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Emerging Tech Helps Progressive Companies Deliver Exceptional CX

Oracle Security Team - Fri, 2018-05-18 19:18

It’s no secret that the art of delivering exceptional service to customers—whether they’re consumers or business buyers—is undergoing dramatic change. Customers routinely expect highly personalized experiences across all touchpoints, from marketing and sales to service and support. I call each of these engagements a moment of truth—because leaving customers feeling satisfied and valued at each touchpoint will have a direct bearing on their loyalty and future spending decisions.

This is why customer experience (CX) has become a strategic business imperative for modern companies. Organizations that provide effective, well-integrated CX across the entire customer journey achieved compound annual growth rates of 17%, versus the 3% growth rates logged by their peers who provided less-effective customer experiences, according to Forrester’s 2017 “Customer Experience Index.”

Fortunately, it’s becoming easier to enter the CX winner’s circle. AI, machine learning, IoT, behavioral analytics, and other innovations are helping progressive companies capitalize on internal and third-party data to deliver highly personalized communications, promotional offers, and service engagements.

How can companies fully leverage today’s tools to support exceptional CX? If they haven’t already done so, companies should start evolving away from cloud 1.0 infrastructures, where an amalgam of best-of-breed services runs various business units. These standalone cloud platforms might have initially provided quick on-ramps to modern capabilities, but now, many companies are paying a price for that expediency. Siloed data and workflows hinder the smooth sharing of customer information among departments. This hurts CX when a consumer who just purchased a high-end digital camera at a retail outlet, for example, webchats with that same company’s service department about a problem, and the service team has no idea this is a premium customer.

In contrast, cloud 2.0 is focused on achieving a holistic view of customers—thanks to simplified, well-integrated services that support each phase of the customer journey. Eliminating information silos benefits companies by giving employees all the information they need to provide a tailored experience for every customer.

Achieving modern CX requires the right vendor partnerships. That starts with evaluating cloud services according to how complete, integrated, and extensible the CX platform is for supporting the entire customer journey. One option is the Oracle Customer Experience Cloud (Oracle CX Cloud) suite, an integrated set of applications for the entire customer lifecycle. It’s complemented by native AI capabilities and Oracle Data Cloud, the world’s largest third-party data marketplace of consumer and business information, which manages anonymized information from more than a billion business and 5 billion consumer identifiers. This means that business leaders, besides understanding customers based on their direct interactions, can use Oracle Data Cloud for insights into social, web surfing, and buying habits at third-party sites and retailers and then apply AI to find profitable synergies.

As new disruptive technologies come to the market—whether that’s the mainstreaming of IoT or drones for business—companies will be under constant pressure to integrate these new capabilities to improve their CX strategies. Modern, integrated cloud services designed for CX don’t support just today’s innovations. With the right cloud choices, companies can continually evolve to meet tomorrow’s CX challenges.

(Photo of Des Cahill by Bob Adler, The Verbatim Agency)

Emerging Tech Helps Progressive Companies Deliver Exceptional CX

Mary Ann Davidson - Fri, 2018-05-18 19:18

It’s no secret that the art of delivering exceptional service to customers—whether they’re consumers or business buyers—is undergoing dramatic change. Customers routinely expect highly personalized experiences across all touchpoints, from marketing and sales to service and support. I call each of these engagements a moment of truth—because leaving customers feeling satisfied and valued at each touchpoint will have a direct bearing on their loyalty and future spending decisions.

This is why customer experience (CX) has become a strategic business imperative for modern companies. Organizations that provide effective, well-integrated CX across the entire customer journey achieved compound annual growth rates of 17%, versus the 3% growth rates logged by their peers who provided less-effective customer experiences, according to Forrester’s 2017 “Customer Experience Index.”

Fortunately, it’s becoming easier to enter the CX winner’s circle. AI, machine learning, IoT, behavioral analytics, and other innovations are helping progressive companies capitalize on internal and third-party data to deliver highly personalized communications, promotional offers, and service engagements.

How can companies fully leverage today’s tools to support exceptional CX? If they haven’t already done so, companies should start evolving away from cloud 1.0 infrastructures, where an amalgam of best-of-breed services runs various business units. These standalone cloud platforms might have initially provided quick on-ramps to modern capabilities, but now, many companies are paying a price for that expediency. Siloed data and workflows hinder the smooth sharing of customer information among departments. This hurts CX when a consumer who just purchased a high-end digital camera at a retail outlet, for example, webchats with that same company’s service department about a problem, and the service team has no idea this is a premium customer.

In contrast, cloud 2.0 is focused on achieving a holistic view of customers—thanks to simplified, well-integrated services that support each phase of the customer journey. Eliminating information silos benefits companies by giving employees all the information they need to provide a tailored experience for every customer.

Achieving modern CX requires the right vendor partnerships. That starts with evaluating cloud services according to how complete, integrated, and extensible the CX platform is for supporting the entire customer journey. One option is the Oracle Customer Experience Cloud (Oracle CX Cloud) suite, an integrated set of applications for the entire customer lifecycle. It’s complemented by native AI capabilities and Oracle Data Cloud, the world’s largest third-party data marketplace of consumer and business information, which manages anonymized information from more than a billion business and 5 billion consumer identifiers. This means that business leaders, besides understanding customers based on their direct interactions, can use Oracle Data Cloud for insights into social, web surfing, and buying habits at third-party sites and retailers and then apply AI to find profitable synergies.

As new disruptive technologies come to the market—whether that’s the mainstreaming of IoT or drones for business—companies will be under constant pressure to integrate these new capabilities to improve their CX strategies. Modern, integrated cloud services designed for CX don’t support just today’s innovations. With the right cloud choices, companies can continually evolve to meet tomorrow’s CX challenges.

(Photo of Des Cahill by Bob Adler, The Verbatim Agency)

Emerging Tech Helps Progressive Companies Deliver Exceptional CX

Mark Wilcox - Fri, 2018-05-18 19:18

It’s no secret that the art of delivering exceptional service to customers—whether they’re consumers or business buyers—is undergoing dramatic change. Customers routinely expect highly personalized experiences across all touchpoints, from marketing and sales to service and support. I call each of these engagements a moment of truth—because leaving customers feeling satisfied and valued at each touchpoint will have a direct bearing on their loyalty and future spending decisions.

This is why customer experience (CX) has become a strategic business imperative for modern companies. Organizations that provide effective, well-integrated CX across the entire customer journey achieved compound annual growth rates of 17%, versus the 3% growth rates logged by their peers who provided less-effective customer experiences, according to Forrester’s 2017 “Customer Experience Index.”

Fortunately, it’s becoming easier to enter the CX winner’s circle. AI, machine learning, IoT, behavioral analytics, and other innovations are helping progressive companies capitalize on internal and third-party data to deliver highly personalized communications, promotional offers, and service engagements.

How can companies fully leverage today’s tools to support exceptional CX? If they haven’t already done so, companies should start evolving away from cloud 1.0 infrastructures, where an amalgam of best-of-breed services runs various business units. These standalone cloud platforms might have initially provided quick on-ramps to modern capabilities, but now, many companies are paying a price for that expediency. Siloed data and workflows hinder the smooth sharing of customer information among departments. This hurts CX when a consumer who just purchased a high-end digital camera at a retail outlet, for example, webchats with that same company’s service department about a problem, and the service team has no idea this is a premium customer.

In contrast, cloud 2.0 is focused on achieving a holistic view of customers—thanks to simplified, well-integrated services that support each phase of the customer journey. Eliminating information silos benefits companies by giving employees all the information they need to provide a tailored experience for every customer.

Achieving modern CX requires the right vendor partnerships. That starts with evaluating cloud services according to how complete, integrated, and extensible the CX platform is for supporting the entire customer journey. One option is the Oracle Customer Experience Cloud (Oracle CX Cloud) suite, an integrated set of applications for the entire customer lifecycle. It’s complemented by native AI capabilities and Oracle Data Cloud, the world’s largest third-party data marketplace of consumer and business information, which manages anonymized information from more than a billion business and 5 billion consumer identifiers. This means that business leaders, besides understanding customers based on their direct interactions, can use Oracle Data Cloud for insights into social, web surfing, and buying habits at third-party sites and retailers and then apply AI to find profitable synergies.

As new disruptive technologies come to the market—whether that’s the mainstreaming of IoT or drones for business—companies will be under constant pressure to integrate these new capabilities to improve their CX strategies. Modern, integrated cloud services designed for CX don’t support just today’s innovations. With the right cloud choices, companies can continually evolve to meet tomorrow’s CX challenges.

(Photo of Des Cahill by Bob Adler, The Verbatim Agency)

Emerging Tech Helps Progressive Companies Deliver Exceptional CX

Joshua Solomin - Fri, 2018-05-18 19:18

It’s no secret that the art of delivering exceptional service to customers—whether they’re consumers or business buyers—is undergoing dramatic change. Customers routinely expect highly personalized experiences across all touchpoints, from marketing and sales to service and support. I call each of these engagements a moment of truth—because leaving customers feeling satisfied and valued at each touchpoint will have a direct bearing on their loyalty and future spending decisions.

This is why customer experience (CX) has become a strategic business imperative for modern companies. Organizations that provide effective, well-integrated CX across the entire customer journey achieved compound annual growth rates of 17%, versus the 3% growth rates logged by their peers who provided less-effective customer experiences, according to Forrester’s 2017 “Customer Experience Index.”

Fortunately, it’s becoming easier to enter the CX winner’s circle. AI, machine learning, IoT, behavioral analytics, and other innovations are helping progressive companies capitalize on internal and third-party data to deliver highly personalized communications, promotional offers, and service engagements.

How can companies fully leverage today’s tools to support exceptional CX? If they haven’t already done so, companies should start evolving away from cloud 1.0 infrastructures, where an amalgam of best-of-breed services runs various business units. These standalone cloud platforms might have initially provided quick on-ramps to modern capabilities, but now, many companies are paying a price for that expediency. Siloed data and workflows hinder the smooth sharing of customer information among departments. This hurts CX when a consumer who just purchased a high-end digital camera at a retail outlet, for example, webchats with that same company’s service department about a problem, and the service team has no idea this is a premium customer.

In contrast, cloud 2.0 is focused on achieving a holistic view of customers—thanks to simplified, well-integrated services that support each phase of the customer journey. Eliminating information silos benefits companies by giving employees all the information they need to provide a tailored experience for every customer.

Achieving modern CX requires the right vendor partnerships. That starts with evaluating cloud services according to how complete, integrated, and extensible the CX platform is for supporting the entire customer journey. One option is the Oracle Customer Experience Cloud (Oracle CX Cloud) suite, an integrated set of applications for the entire customer lifecycle. It’s complemented by native AI capabilities and Oracle Data Cloud, the world’s largest third-party data marketplace of consumer and business information, which manages anonymized information from more than a billion business and 5 billion consumer identifiers. This means that business leaders, besides understanding customers based on their direct interactions, can use Oracle Data Cloud for insights into social, web surfing, and buying habits at third-party sites and retailers and then apply AI to find profitable synergies.

As new disruptive technologies come to the market—whether that’s the mainstreaming of IoT or drones for business—companies will be under constant pressure to integrate these new capabilities to improve their CX strategies. Modern, integrated cloud services designed for CX don’t support just today’s innovations. With the right cloud choices, companies can continually evolve to meet tomorrow’s CX challenges.

(Photo of Des Cahill by Bob Adler, The Verbatim Agency)

5 Subjects Every Computer Science Student Should Learn

Shay Shmeltzer - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

Categories: Development

5 Subjects Every Computer Science Student Should Learn

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of...

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Categories: DBA Blogs

5 Subjects Every Computer Science Student Should Learn

Tim Dexter - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

Categories: BI & Warehousing

5 Subjects Every Computer Science Student Should Learn

PeopleSoft Technology Blog - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Peeyush Tugnawat - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Pat Shuff - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Oracle Security Team - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Mary Ann Davidson - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Mark Wilcox - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

5 Subjects Every Computer Science Student Should Learn

Joshua Solomin - Fri, 2018-05-18 18:55

I was fortunate this year to attend the Association for Computer Machinery’s SIGCSE (Special Interest Group on Computer Science Education) conference, where there was a good deal of conversation about what a modern computer science curriculum should include.

Technology changes quickly and it can be difficult for academic programs to keep pace. Still, if computer science students are to contribute meaningfully to the field in either industry or research jobs, it’s critical that they learn modern computing skills. Here are five subjects I think every higher education institution should teach their undergraduate computer science majors:

1. Parallel Programming

The single, standalone server with one CPU has gone the way of the dodo bird, displaced by the cloud, server farms and multithreaded parallel processors. Yet colleges and universities are still mainly teaching their undergraduates sequential programming—programs that execute instructions one after the other—as they have for decades.

Modern computing environments and massive data sets demand not just that we process multiple instructions simultaneously across multiple servers (distributed computing), but also that programs be written to process multiple instructions simultaneously on multicore chips within multiple servers and devices.

Too often, parallel programming is relegated to a single chapter in a textbook, easily skipped when time in the semester runs short. To prepare students for high-performance computing, big data, machine learning, blockchain and more, we must teach them to both think and program in parallel.

2. Green Programming

With the ubiquity of battery-driven computers, energy efficiency is more important than ever. The more we ask our smart devices to do, the more energy they need to do it and the more quickly they exhaust their batteries. The same is true for massive server clusters, where fires related to energy-consumption are not uncommon as we demand faster and faster processing of more and more data.

How you architect a software program directly affects how much energy is needed to execute the program, yet few undergraduate programs teach students about this relationship. In a fast-warming world, one in which we dream big dreams about all the ways artificial intelligence and high-performance computing will make our lives better, it is imperative that we write energy-optimized software. Students will not be able to do that if we don’t teach them how.

3. Collaborative Development

Academia persists in trying to measure what individual students know. In most programming classes, students start from a blank screen and write clean code independently or, less often, with a partner.

But this isn’t how software is engineered in the real world. Professional software engineers almost always start with someone else’s code and work collaboratively in large groups to modify, improve and correct that code, which is then integrated with code written by other engineers in other groups.

It’s common for software development groups to include people from different countries, in different time zones. Working effectively requires team members to communicate well in different languages and across different cultures. It also means that someone else needs to be able to look at your code and know what it does, so following formatting standards and providing clear commenting are critical.

However, in our desire to ensure that each student understands every programming concept and rule of syntax, we overlook opportunities to teach collaborative software development and help students develop critical professional skills.

4. Hardware Architecture

In the minds of most college students, IBM, Intel, and AMD—the inventors and developers of the multicore processor—are old news…old companies founded by old guys. Mobile applications are where the action is.

But mobile apps are driven by data, usually by a lot of data, and they won’t be of much use without the processors, databases and networks that power them.

Computing works and advances based on the entire system, from the power source to the user interface, and students will be more successful if they know how to open the box and “kick the tires.” They can then optimize for energy efficiency and write parallel code that makes use of new hardware architectures. They can manage caching, memory architecture and resource allocation issues. They can explain and explore quantum computing.

Computer science doesn’t stop at software or coding. Students need foundations in hardware architecture, too, including electrical engineering and physics. We need computer scientists who can test and push the boundaries of hardware just as much as they push what can be achieved with software.

5. Computer History and Ethics

Something I heard at the Turing 50th Anniversary celebration last summer has stuck with me: Computing is not neutral. It can be used for good or evil. It can be used to help people and it can be used to manipulate and harm them.

For several decades now, we have been making computing advances for the sake of computing, because what we can make computers do is cool, because the challenge of the next thing is too alluring to pass up, because there is money to be made if we can do “X.”

Just because we can do something with computing, however, doesn’t mean we should. Computing power is so great that we need policies to regulate and manage it, in order to protect and benefit people.

It’s important for students of computing to understand its history and to take courses grounded in ethics so they can make responsible decisions and guide others. They should know computing’s historical villains and heroes, its inventors and detractors, and how it has been used to benefit and hurt people. The old saw applies here: If we do not learn our history, we are doomed to repeat it.

Even in a crowded curriculum, we must ensure students are gaining the skills and knowledge they need to become technology innovators, business leaders and positive contributors to society in the coming decades. This list is only a starting point.

Alison Derbenwick Miller is vice president of Oracle Academy.

How Blockchain Will Disrupt the Insurance Industry

Shay Shmeltzer - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

Categories: Development

How Blockchain Will Disrupt the Insurance Industry

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily...

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Categories: DBA Blogs

How Blockchain Will Disrupt the Insurance Industry

Tim Dexter - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

Categories: BI & Warehousing

How Blockchain Will Disrupt the Insurance Industry

PeopleSoft Technology Blog - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

How Blockchain Will Disrupt the Insurance Industry

Peeyush Tugnawat - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

How Blockchain Will Disrupt the Insurance Industry

Pat Shuff - Fri, 2018-05-18 18:49

The insurance industry relies heavily on the notion of trust among transacting parties. For example, when you go to buy car insurance you get asked for things like your zip code, name, age, daily mileage, and make & model of your car. Other than, maybe, the make & model of your car you can pretty much falsify other information about yourself for a better insurance quote. Underwriters trust that you are providing the correct information, which is one of the many risks in the underwriting business.

Enterprise blockchain platforms such as one from Oracle essentially enables trust-as-a-service in such interactions. Participants (insurer and insured) need to come together to do business, but they do not necessarily trust each other. Blockchain provides a scalable mechanism to securely and easily enable trust in such scenarios. There are 4 key properties of Blockchain that enable trust-as-a-service:

  1. Transparency of digital events and transactions it manages,
  2. Immutability of records stored on the blockchain. through append-only time-stamped and hashed records,
  3. Security and assurance that records stored on blockchain aren't compromised through built-in consensus and encryption mechanisms,
  4. Privacy through cryptography

Blockchain can be a good solution for a number of insurance use cases such as:

  • Reducing frauds in underwriting and claims by validating data from customers and suppliers in the value chain
  • Reducing claims by offering tokenized incentives to promote safer driving behavior by capturing data from insured entities like motor vehicles
  • Enabling pay-per-mile billing for insurance by keeping verifiable records of miles traveled
  • And, in the not so distant future, using blockchain to determine liability in case of an accident between two autonomous vehicles by using blockchain to manage timestamped immutable records of decisions made by deep-learning models from both autonomous vehicles right before the accident.

Besides these use cases, blockchain has potential to eliminate intermediaries, improve transparency of records, eliminate manual paperwork, and error-prone processes, which together can deliver orders of magnitude improvement in operational efficiency for businesses. Of course, there are other types of insurance such as healthcare, reinsurance, catastrophic events insurance, property and casualty insurance, which would have some unique flavor of use cases but they would similarly benefit from blockchain to reduce risk and improve business efficiency.

There is no question that blockchain can, potentially, be a disruptive force in the insurance industry. It would have to overcome legal and regulatory barriers before we see mass adoption of blockchain among the industry participants. 

If you are working on an interesting project related to the use of Blockchain for insurance industry feel free to get in touch by leaving a comment or contact us through social media or Oracle sales rep. We’d be glad to help you connect with our subject matter experts and with your industry peers who may be working on similar use cases with Oracle. For more information on Oracle Blockchain, please visit Oracle Blockchain home pages here, and here

 

 

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