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2014 U.S. State of Cybercrime Infographic

 2014 U.S. State of Cybercrime Infographic

U.S. organizations are still losing the cyberwar to hackers according to the 2014 U.S. State of Cybercrime survey, recently conducted by CSO, PwC, the U.S. Secret Service, and the CERT Division of Software Engineering Institute at Carnegie Mellon University.

The U.S. State of Cybercrime infographic illustrates the results from this survey as well as the continuing upheaval organizations face combatting cybercrime and the effects it is having and will continue to have on U.S. organizations.

For more information on the study, click here

cybercrime copy 2014 U.S. State of Cybercrime Infographic

Salesforce Takes On Tableau, Oracle In Analytics

Investors.com

Analyze this: Salesforce.com is challenging Tableau Software, Oracle and others for a piece of the fast-growing, multibillion-dollar data analytics software market.

Salesforce.com plans to launch its first major data analytics software service on Monday. The enterprise software company intends to compete with rivals that already offer software to help companies analyze large amounts of data through easy-to-read charts and graphs.

A pioneer in software-as-a-service delivered via the Internet cloud, Salesforce.com is the No. 1 maker of customer relationship management (CRM) software, which helps companies deal with customers and partners. CRM is a key segment of the business software market, but Salesforce is moving into other areas to boost revenue.

Its latest move is another step in becoming a one-stop shop for all of a company’s business software needs, says Anna Rosenman, director of Salesforce.com’s analytics cloud.

“This is one of the biggest announcements we have made in years,” Rosenman told IBD. “We are entering an entirely new market.”

Salesforce has, on a small scale, already offered some data analytics software. The new service, called Wave, will help companies pull a wide swath of data from a variety of sources so it can be chopped up and best used by a company, Rosenman says.

Infographic: Everyday Big Data

Vouchercloud

Scientists and businesses often encounter difficulties in analysing huge data sets, otherwise known as “Big Data”. Its size is forever changing across many landscapes, with the amount of data created each day constantly increasing – now four times faster than the world economy. Every day we create 2.5 quintillion bytes of data, which is enough to fill 10 million Blu-Ray discs, which in turn is enough to make a stack the size of 4 Eiffel Towers. Big doesn’t seem to be quite ‘big’ enough a word to describe how data is evolving.

The most astonishing thing about Big Data is the speed at which it is increasing. 90% of the world’s data, for example, was created in the last 2 years alone. The number of people with access to the internet today is equal to the world’s entire population in 1960 (3 billion). Global communication has never been easier and it might not come as much of a shock that there are 204 million emails sent per minute. But there are also 216,000 Instagram posts and 217,000 tweets. This is social and business conversation at its best.

The data collected through all these interactions is helping to shape the way we live our lives. As you can see below in the data graphic by vouchercloud it is helping us to save money (comparison websites, reducing energy bills, monitoring our fuel consumption and tailored coupons based on our previous spending habits). It is helping us to get around more efficiently – urban transport is improved using real time data capture and managing traffic hotspots by changing bus routes or traffic light sequences to ease congestion. Even more topical and important, it is helping us to save lives; streaming patient data to recognise outbreaks of illnesses and disease, identifying those at risk and managing the costs of treating patients.

Data is improving and expanding across mobile, digital media and social media, and Big Data is innovating the future ahead of us.

Big Data GRAPHIC1 e1413817382616 Infographic: Everyday Big Data

IDG Corporate Video 2015

idg logo1 IDG Corporate Video 2015

IDG is the world’s leading media, events, and research company reaching over 280 million technology buyings in 97 countries.

IDG Communications (a subsidiary of IDG) is the largest global technology media, data and services company. It delivers personalized and contextual-based experiences for the most powerful tech buyers.

From millennial tech enthusiasts to senior executives, IDG understands and reaches them all.

With New Ad Platform, Facebook Opens Gates to Its Vault of User Data

The New York Times

SAN FRANCISCO — Facebook built itself into the No. 2 digital advertising platform in the world by analyzing the vast amount of data it had on each of its 1.3 billion users to sell individually targeted ads on its social network.

Now it is going to take those targeted ads to the rest of the Internet, mounting its most direct challenge yet to Google, the leader in digital advertising with nearly one-third of the global market.

On Monday, Facebook will roll out a rebuilt ad platform, called Atlas, that will allow marketers to tap its detailed knowledge of its users to direct ads to those people on thousands of other websites and mobile apps.

“We are bringing all of the people-based marketing functions that marketers are used to doing on Facebook and allowing them to do that across the web,” David Jakubowski, the company’s head of advertising technology, said in an interview.

Continue reading…

3 mistaken assumptions about what Big Data can do for you

CITEworld

Big data is certainly all the rage. The Wall Street Journal recently ran a piece ondata scientists commanding up to $300,000 per year with very little experience. Clearly the era of embracing big data is here.

However, since the tools and best practices in this area are so novel, it’s important to revisit our assumptions about what big data can do for us – and, perhaps more importantly, what it can’t do. Here are three commonly held yetmistaken assumptions about what big data can do for you and your business.

Big Data Can’t Predict the Future

Big data – and all of its analysis tools, commentary, science experiments and visualizations – can’t tell you what will happen in the future. Why? The data you collect comes entirely from the past. We’ve yet to reach the point at which we can collect data points and values from the future.

We can analyze what happened in the past and try to draw trends between actions and decision points and their consequences, based on the data, and we might use that to guess that under similar circumstances, if a similar decision were made, similar outcomes would occur as a result. But we can’t predict the future.

Many executives and organizations attempt to glean the future out of a mass of data. This is a bad idea, because the future is always changing. You know how financial advisers always use the line, “Past performance does not guarantee future results?” This maxim applies to big data as well.

Instead of trying to predict the future, use big data to optimize and enhance what’s currently true. Look at something that’s happening now and constructively improve upon the outcomes for that current event. Use the data to find the right questions to ask. Don’t try to use big data as a crystal ball.

Big Data Can’t Replace Your Values – or Your Company’s

Big data is a poor substitute for values – those mores and standards by which you live your life and your company endeavors to operate. Your choices on substantive issues may be more crystallized, and it may be easier and clearer to sort out the advantages and disadvantages of various courses of action, but the data itself can’t help you interpret how certain decisions stack up against the standards you set for yourself and for your company.

Data can paint all sorts of pictures, both in the numbers themselves and through the aid of visualization software. Your staff can create many projected scenarios about any given issue, but those results are simply that – a projection. Your job as an executive, and as a CIO making these sorts of tools and staff available within your business, is to actually reconcile that data against your company’s values.

For instance, imagine you’re a car manufacturer. Your big data sources and tools tell you that certain vehicle models have a flaw that may cost a few cents to repair on vehicles yet to be manufactured, but would cost significantly more to repair in vehicles that have already been purchased by customers and are in production use. The data, and thus your data scientists on staff, might recommend fixing the issue on cars still on the assembly line but not bothering to fix the cars already out there in the world, simply because the data might have shown the cost exceeded the likelihood of damages across the board.

(Note that this scenario may sound familiar to you if you have been following theGeneral Motors ignition switch saga. However, this is only a hypothetical example, and further, there is no evidence big data played into the GM recall.)

Say your company has a value statement that quality is job 1 and safety is of paramount importance. Though the data suggests a recall isn’t worth it, you make the call as an executive to start the recall. You’re informed, but you’re not controlled by big data.

Above all, it’s vital to remember that sometimes the right answer appears to be the wrong one when viewed through a different lens. Make sure you use the right lens.

Read more…

Shadow cloud services pose a growing risk to enterprises

IDG News Service

A growing tendency by business units and workgroups to sign up for cloud services without any involvement from their IT organization creates serious risks for enterprises.

The risks from shadow cloud services include issues with data security, transaction integrity, business continuity and regulatory compliance, technology consulting firm PricewaterhouseCoopers (PwC) warned last week.

“The culture of consumerization within the enterprise — having what you want, when you want it, the way you want it, and at the price you want it — coupled with aging technologies and outdated IT models, has propelled cloud computing into favor with business units and individual users,” PwC said in a report.

Increasingly, workgroups and even individual users in companies are subscribing directly to cloud services for business reasons because it is easy and relatively inexpensive for them to do, said Cara Beston, cloud risk assurance leader at PwC.

“There is a new form of shadow IT and it is likely more pervasive across the company” than many might imagine, given the easy access to cloud services, Beston said. “It is harder to find, because it is being procured at small cost and is no longer operating within the bounds of the company.”

Some typical use cases for shadow cloud services include collaboration software, storage, customer relationship management apps and human resources.

The Software as a Service (SaaS) delivery model allows business units and workgroups to quickly deal with business process challenges without having to wait for IT to help out. The fact that the cost for such services is usually an operating expense rather than a capital expense is another advantage.

“Shadow cloud is happening under the radar” at many organizations, Beston said. Without governance, such cloud services present significant data security risks and the potential for technology and service redundancies.

Risks include inadvertent exposure of regulated data, improper access and control over protected and confidential data and intellectual property and breaching of rules pertaining to how some data should be handled.

Companies in regulated industries face a real risk of becoming non-compliant with data security and privacy obligations without even realizing it. Importantly, while many business users sign onto cloud services because of the perceived lower costs, a lack of control over how the services are being used can often result in service duplication and higher-than-anticipated operational costs, she said.

Cloud services for work groups of between five and 10 business users can range from as little as a few hundred dollars a month to a few thousand dollars. But the costs can quickly get out of control when all the different groups that might be using similar services within an organization are counted.

Continue reading…

5 Measurement Pitfalls to Avoid

Mashable

Say your goal is to increase the number of customers you serve each day. Perhaps you run a city office processing food stamp applications, or maybe you’re offering technical support for your company’s product. How many customers do you serve online, in person and over the phone? What’s the average time to resolve a problem in each of these channels? Which types of customer requests take the longest, and which can be handled expediently?

If you can’t answer these questions, you’re setting yourself up for failure before you even begin to try.

Data-driven decision making is a way of life these days, from city hall to the corporate boardroom. If you have the numbers to dictate a course of action, the thinking goes, why would you use your heart or your mind? But in the quest to back up every move with cold, hard data, it can be easy to mistake any old numbers for useful numbers. Not all data is created equal, and the best way to ensure you’ll be collecting the right data is to develop the right set of performance metrics.

So how do you decide which metrics will help you and which will just distract you from the central issues? Here are five common mistakes people make when dealing with data, and some tips to avoid them.

Mistake #1: Just having metrics is enough

It’s true that measuring a little bit is better than measuring nothing. But too many people are satisfied upon merely being able to utter the word “metrics” to a supervisor, and too many supervisors assume that if their team is counting anything at all, they must be doing something right.

Data is only useful if it allows you to measure and manage performance quality. This means it’s not necessarily as important for, say, the Buildings Department to count how many buildings passed inspection as it is for it to know the types of citations that caused them to fail, the number of inspections each inspector completed in one day, and how many buildings corrected their violations within one or two months of initial inspection. This richer set of data will reveal inefficiencies in the inspection process and allow the department to work toward better safety standards.

Mistake #2: The more metrics, the better

A common misconception is that if something can be counted, it should be counted. I’ve made the mistake of laying out tabs and tabs of metrics on a spreadsheet, only to find that the effort required to collect the data is a drain on not only my time, but the time of the people assigned to carry out the very work we’re trying to measure.

You never want your performance monitoring to be so onerous that it actually hinders performance itself. When coming up with a set of metrics, it helps to start by brainstorming everything you could possibly measure, then prioritizing the top 10 indicators that will yield the most critical information about your program. Start with a manageable load, and gradually add more — as long as the effort required to collect the data will pay for itself in useful observations and opportunities for improvement.

Mistake #3: Value judgments should be assigned to volumes

On the surface, it may seem intuitive that more calls answered is better than fewer calls answered. But imagine that in order to squeeze in an extra five calls an hour, the quality of each call is compromised. Less information is gathered, and fewer issues are addressed. Callers aren’t satisfied with the first call, so they call a second or a third time, further increasing your call numbers but taking up extra time and failing to address the reasons why the calls are coming in the first place. Perhaps calls that last a minute longer but more adequately address the caller’s questions end up preventing repeat calls, thus rendering the more-equals-better line of thinking not just mistaken, but backwards.

It’s also important to realize that many metrics, when counted as absolute numbers,aren’t particularly helpful. Without context, a number is more or less meaningless. Any numerator deserves a denominator, and pure numbers should be represented as a percentage of the total. For example, moving 1,000 homeless individuals off of the street and into temporary housing is laudable. But if the goal is to create housing for 20,000 homeless people, then it’s important to recognize that you’re only 5% of the way there.

Continue reading…

The internet of things – the next big challenge to our privacy

The Guardian

If there’s a depressing slogan for the early era of the commercial internet, it’s this: “Privacy is dead – get over it.”

For most of us, the internet is complex and opaque. Some might be vaguely aware that their personal data are getting sucked, their search histories tracked, and their digital journeys scoured.

But the current nature of online services provides few mechanisms for individuals to have oversight and control of their information, particularly across tech-vendors.

An important question is whether privacy will change as we enter the era of pervasive computing. Underpinned by the Internet of Things, pervasive computing is where technology is seamlessly embedded within the real world, intrinsically tied to the physical environment.

If the web is anything to go by, the new hyperconnected world will only make things worse for privacy. Potentially much worse.

More services and more things only mean more data being generated and exchanged. The increase in data volume and complexity might plausibly result in less control. It’s a reasonable assumption, and it leaves privacy in a rather sorry state.

Many of the future predictions about privacy reflect this bleak diagnosis. If privacy isn’t dead yet, then billions-upon-billions of chips, sensors, and wearables will seal the deal.

But before jumping to such conclusions – and bearing in mind the immense power of established tech-vendors and their interest in this space – there may still be reasons to be positive. In particular, the fundamental differences between pervasive computing and Web 2.0 provide a beacon of hope.

One difference is that with pervasive computing, much of the technology becomes tangible and familiar. This makes issues of privacy more readily apparent to users. Web browsing histories stretching back over time are one thing; Google Glass is quite another.

If you can physically witness aspects of data collection, it short-circuits what has traditionally been a long feedback loop between privacy risk and cumulative effect. The hope is that the increased awareness inspires action.

This ties to a second difference: the technology itself could enable action. Unlike the web, where offerings tend to be one-size-fits-all, pervasive computing is driven by the individual, focusing on customised, person-centric services and experiences.

If the technology supporting this properly places individuals in the driving seat, it could also be used to provide individuals with the opportunity to take control of their personal data.

Moving from the abstract web

It has taken years for the sort of awareness and backlash that we’re now starting to see against Facebook, Google, and other major internet vendors that trade in personal data.

This is a product, in many respects, of the inherent obscurity of data collection by web-based services.

Moving from the web to the Internet of Things, many aspects of technology shift from being abstract and hidden, to being grounded in the real world.

Continue reading…

Combining the Flexibility of Public-Cloud Apps with the Security of Private-Cloud Data White Paper

CITEworld

Cloud applications are a priority for every business – the technology is flexible, easy-to-use, and offers compelling economic benefits to the enterprise. The challenge is that cloud applications increase the potential for corporate data to leak, raising compliance and security concerns for IT. A primary security concern facing organizations moving to the cloud is how to secure and control access to data saved in cloud applications.

This white paper explores technologies that combine the flexibility of public cloud apps like Salesforce and Box, with the security and compliance of a private cloud. When deployed as part of an end-to-end data protection program, such an approach can provide the same security and assurances as can be achieved with premises-based applications.

Comprehensive Data Protection in the Cloud

In today’s business, IT may no longer own or manage the apps, the devices, or the underlying network infrastructure, yet is still responsible for securing sensitive corporate data. While cloud application vendors secure their infrastructure, the security of the data remains the responsibility of the customer using the application. A comprehensive approach to data security in cloud environments covers the full lifecycle of data in an organization—in the cloud, on the device, and at the point of access.

•In the Cloud—Most cloud apps don’t encrypt data-at-rest, and those that do encrypt manage the keys themselves. For organizations in regulated industries and/or with sensitive data stored in these apps, the ability to maintain confidentiality of corporate data remains unsolved.

•At Access—Cloud apps provide limited access control, data leakage prevention, and visibility when compared with applications hosted on premises. This makes it difficult to control who, what, where, and when employees access cloud applications.

•On the Device—Since cloud applications can be accessed from any device, anywhere, a comprehensive security solution should include protection for cloud application data on client devices such as laptops, tablets and smartphones.

Click here to view the full white paper