5 ways to find and keep customer-focused IT pros
One option is to bring in workers with industry experience who can take on business-focused IT roles. ... Among people with those types of backgrounds, he said he looks for “attitude and passion to go after unsolved problems.” Another way to find people who will excel at working with customers is to get a sense of how they would solve a business problem with technology. Aaron Gette, CIO of Bay Clubs, a luxury fitness and country club company, said he cares less about titles and hot IT roles and more about intangible qualities. “I’m looking for nontraditional IT people. They like to talk to people, not just on social media, but actually socializing and being involved in initiatives,” he said. “They need to be involved in member forums and understand what’s working with our programs.”
Organisations are changing how they spend their cyber security budget
“Firms are coming to terms with the inevitability of a cyber breach,” said Duncan Brown, research director at PAC. “Rather than spending a majority of security budget on prevention, firms will apply a more balanced approach to budgeting for cyber attacks.” ... It’s vital that organisations find the right balance between prevention and response. An organisation that puts all its eggs in one basket and solely spends on prevention will find itself in a tough situation when it inevitably suffers a breach, ditto for those that spend solely on response. To find the right balance, organisations needs to implement a framework that combines prevent and protect, and detect and respond – and enables them to work together.
Deep Learning Catches On in New Industries, from Fashion to Finance
“One of the things Baidu did well early on was to create an internal deep learning platform,” Ng said. “An engineer in our systems group decided to apply it to decide a day in advance when a hard disk is about fail. We use deep learning to detect when there might’ve been an intrusion. Many people are now learning about deep learning and trying to apply it to so many problems.” Deep learning is being tested by researchers to glean insights from medical imagery. Emmanuel Rios Velazquez, a postdoctoral researcher at the Dana-Farber Cancer Institute in Boston, is exploring whether deep learning could help to more accurately predict a patient’s outcome from images of his or her cancer.
Private Cloud: Insurers' Secure Solution
“The insurance industry has often been slower to adopt public cloud than many other industries because of regulations of how data needs to be managed,” says Jeffrey Goldberg, vice president of research and consulting at Novarica. “Some of those are legitimate concerns about data security and some of it is also fear-based — they would like to get the advantages of public cloud but want to maintain control.” Private cloud models — which are either implemented on-premises behind a corporate firewall, or off-premises but within the client’s firewall and dedicated solely to the client — have begun to address insurers’ security concerns. This sector is growing fast: Research firm Technology Business Research forecasts 35% growth in the private cloud sector in 2015.
The Internet of Things Will Give Rise To The Algorithm Economy
Data is the oil of the 21st century. But oil is just useless thick goop until you refine it into fuel. And it’s this fuel – proprietary algorithms that solve specific problems that translate into actions – that will be the secret sauce of successful organizations in the future. Algorithms are already all around us. Consider the driver-less car. Google’s proprietary algorithm is the connective tissue that combines the software, data, sensors and physical asset together into a true leap forward in transportation. Consider high frequency trading. It’s a trader’s unique algorithm that drives each decision that generates higher return than their competitors, not the data that it accesses. And while we’re talking about Google, what makes it one of the most valuable brands in the world? It isn’t data; it’s their most closely guarded secret, their algorithms.
Q&A with Benjamin Wootton on DevOps Landscape in 2015
DevOps from an automation perspective is now big enough a field to be a specialist area. There are so many tools and the space is moving quickly that people can concentrate on it full time and deliver competitive advantage to their businesses. Having a team of people working with these tools on these type of activities can really work and help all of the developers and testers to go faster, leveraging up their value to the business. I like to see senior engineers in this DevOps team who bring a DevOps mindset and career experience across dev, test and operations. They can then go out and coach other staff members onto the central automation platform, ideally giving those teams an increasing amount of ownership of the automation.
Cloud computing more about agile development than cost
"For CIOs, the message is clear: Shift into the driver seat, or others will," Forrester said in releasing its cloud forecast. "A lot of enterprises are voting with their budgets and they're adopting cloud across the board," Rymer says Small wonder then, that for many organizations, the first question about the cloud is a settled matter -- not a question of if, but when, and how. ... "The bottom line here is ... cloud is the next platform," Rymer says. "We don't get a lot of questions from clients anymore about whether or not they're going to go to public clouds. It's really how do we get there." So how do they get there? To Forrester, it is essential to bridge the gap between the IT shop and the business lines of an organization.
The enterprise technologies to watch in 2015
The new technologies on the list include a few that aren't well-known but I believe represent either key advances likely to grow in strategic importance (machine learning, data science), or new developments that offer very significant benefits tactically with relatively little effort to realize (containers,instant app composition, machine-to-machine systems.) There are also a few long-standing categories which have re-emerged recently as leading areas of technology focus for most organizations with new approaches, or have actually developed into parallel tracks with different levels of impact, often with a clear separation of efforts within many companies (hybrid cloud and commercial public cloud, for example.) I've also consolidated some of last year's items as well, as explained above.
Salesforce teams up with Google and others to breakdown big data tech barriers
“Salesforce Wave for Big Data connects the Analytics Cloud to the industry’s most comprehensive ecosystem of big data innovators. Now every company can extend any data source to business users to transform every customer relationship,” he added. Google’s contribution will tackle the volume piece of the big data equation by allowing users to run advanced queries on their datasets, while Cloudera will provide users with a centralised hub where their information can be stored and analysed securely. Meanwhile, New Relic’s software analytics platform is being introduced to tackle velocity, by providing users with a means of deriving real-time information about the performance of a company’s web and mobile apps.
AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power
Data centers often have significant numbers of idle machines because they are built to handle surges in demand, such as a rush of sales on Black Friday. Sentient has created software that connects machines in different places over the Internet and puts them to work running machine-learning software as if they were one very powerful computer. That software is designed to keep data encrypted as much as possible so that what Sentient is working on–perhaps for a client–is kept confidential. Sentient can get up to one million processor cores working together on the same problem for months at a time, says Adam Beberg, principal architect for distributed computing at the company. Google’s biggest machine-learning systems don’t reach that scale, he says.
Quote for the day:
“No great manager or leader ever fell from heaven, its learned not inherited.” -- Tom Northup
One option is to bring in workers with industry experience who can take on business-focused IT roles. ... Among people with those types of backgrounds, he said he looks for “attitude and passion to go after unsolved problems.” Another way to find people who will excel at working with customers is to get a sense of how they would solve a business problem with technology. Aaron Gette, CIO of Bay Clubs, a luxury fitness and country club company, said he cares less about titles and hot IT roles and more about intangible qualities. “I’m looking for nontraditional IT people. They like to talk to people, not just on social media, but actually socializing and being involved in initiatives,” he said. “They need to be involved in member forums and understand what’s working with our programs.”
“Firms are coming to terms with the inevitability of a cyber breach,” said Duncan Brown, research director at PAC. “Rather than spending a majority of security budget on prevention, firms will apply a more balanced approach to budgeting for cyber attacks.” ... It’s vital that organisations find the right balance between prevention and response. An organisation that puts all its eggs in one basket and solely spends on prevention will find itself in a tough situation when it inevitably suffers a breach, ditto for those that spend solely on response. To find the right balance, organisations needs to implement a framework that combines prevent and protect, and detect and respond – and enables them to work together.
Deep Learning Catches On in New Industries, from Fashion to Finance
“One of the things Baidu did well early on was to create an internal deep learning platform,” Ng said. “An engineer in our systems group decided to apply it to decide a day in advance when a hard disk is about fail. We use deep learning to detect when there might’ve been an intrusion. Many people are now learning about deep learning and trying to apply it to so many problems.” Deep learning is being tested by researchers to glean insights from medical imagery. Emmanuel Rios Velazquez, a postdoctoral researcher at the Dana-Farber Cancer Institute in Boston, is exploring whether deep learning could help to more accurately predict a patient’s outcome from images of his or her cancer.
Private Cloud: Insurers' Secure Solution
“The insurance industry has often been slower to adopt public cloud than many other industries because of regulations of how data needs to be managed,” says Jeffrey Goldberg, vice president of research and consulting at Novarica. “Some of those are legitimate concerns about data security and some of it is also fear-based — they would like to get the advantages of public cloud but want to maintain control.” Private cloud models — which are either implemented on-premises behind a corporate firewall, or off-premises but within the client’s firewall and dedicated solely to the client — have begun to address insurers’ security concerns. This sector is growing fast: Research firm Technology Business Research forecasts 35% growth in the private cloud sector in 2015.
The Internet of Things Will Give Rise To The Algorithm Economy
Data is the oil of the 21st century. But oil is just useless thick goop until you refine it into fuel. And it’s this fuel – proprietary algorithms that solve specific problems that translate into actions – that will be the secret sauce of successful organizations in the future. Algorithms are already all around us. Consider the driver-less car. Google’s proprietary algorithm is the connective tissue that combines the software, data, sensors and physical asset together into a true leap forward in transportation. Consider high frequency trading. It’s a trader’s unique algorithm that drives each decision that generates higher return than their competitors, not the data that it accesses. And while we’re talking about Google, what makes it one of the most valuable brands in the world? It isn’t data; it’s their most closely guarded secret, their algorithms.
Q&A with Benjamin Wootton on DevOps Landscape in 2015
DevOps from an automation perspective is now big enough a field to be a specialist area. There are so many tools and the space is moving quickly that people can concentrate on it full time and deliver competitive advantage to their businesses. Having a team of people working with these tools on these type of activities can really work and help all of the developers and testers to go faster, leveraging up their value to the business. I like to see senior engineers in this DevOps team who bring a DevOps mindset and career experience across dev, test and operations. They can then go out and coach other staff members onto the central automation platform, ideally giving those teams an increasing amount of ownership of the automation.
Cloud computing more about agile development than cost
"For CIOs, the message is clear: Shift into the driver seat, or others will," Forrester said in releasing its cloud forecast. "A lot of enterprises are voting with their budgets and they're adopting cloud across the board," Rymer says Small wonder then, that for many organizations, the first question about the cloud is a settled matter -- not a question of if, but when, and how. ... "The bottom line here is ... cloud is the next platform," Rymer says. "We don't get a lot of questions from clients anymore about whether or not they're going to go to public clouds. It's really how do we get there." So how do they get there? To Forrester, it is essential to bridge the gap between the IT shop and the business lines of an organization.
The enterprise technologies to watch in 2015
The new technologies on the list include a few that aren't well-known but I believe represent either key advances likely to grow in strategic importance (machine learning, data science), or new developments that offer very significant benefits tactically with relatively little effort to realize (containers,instant app composition, machine-to-machine systems.) There are also a few long-standing categories which have re-emerged recently as leading areas of technology focus for most organizations with new approaches, or have actually developed into parallel tracks with different levels of impact, often with a clear separation of efforts within many companies (hybrid cloud and commercial public cloud, for example.) I've also consolidated some of last year's items as well, as explained above.
Salesforce teams up with Google and others to breakdown big data tech barriers
“Salesforce Wave for Big Data connects the Analytics Cloud to the industry’s most comprehensive ecosystem of big data innovators. Now every company can extend any data source to business users to transform every customer relationship,” he added. Google’s contribution will tackle the volume piece of the big data equation by allowing users to run advanced queries on their datasets, while Cloudera will provide users with a centralised hub where their information can be stored and analysed securely. Meanwhile, New Relic’s software analytics platform is being introduced to tackle velocity, by providing users with a means of deriving real-time information about the performance of a company’s web and mobile apps.
AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power
Data centers often have significant numbers of idle machines because they are built to handle surges in demand, such as a rush of sales on Black Friday. Sentient has created software that connects machines in different places over the Internet and puts them to work running machine-learning software as if they were one very powerful computer. That software is designed to keep data encrypted as much as possible so that what Sentient is working on–perhaps for a client–is kept confidential. Sentient can get up to one million processor cores working together on the same problem for months at a time, says Adam Beberg, principal architect for distributed computing at the company. Google’s biggest machine-learning systems don’t reach that scale, he says.
Quote for the day:
“No great manager or leader ever fell from heaven, its learned not inherited.” -- Tom Northup
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