.1 What the Reader Will Learn......................................................... 169
8.2 What Does Big Data Mean for Information Systems?................. 169
8.3 Data Storage and Database Management Systems...................... 170
8.3.1 Database Management Systems.................................... 172
8.3.2 Key-Value Databases .................................................... 173
8.3.3 Online Transactional Processing (OLTP) ..................... 173
8.3.4 Decision Support Systems (DSS) ................................. 175
8.3.5 Column-Based Databases ............................................. 176
8.3.6 In Memory Systems...................................................... 176
8.4 What a DBA Worries About......................................................... 177
8.4.1 Scalability ..................................................................... 177
8.4.2 Performance .................................................................. 178
8.4.3 Availability.................................................................... 179
8.4.4 Data Migration.............................................................. 179
8.4.5 Not All Systems Are Data Intensive ............................. 182
8.4.6 And There Is More to Data than Storage ...................... 183
8.5 Open Source................................................................................. 183
8.6 Application Packages................................................................... 184
8.6.1 Open Source vs Vendor Supplied?................................ 186
8.7 The Cloud and Big Data............................................................... 187
8.8 Hadoop and NoSQL..................................................................... 189
8.9 Summary ...................................................................................... 190
8.10 Review Questions......................................................................... 190
8.11 Group Work Research Activities.................................................. 191
8.11.1 Discussion Topic 1 ........................................................ 191
8.11.2 Discussion Topic 2 ........................................................ 191
References................................................................................................. 191


9 Sources..................................................................................................... 193
9.1 What the Reader Will Learn......................................................... 193
9.2 Data Sources for Data – Both Big and Small............................... 193
9.3 The Four Vs – Understanding What Makes Data Big Data ......... 194
9.4 Categories of Data........................................................................ 196
9.4.1 Classification by Purpose.............................................. 196
9.4.2 Data Type Classification and Serialisation
Alternatives................................................................... 198
9.5 Data Quality ................................................................................. 202
9.5.1 Extract, Transform and Load (ETL) ............................. 205
9.6 Meta Data..................................................................................... 206
9.6.1 Internet of Things (IoT) ................................................ 206
9.7 Data Ownership............................................................................ 209
9.8 Crowdsourcing ............................................................................. 210
9.9 Summary ...................................................................................... 212
9.10 Review Questions......................................................................... 212
9.11 Group Work Research Activities.................................................. 212
9.11.1 Discussion Topic 1 ........................................................ 213
9.11.2 Discussion Topic 2 ........................................................ 213
References................................................................................................. 213


10 IS Security................................................................................................ 215
10.1 What the Reader Will Learn......................................................... 215
10.2 What This Chapter Could Contain but Doesn’t ........................... 215
10.3 Understanding the Risks .............................................................. 216
10.3.1 What Is the Scale of the Problem?................................ 216
10.4 Privacy, Ethics and Governance ................................................... 218
10.4.1 The Ethical Dimension of Security............................... 218
10.4.2 Data Protection.............................................................. 221
10.5 Securing Systems......................................................................... 224
10.5.1 Hacking......................................................................... 224
10.5.2 Denial of Service........................................................... 225
10.5.3 Denial of Service Defence Mechanisms....................... 226
10.5.4 Viruses and Worms and Trojan Horses
(Often Collectively Referred to as Malware)................ 227
10.5.5 Spyware......................................................................... 228
10.5.6 Defences Against Malicious Attacks ............................ 228
10.6 Securing Data............................................................................... 230
10.6.1 Application Access Control .......................................... 233
10.6.2 Physical Security........................................................... 234
10.6.3 Malicious Insiders and Careless Employees................. 235
10.7 Does Big Data Make for More Vulnerability? ............................. 235
10.8 Summary ...................................................................................... 235
10.9 Review Questions......................................................................... 236
10.10 Group Work Research Activities.................................................. 236
10.10.1 Discussion Topic 1 ........................................................ 236
10.10.2 Discussion Topic 2 ........................................................ 236
References................................................................................................. 237


11 Technical Insights.................................................................................... 239
11.1 What the Reader Will Learn......................................................... 239
11.2 What You Will Need for This Chapter ......................................... 239
11.3 Hands-on with Hadoop ................................................................ 240
11.3.1 The Sandbox ................................................................. 240
11.3.2 Hive............................................................................... 248
11.3.3 Pig ................................................................................. 251
11.3.4 Sharing Your Data with the Outside World................... 256
11.3.5 Visualization ................................................................. 258
11.3.6 Life Is Usually More Complicated!.............................. 261
11.4 Hadoop Is Not the Only NoSQL Game in Town!........................ 262
11.5 Summary ...................................................................................... 263
11.6 Review Questions......................................................................... 263
11.7 Extending the Tutorial Activities.................................................. 263
11.7.1 Extra Question 1 ........................................................... 263
11.7.2 Extra Question 2 ........................................................... 264
11.7.3 Extra Question 3 ........................................................... 264
11.8 Hints for the Extra Questions....................................................... 264
11.9 Answer for Extra Questions......................................................... 264
11.9.1 Question 1 ..................................................................... 264
11.9.2 Question 2 ..................................................................... 265
11.9.3 Question 3 ..................................................................... 265
Reference .................................................................................................. 266

12 The Future of IS in the Era of Big Data................................................ 267
12.1 What the Reader Will Learn......................................................... 267
12.2 The Difficulty of Future Gazing with IT...................................... 267
12.3 The Doubts................................................................................... 268
12.4 The Future of Information Systems (IS)...................................... 269
12.4.1 Making Decisions About Technology........................... 271
12.5 So What Will Happen in the Future? ........................................... 275
12.5.1 The Future for Big Data................................................ 275
12.5.2 Ethics of Big Data......................................................... 279
12.5.3 Big Data and Business Intelligence .............................. 280
12.5.4 The Future for Data Scientists ...................................... 281
12.5.5 The Future for IS Management..................................... 282
12.5.6 Keeping Your Eye on the Game .................................... 285
12.6 Summary ...................................................................................... 286
12.7 Review Questions......................................................................... 286
12.8 Group Work Research Activities.................................................. 287
12.8.1 Discussion Topic 1 ........................................................ 287
12.8.2 Discussion Topic 2 ........................................................ 287
References................................................................................................. 287
Index................................................................................................................. 289


http://www.springer.com/us/book/9783319135021



Big Data System Development
2015 Presentation by Chen et al.
https://sse.uni-due.de/bigdse15/BIGDSE2015-Chen_et_al.pdf


Addressing the Software Engineering Challenges of Big Data
POSTED ON OCTOBER 21, 2013 BY IAN GORTON IN ARCHITECTURE
https://insights.sei.cmu.edu/sei_blog/2013/10/addressing-the-software-engineering-challenges-of-big-data.html

Article - McKinsey Quarterly October 2011
Are you ready for the era of ‘big data’?
By Brad Brown, Michael Chui, and James Manyika
http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-ready-for-the-era-of-big-data





https://books.google.co.in/books?id=absoBgAAQBAJ

http://www.springer.com/us/book/9783319135021

Authors:  Lake, Peter, Drake, Robert
Publisher Springer


Table of Contents

1 Introducing Big Data .............................................................................. 1
1.1 What the Reader Will Learn......................................................... 1
1.2 Big Data: So What Is All the Fuss About?................................... 1
1.2.1 Defining “Big Data”...................................................... 2
1.2.2 Big Data: Behind the Hype ........................................... 3
1.2.3 Google and a Case of the Flu........................................ 6
1.3 Big Data: The Backlash Begins ................................................... 6
1.3.1 Big Data Catches a Cold............................................... 6
1.3.2 It’s My Data – So What’s in It for Me?......................... 8
1.3.3 Bucking the Backlash – The Hype Cycle ..................... 9
1.4 A Model for Big Data .................................................................. 11
1.4.1 Strategy ......................................................................... 12
1.4.2 Structure........................................................................ 13
1.4.3 Style .............................................................................. 13
1.4.4 Staff............................................................................... 13
1.4.5 Statistical Thinking ....................................................... 14
1.4.6 Synthesis....................................................................... 14
1.4.7 Systems......................................................................... 15
1.4.8 Sources.......................................................................... 15
1.4.9 Security ......................................................................... 16
1.5 Summary ...................................................................................... 16
1.6 Review Questions......................................................................... 16
1.7 Group Work/Research Activity .................................................... 17
1.7.1 Discussion Topic 1 ........................................................ 17
1.7.2 Discussion Topic 2 ........................................................ 17
References................................................................................................. 17


2 Strategy .................................................................................................... 19
2.1 What the Reader Will Learn......................................................... 19
2.2 Introduction.................................................................................. 19
2.3 What is Strategy? ......................................................................... 20
2.4 Strategy and ‘Big Data’................................................................ 22
2.5 Strategic Analysis......................................................................... 26
2.5.1 Analysing the Business Environment ........................... 26
2.5.2 Strategic Capability – The Value Chain ........................ 32
2.5.3 The SWOT ‘Analysis’................................................... 38
2.6 Strategic Choice ........................................................................... 43
2.6.1 Introduction................................................................... 43
2.6.2 Type, Direction and Criteria
of Strategic Development.............................................. 44
2.6.3 Aligning Business and IT/IS Strategy........................... 47
2.7 Summary ...................................................................................... 51
2.8 Review Questions......................................................................... 51
2.9 Group Work Research Activities.................................................. 51
2.9.1 Discussion Topic 1 ........................................................ 51
2.9.2 Discussion Topic 2 ........................................................ 51
References................................................................................................. 52


3 Structure .................................................................................................. 53
3.1 What the Reader Will Learn......................................................... 53
3.2 Introduction.................................................................................. 53
3.3 What Is ‘Structure’? ..................................................................... 55
3.3.1 What Do We Mean by ‘Structure’?............................... 55
3.4 Formal Structures......................................................................... 56
3.4.1 The “Organisational Chart”: What Does
It Tell Us?...................................................................... 56
3.4.2 Structure, Systems and Processes................................. 61
3.4.3 Formal Structure: What Does This Mean
for Big Data?................................................................. 65
3.4.4 Information Politics ...................................................... 66
3.5 Organisational Culture: The Informal Structure .......................... 69
3.5.1 What Do We Mean by ‘Culture’? ................................. 69
3.5.2 Culture and Leadership................................................. 69
3.5.3 The “Cultural Web”....................................................... 71
3.6 Summary ...................................................................................... 78
3.7 Review Questions......................................................................... 78
3.8 Group Work/Research Activity .................................................... 78
3.8.1 Discussion Topic 1 ........................................................ 78
3.8.2 Discussion Topic 2 ........................................................ 79
References................................................................................................. 79


4 Style .......................................................................................................... 81
4.1 What the Reader Will Learn......................................................... 81
4.2 Introduction.................................................................................. 81
4.3 Management in the Big Data Era................................................. 82
4.3.1 Management or Leadership?......................................... 82
4.3.2 What Is ‘Management’?................................................ 83
4.3.3 Styles of Management................................................... 86
4.3.4 Sources of Managerial Power ....................................... 87
4.4 The Challenges of Big Data (the Four Ds)................................... 90
4.4.1 Data Literacy................................................................. 90
4.4.2 Domain Knowledge ...................................................... 92
4.4.3 Decision-Making........................................................... 93
4.4.4 Data Scientists............................................................... 96
4.4.5 The Leadership Imperative ........................................... 98
4.5 Summary ...................................................................................... 99
4.6 Review Questions......................................................................... 100
4.7 Group Work/Research Activity .................................................... 100
4.7.1 Discussion Topic 1 ........................................................ 100
4.7.2 Discussion Topic 2 ........................................................ 100
References................................................................................................. 100


5 Staff .......................................................................................................... 103
5.1 What the Reader Will Learn......................................................... 103
5.2 Introduction.................................................................................. 103
5.3 Data Scientists: The Myth of the ‘Super Quant’.......................... 104
5.3.1 What’s in a Name? ........................................................ 104
5.3.2 Data “Science” and Data “Scientists”........................... 105
5.3.3 We’ve Been Here Before…........................................... 110
5.4 It Takes a Team…......................................................................... 111
5.4.1 What Do We Mean by “a Team”?................................. 111
5.4.2 Building High-Performance Teams .............................. 113
5.5 Team Building as an Organisational Competency ....................... 117
5.6 Summary ...................................................................................... 121
5.7 Review Questions......................................................................... 121
5.8 Group Work/Research Activity .................................................... 122
5.8.1 Discussion Topic 1 ........................................................ 122
5.8.2 Discussion Topic 2 ........................................................ 122
References................................................................................................. 122


6 Statistical Thinking................................................................................. 125
6.1 What the Reader Will Learn......................................................... 125
6.2 Introduction: Statistics Without Mathematics.............................. 125
6.3 Does “Big Data” Mean “Big Knowledge”? ................................. 126
6.3.1 The DIKW Hierarchy ................................................... 127
6.3.2 The Agent-in-the-World................................................ 128
6.4 Statistical Thinking – Introducing System 1 and System 2 ......... 129
6.4.1 Short Circuiting Rationality.......................................... 132
6.5 Causality, Correlation and Conclusions....................................... 133
6.6 Randomness, Uncertainty and the Search for Meaning............... 135
6.6.1 Sampling, Probability and the Law
of Small Numbers......................................................... 137
6.7 Biases, Heuristics and Their Implications for Judgement............ 138
6.7.1 Non-heuristic Biases..................................................... 142
6.8 Summary ...................................................................................... 144
6.9 Review Questions......................................................................... 144
6.10 Group Work Research Activities.................................................. 145
6.10.1 Discussion Topic 1 – The Linda Problem..................... 145
6.10.2 Discussion Topic 2 – The Birthday Paradox................. 145
References................................................................................................. 146


7 Synthesis................................................................................................... 147
7.1 What the Reader Will Learn......................................................... 147
7.2 From Strategy to Successful Information Systems...................... 147
7.2.1 The Role of the Chief Information Officer (CIO)......... 148
7.2.2 Management of IS Projects........................................... 149
7.3 Creating Requirements That Lead to Successful
Information Systems.................................................................... 151
7.4 Stakeholder Buy-In ...................................................................... 154
7.5 How Do We Measure Success...................................................... 155
7.6 Managing Change ........................................................................ 156
7.7 Cost Benefits and Total Cost of Ownership ................................. 158
7.7.1 Open Source.................................................................. 158
7.7.2 Off the Shelf vs Bespoke .............................................. 159
7.7.3 Gauging Benefits........................................................... 160
7.8 Insourcing or Outsourcing?.......................................................... 161
7.9 The Effect of Cloud...................................................................... 163
7.10 Implementing ‘Big Data’.............................................................. 164
7.11 Summary ...................................................................................... 165
7.12 Review Questions......................................................................... 166
7.13 Group Work Research Activities.................................................. 166
7.13.1 Discussion Topic 1 ........................................................ 166
7.13.2 Discussion Topic 2 ........................................................ 166
References................................................................................................. 166


8 Systems..................................................................................................... 169
8.1 What the Reader Will Learn......................................................... 169
8.2 What Does Big Data Mean for Information Systems?................. 169
8.3 Data Storage and Database Management Systems...................... 170
8.3.1 Database Management Systems.................................... 172
8.3.2 Key-Value Databases .................................................... 173
8.3.3 Online Transactional Processing (OLTP) ..................... 173
8.3.4 Decision Support Systems (DSS) ................................. 175
8.3.5 Column-Based Databases ............................................. 176
8.3.6 In Memory Systems...................................................... 176
8.4 What a DBA Worries About......................................................... 177
8.4.1 Scalability ..................................................................... 177
8.4.2 Performance .................................................................. 178
8.4.3 Availability.................................................................... 179
8.4.4 Data Migration.............................................................. 179
8.4.5 Not All Systems Are Data Intensive ............................. 182
8.4.6 And There Is More to Data than Storage ...................... 183
8.5 Open Source................................................................................. 183
8.6 Application Packages................................................................... 184
8.6.1 Open Source vs Vendor Supplied?................................ 186
8.7 The Cloud and Big Data............................................................... 187
8.8 Hadoop and NoSQL..................................................................... 189
8.9 Summary ...................................................................................... 190
8.10 Review Questions......................................................................... 190
8.11 Group Work Research Activities.................................................. 191
8.11.1 Discussion Topic 1 ........................................................ 191
8.11.2 Discussion Topic 2 ........................................................ 191
References................................................................................................. 191


9 Sources..................................................................................................... 193
9.1 What the Reader Will Learn......................................................... 193
9.2 Data Sources for Data – Both Big and Small............................... 193
9.3 The Four Vs – Understanding What Makes Data Big Data ......... 194
9.4 Categories of Data........................................................................ 196
9.4.1 Classification by Purpose.............................................. 196
9.4.2 Data Type Classification and Serialisation
Alternatives................................................................... 198
9.5 Data Quality ................................................................................. 202
9.5.1 Extract, Transform and Load (ETL) ............................. 205
9.6 Meta Data..................................................................................... 206
9.6.1 Internet of Things (IoT) ................................................ 206
9.7 Data Ownership............................................................................ 209
9.8 Crowdsourcing ............................................................................. 210
9.9 Summary ...................................................................................... 212
9.10 Review Questions......................................................................... 212
9.11 Group Work Research Activities.................................................. 212
9.11.1 Discussion Topic 1 ........................................................ 213
9.11.2 Discussion Topic 2 ........................................................ 213
References................................................................................................. 213


10 IS Security................................................................................................ 215
10.1 What the Reader Will Learn......................................................... 215
10.2 What This Chapter Could Contain but Doesn’t ........................... 215
10.3 Understanding the Risks .............................................................. 216
10.3.1 What Is the Scale of the Problem?................................ 216
10.4 Privacy, Ethics and Governance ................................................... 218
10.4.1 The Ethical Dimension of Security............................... 218
10.4.2 Data Protection.............................................................. 221
10.5 Securing Systems......................................................................... 224
10.5.1 Hacking......................................................................... 224
10.5.2 Denial of Service........................................................... 225
10.5.3 Denial of Service Defence Mechanisms....................... 226
10.5.4 Viruses and Worms and Trojan Horses
(Often Collectively Referred to as Malware)................ 227
10.5.5 Spyware......................................................................... 228
10.5.6 Defences Against Malicious Attacks ............................ 228
10.6 Securing Data............................................................................... 230
10.6.1 Application Access Control .......................................... 233
10.6.2 Physical Security........................................................... 234
10.6.3 Malicious Insiders and Careless Employees................. 235
10.7 Does Big Data Make for More Vulnerability? ............................. 235
10.8 Summary ...................................................................................... 235
10.9 Review Questions......................................................................... 236
10.10 Group Work Research Activities.................................................. 236
10.10.1 Discussion Topic 1 ........................................................ 236
10.10.2 Discussion Topic 2 ........................................................ 236
References................................................................................................. 237


11 Technical Insights.................................................................................... 239
11.1 What the Reader Will Learn......................................................... 239
11.2 What You Will Need for This Chapter ......................................... 239
11.3 Hands-on with Hadoop ................................................................ 240
11.3.1 The Sandbox ................................................................. 240
11.3.2 Hive............................................................................... 248
11.3.3 Pig ................................................................................. 251
11.3.4 Sharing Your Data with the Outside World................... 256
11.3.5 Visualization ................................................................. 258
11.3.6 Life Is Usually More Complicated!.............................. 261
11.4 Hadoop Is Not the Only NoSQL Game in Town!........................ 262
11.5 Summary ...................................................................................... 263
11.6 Review Questions......................................................................... 263
11.7 Extending the Tutorial Activities.................................................. 263
11.7.1 Extra Question 1 ........................................................... 263
11.7.2 Extra Question 2 ........................................................... 264
11.7.3 Extra Question 3 ........................................................... 264
11.8 Hints for the Extra Questions....................................................... 264
11.9 Answer for Extra Questions......................................................... 264
11.9.1 Question 1 ..................................................................... 264
11.9.2 Question 2 ..................................................................... 265
11.9.3 Question 3 ..................................................................... 265
Reference .................................................................................................. 266

12 The Future of IS in the Era of Big Data................................................ 267
12.1 What the Reader Will Learn......................................................... 267
12.2 The Difficulty of Future Gazing with IT...................................... 267
12.3 The Doubts................................................................................... 268
12.4 The Future of Information Systems (IS)...................................... 269
12.4.1 Making Decisions About Technology........................... 271
12.5 So What Will Happen in the Future? ........................................... 275
12.5.1 The Future for Big Data................................................ 275
12.5.2 Ethics of Big Data......................................................... 279
12.5.3 Big Data and Business Intelligence .............................. 280
12.5.4 The Future for Data Scientists ...................................... 281
12.5.5 The Future for IS Management..................................... 282
12.5.6 Keeping Your Eye on the Game .................................... 285
12.6 Summary ...................................................................................... 286
12.7 Review Questions......................................................................... 286
12.8 Group Work Research Activities.................................................. 287
12.8.1 Discussion Topic 1 ........................................................ 287
12.8.2 Discussion Topic 2 ........................................................ 287
References................................................................................................. 287
Index................................................................................................................. 289


http://www.springer.com/us/book/9783319135021



Big Data System Development
2015 Presentation by Chen et al.
https://sse.uni-due.de/bigdse15/BIGDSE2015-Chen_et_al.pdf


Addressing the Software Engineering Challenges of Big Data
POSTED ON OCTOBER 21, 2013 BY IAN GORTON IN ARCHITECTURE
https://insights.sei.cmu.edu/sei_blog/2013/10/addressing-the-software-engineering-challenges-of-big-data.html

Article - McKinsey Quarterly October 2011
Are you ready for the era of ‘big data’?
By Brad Brown, Michael Chui, and James Manyika
http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-ready-for-the-era-of-big-data




 

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