Dice Tech Salary Survey Results | Dice Resource Center #Jobs

Released January 24, 2012.

Tech professionals see pay jump.
Bonus popularity on the rise.

Download a printable PDF of the Dice Tech Salary Survey Results.

Technology professionals enjoyed their largest annual salary growth since 2008, according to the latest Dice Tech Salary Survey. After two straight years of wages remaining nearly flat, tech professionals on average garnered salary increases of more than 2%, boosting their average annual wage to $81,327 from $79,384 in 2010.

A more considerable jump was noted in both size of average bonuses, up 8% to $8,769, and the number of technology professionals receiving bonuses: 32% in 2011, compared with 29% in 2010 and 24% in 2009. The industries most likely to pay out bonuses: Telecom, Hardware, Banking, Utilities/Energy and Software.

“Finally! Compensation has mustered some momentum, as more and more top tech markets are notching increases in pay. Silicon Valley’s compensation moved first and wrote the playbook for highly qualified tech professionals to ask for more – whether that be in Seattle, Houston or Raleigh,” said Tom Silver, SVP, North America at Dice.

“The increasing popularity of bonuses shows companies are rewarding their top performers. While everyone loves a bonus, anyone who has been through a cycle knows that bonuses both reward and punish. In fast-changing markets, it’s imperative for highly skilled tech professionals to capitalize on their career and compensation options.”

Six-figure Silicon Valley

In Silicon Valley, annual tech salaries topped six figures for the first time since the survey began about a decade ago. The highest in the nation, Silicon Valley’s annual salary of $104,195, increased 5% year/year. In addition, bonuses are both fatter and more frequent in Silicon Valley – with 38% of tech professionals receiving bonuses at an average of $12,450.

While the Valley’s resurgence is well documented, other tech markets did exceptionally well too. In fact, 12 of the top 20 cities for tech jobs had above average wage growth. Austin had a 13% jump in pay to average $89,419. Portland, OR showed an annual wage increase of 12% to $82,055; Houston saw 7% growth ($89,307); and Washington D.C./Baltimore experienced nearly 6% growth ($94,317).

Chicago and Seattle each garnered 5% increases in average tech salaries, Denver and Dallas/Ft. Worth managed 4% growth, while New York, Los Angeles and Raleigh, North Carolina each increased 3%.

“Conventional wisdom says that as Silicon Valley goes, so goes the tech world. That’s true, and Silicon Valley is going well, but it doesn’t tell the entire story when it comes to tech employment,” added Mr. Silver. “Nationally, we’re seeing stiffer competition and higher salaries for tech pros with the right skill sets and the right experience level.”

Difference makers: skills and experience

While salaries are on the rise among technology professionals, entry-level salaries continue to be pushed downward, according to the survey. The professionals who generally saw their wages increase were those with 11 or more years of experience in their field.

The skills that commanded six-figure salaries and had above average year/year growth are:

“This looks like a push towards enterprise java — with WebSphere, JBoss and WebLogic showing outsized gains,” said Alice Hill, Managing Director, Dice.com. “Not to mention, a continuation of the trends we’ve seen toward tech professionals helping their companies gain more insight into their cost structures, customer behavior and emerging trends. If tech professionals spark companies to win by harnessing their data, that’s when the tech department is no longer seen as a cost center, but a strategic partner in meeting companies’ goals.”

Additional valuable salary tables










Dice Salary Survey Methodology

The Dice Salary Survey was administered online with 18,325 employed technology professionals responding between September 19 and November 21, 2011. Respondents were invited to participate in the survey through a notification on the Dice home page, and registered technology professionals were sent an email invitation. A cookie methodology was used to ensure that there was no duplication of responses between or within the various sample groups, and duplicate responses from a single email address were removed.

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Business intelligence, analytics help CIO challenge collective wisdom #BI

Atefeh “Atti” Riazi says she is a big believer in gut instinct. But when it comes to IT and business alignment, she’s convinced that intuition must give way to decisions based on business intelligence, predictive analytics in particular.

After all, CIOs have not exactly excelled at predicting the future, Riazi noted, judging from the profession’s black eyes over technology spending that failed to deliver an ROI or customer value.

More leadership resources

Atefeh Riazi
Atefeh Riazi

Business intelligence, analytics help CIO challenge collective wisdom

CIO Atefeh Riazi: IT innovation requires breaking some eggs

Atefeh Riazi on IT innovation

CIO Innovators: Profiles in IT and business leadership

“We are guessing the future based on the knowns that we have,” said Riazi, CIO of the New York City Housing Authority (NYCHA). “The problem is we don’t know what we don’t know,” she said, channeling former U.S. Secretary of Defense Donald Rumsfield.

Corralling IBM’s consulting services (pro bono, due to the vendor’s interest in creating energy-efficient “smart buildings”), Riazi is using business intelligence and predictive analytics to challenge — as she puts it — “urban legends and sacred cows” of public assistance programs. Everything — from how best to reduce operating costs and increase building efficiencies, to which investments will actually improve the quality of life of more than 400,000 NYCHA residents — is being examined with BI and predictive analytics tools.

Riazi uses a variety of BI tools, from SAP AG’s BusinessObjects and IBM’s Cognos and WebSphere, to an Omniscope application by Visokio Ltd. that runs correlations on large data sets on Riazi’s desktop. The products, however, are not the point, she insists: “It’s about opening minds.”

To foster the open-mindedness that analytics and predictive modeling require, Riazi urges CIOs to start adding some statisticians, mathematicians and sociologists to their staffs.

“We have to get out of our comfort zone, which means IT is not about deploying hardware and software. It is about intelligence, which is why IT professionals had better understand how to use data,” Riazi said. Analytics is going to take the IT profession “from the place of ‘thinking’ to what I call the wisdom phase. It is not that ‘I think, therefore I am,’ but that ‘I know, therefore I am,” she said.

The challenge for Riazi is to gather data from the vast group of people living in NYCHA buildings. It’s the largest housing authority in North America, with some 13,000 employees on hand, but many NYCHA occupants don’t have online access. Without that, collecting data about how to improve the authority’s aging buildings or reduce crime surrounding the housing areas is difficult, to say the least. Business intelligence can help fill that data gap, however.

Unexpected BI and analytics results

One example where business intelligence and predictive analytics proved helpful and gave unexpected insights is the work Riazi’s team recently did on crime. The “sacred cow” says cameras deter violent crime. After running analytics on a decade’s worth of information from multiple sources, including police reports, her team’s data showed that security cameras do deter vandalism.

However, the cameras appeared to have no effect on violent crime once they had been in place for two months or longer. Only when cameras were coupled with other measures, such as random police patrols and a good intercom system, was crime deterred.

“Here we were, ready to make huge investments on additional camera equipment,” Riazi said, “before these findings showed that would not be a particularly useful expenditure.”

The most valuable lesson Riazi and her team learned from IBM’s business intelligence and predictive analytics practices was the importance of analyzing a variety of elements, many of which at first seemed irrelevant to the authority’s core mission, she said. She began applying data from outside sources — from Europe and the U.S. Census Bureau, for instance — to figure out what actually does deter crime; and she began considering other factors: the distance of the nearest supermarket, for example, and the proximity of houses of worship. Then the NYCHA data began taking on a different look.

“You kind of start playing, taking some elements out and introducing some strange elements, then seeing where your model [for deducing what deters crime] starts tracking,” Riazi said.

Making smart buildings smart with BI

Riazi took advantage of IBM’s interest in smart buildings to help vet IT’s contribution to NYCHA’s push to make its housing more energy-efficient. The accepted dogma, Riazi said, was that if the housing authority put energy performance instrumentation on its boilers, elevators and lighting, it would reduce energy costs.

“The first position I take is, ‘Is that correct? Let’s prove it,’” Riazi said. “If I ask my chairman for $2 billion to make an investment, will it pay? We have done a lot of work [with business intelligence] in finding where the value comes from.”

Project roadmap

2009

  • Atefah “Atti” Riazi joins the New York City Housing Authority.
Early 2010
  • Riazi studies the impact that BI would have on residents’ quality of life, IT performance and costs.
Mid-2010
  • IBM offers its BI and “smart building” consulting service pro bono.
Summer 2010
  • Analysis is completed and helps redirect IT and business investments strategies.
Next phase
  • BI is used to gain a better understanding of the value of investments to improve future living conditions. — L.T.

Business intelligence and predictive analytics showed that the “first value” actually comes from having new windows, a roof that doesn’t leak and an efficient boiler, she said. “If your boiler hasn’t been tuned and it is running at 40% efficiency, it makes no sense to put instrumentation on it.”

Moreover, past reviews on the usefulness of instrumentation have not been an exact science. Much of the discussion and many of the pilot programs on using instrumentation to reduce energy costs typically have involved 10 builders or fewer, she said, or less than 1% of NYCHA’s housing stock.

In addition, while energy standards for such instrumentation exist, they have not resulted in the software and applications that make a boiler, for example, “talk” to a management console. So, Riazi has assigned her team of software engineers to develop the software.

“We are writing the language — the handshake from the controller to the building management systems — based on some of these standards. We believe it will become the government standard — and when that happens, the North American standard for smart buildings,” Riazi said.

A career of ruffling feathers

Challenging the accepted collective wisdom turns out to be something of a habit for the Iranian-born Riazi. In her 20s, freshly armed with a degree in electrical engineering, she helped drive the New York Metropolitan Transit Authority’s effort to revolutionize the way New Yorkers travel, with the introduction of the MetroCard. That was a $2 billion project that stretched over some 14 years and ruffled a ton of feathers.

As global CIO at the advertising firm Ogilvy & Mather, Riazi operated in the thick of company politics. A big part of her job was traveling from country to country convincing the firm’s business elite that IT transformation could provide a competitive edge. Today, in addition to her job at NYCHA, she is proselytizing for IT transformation on behalf of CIOs Without Borders, a not-for-profit charity she launched, and of which she is executive director. The organization is patterned after the well-known medical humanitarian organization; the CIO version is dedicated to using IT to provide medical information to underserved communities worldwide, she said.

“Thinking outside the box is our job as CIO,” Riazi said.

When IBM came in, Riazi said, there was reluctance in the beginning to do just that — to question the same old sacred cows and urban legends. “Then you realize there is value in just going along and [instead] proving yourself wrong, because in the end, when the data tells you, ‘No, you were wrong,’ you realize you have to delve into it,” she said.

Right now, IT is in the infancy of what analytics can do, Riazi said, and she is first to say that her team is just scratching the surface. But the promise? Breathtaking.

CIO Innovators

The SearchCIO.com CIO Innovators profile series highlights how CIOs use technology to meet both IT and business leadership objectives. To suggest a leader for a future CIO Innovator profile, email editor@searchcio.com.

Let us know what you think about the story; email Linda Tucci, Senior News Writer.


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Turning to BI analytics to turn a profit #BI

The recession is possibly the best thing that has happened to business intelligence, allowing the power of BI analytics to finally come into focus.

Like many companies, Dealer Services Corp., a national inventory lender for independent used-car dealers, did not see the effects of the recession on its balance sheet until the third quarter of 2008.

Dealer Services’ CIO Chris Brady started taking action in the first quarter, however, thanks to business analytics data she culled from Information Builders Inc.’s WebFocus RStat BI platform. Armed with that data, her team spotted dealers whose inventory was lagging. If she saw, for example, that a car that normally sold in 47 days was still sitting on the lot at day 62, she suggested that the dealer start trimming inventory and stop waiting for a lucky sale on day 63.

Imparting that information to its customers might seem counterintuitive in the short term, said Brady, on hand for this week’s Information Builders Summit, the annual gathering of the company’s WebFocus BI platform users. “The less business they do, the less we do, but it is better than doing bad business or letting these guys go out of business,” she said. For Carmel, Ind.-based Dealer Services, it was a six months’ heads-up that it needed to adjust its financial planning.

Utz Quality Foods Inc., the family-owned potato chip and snack food maker, also is gathering BI analytics on supermarket chain sales of its snack foods, and sharing this information with its customer base. The aim of the project is to reshape its product development strategy as customer demand shifts.

Getting BI analytics underway

BI analytics is about finding patterns in the avalanche of data generated by, and relevant to the business that allow it to anticipate future events and help drive business decisions.

“If you think about it, analytics is really the last frontier for competitive advantage,” said Wayne Eckerson, director of TDWI Research in Hingham, Mass., a consulting firm and membership organization for BI and data warehousing professionals. “We’ve done just about everything else; we’ve streamlined processes, engineered processes, cut costs, looked at human capital.”

The less business [dealers] do, the less we do, but it is better than doing bad business or letting these guys go out of business.

Chris Brady, CIO, Dealer Services Corp.

Organizations or departments with an “analytical pain,” are good places to begin a new BI analytics project, Eckerson said, citing an example of a police department that was driven to use analytics to predict and prevent crime after winning the dubious distinction of being fifth on the list of the most dangerous cities in the country. The department now meshes historical and current crime data with geographical, weather and event data (including phases of the moon), as well as a plethora of other data to optimize its police coverage.

Phil Collard, head of business operational support, at U.K.-based Scottish and Southern Energy PLC (SSE), the country’s largest energy supplier, said he can attest to the importance of picking the right project. He spearheaded a customer-driven overhaul of SSE’s customer portal and Web services. The project got off the ground after data (including a massive customer survey) showed that customers wanted secure and reliable access to billing 24/7. SSE wanted to both reduce the cost of managing commercial customers and enhance service. With a new Web-based system, sales teams, armed with real-time data, now also can alert customers about spikes in consumption, giving them the option to modify their behavior.

Had it not gathered such BI analytics data, the company stood the chance of losing its largest revenue-generating power users. “If we didn’t change, we would definitely lose market share,” Collard said. About 40% of these big-business power consumers now are using the website, resulting in a revenue increase measured in the many millions of dollars, he said.

Let us know what you think about the story; email Linda Tucci, Senior News Writer.


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BI projects require open mind, deft touch

Niel Nickolaisen
The Real Niel
Niel Nickolaisen

If the myriad CIO surveys performed recently are to be believed, analytics projects are one of the CIO’s top current priorities. I have found that one key to ensuring such business intelligence projects start and end well is remembering that analytics projects differ greatly from other types of IT projects. How are they different? Let me count the ways:

1. Analytics projects can be highly nuanced.

Other IT projects — accounting or production planning, for example — follow a fairly well-understood process. Analytics projects, on the other hand, reflect the way humans make decisions. And because humans make decisions in a nearly infinite number of ways, analytics projects often do not follow a prescribed path.

Instead of mapping business rules, transactions and workflows, analytics projects require that we stay in very close contact with our project stakeholders so the project can track to the meanderings of their human minds.

2. Analytics projects might evolve in unanticipated ways.

Both the nuanced nature of human decision making and our successful analytics projects argue for projects that are designed to evolve and adapt to change. This means that there likely is no such thing as a “Big Bang” analytics project. These projects work best when we use Agile methods and create and get feedback on rapid prototypes, plan multiple project iterations, and make frequent go/no-go decisions.

For example, one of our departments was adamant about the end state of an analytics project. They knew exactly what they needed, and they wanted it in one major release. When I talked with them about breaking the project into adaptable phases, they told me, “No need, we know how to make the decisions; we just need you to get us the data.”

Not wanting to give in to a doing a Big Bang analytics project, I issued a challenge: Let’s take an Agile approach to the project, then decide whether it worked. If the Agile approach was inferior, I would buy the project sponsor and her staff a lunch. With that gauntlet thrown down, the sponsor and I agreed on a first phase that supported what her team felt would be the most obvious benefit of the project.

For the next few weeks, as we worked on the first phase and the sponsor started playing with an early version of our product, she and her team recognized they were gaining insights into cause-effect relationships they had not anticipated. As we starting planning for the next phase, their requirements changed completely. No longer did they want to gather data about product returns. Instead, lack of inventory, or stockouts, became the driving factor for the next phase. When the project ended (successfully, I might add) the end state in no way resembled what the team had thought of originally. Rather, it had evolved along with their thinking and decision making.

3. Effective analytics projects incorporate external data.

IT projects typically focus on information that is inside the organization: accounts and amounts, items, inventories, sales orders, customer contacts. In order to lead effectively to improved decision making, however, analytics projects must gather information from outside the enterprise as well. Because we might not control or even know how to get to this data, our planning should include experiments and tasks to find it.

Suppose our analytics project is designed to improve decisions about our product lifecycle management. We can better manage these lifecycles if we have such internal information as sales transactions, inventory records, product-line goals, pricing and discount history, success criteria, and marketing campaign results. Internal information alone, however, might not lead to better lifecycle management. It also might be worthwhile to factor in external information: competitive products, product alternatives, product placement, macroeconomic data, weather, school calendars — pretty much anything that might affect a product’s potential success.

Once we understand which external information might improve our decision making, we can determine how critical and how available it is — as well as how much we are willing to pay for it.

4. Analytics projects focus on cause-effect relationships.

Three underlying beliefs drive my approach to analytics projects. First, such projects should improve decision making demonstrably. Second, better decision making comes from establishing cause-effect relationships accurately. Third, establishing accurate cause-effect relationships is extremely difficult and fraught with risk.

Why “fraught with risk”? We often think that correlation is the same as cause. Having an excellent correlation does not mean that there is a cause-effect relationship.

We can make business intelligence incredibly powerful if we establish cause. The foundation of improved decision making is establishing cause. But how do we do that? For starters, we can think through these (hopefully obvious) cause-effect relationships. But before we commit to these relationships, we should test our assumptions to see whether they exist. Will reducing call-center queue times increase sales? Try it and see. Will longer testing cycles improve product quality? Try them and see.

Analytics projects also require IT leadership that is both credible and consultative: credible about delivering phased, high-impact projects; credible about changing the role of IT to deliver business value through analytics; consultative about the power that quality analytics can unleash; consultative about taking a different approach to BI projects.

Niel Nickolaisen is CIO at Western Governors University in Salt Lake City. He is a frequent speaker, presenter and writer on IT’s dual role enabling strategy and delivering operational excellence. Write to him at nnick@wgu.edu.

This was first published in February 2012

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Wolfram, a Search Engine, Finds Answers Within Itself

Stephen Wolfram, a 52-year-old scientist, software designer and entrepreneur, tends to go his own way — often with noteworthy results. He published his first physics paper at 15, earned his Ph.D. from Caltech at 20 and two years later won a MacArthur prize.

Less than three years ago, Dr. Wolfram created a new kind of search engine, called Wolfram Alpha. Unlike Google or Microsoft’s Bing, Wolfram Alpha does not forage the Web. It culls its own painstakingly curated database to find answers.

There was skepticism in 2009, when Wolfram Alpha arrived, with critics saying the approach was very limited, useful mainly for math and science facts. But the technology has come a long way, including delivering many answers for Siri, the question-answering personal assistant in the Apple iPhone 4S.

The new version of Wolfram Alpha arrives Wednesday afternoon. Its formal name is Wolfram Alpha Pro, and Dr. Wolfram calls “Step 2, the next step of what can be done with this approach,” which he describes as a “computational knowledge engine.” This is a premium version of the search engine: $4.99 a month, or $2.99 for students.

The new version handles data and images. In a recent demonstration, Dr. Wolfram, using his computer mouse, dragged in a table of the gross domestic product figures for France for 1961 to 2010, and Wolfram Alpha produced on the Web page a color-coded bar chart, which could be downloaded in different document formats. He put in a table of campaign contributions to politicians over several years, and Wolfram Alpha generated a chart and brief summary, saying that House members received less on average than senators.

Dr. Wolfram dragged in a 3-D image and after a few seconds it rendered the image — a guitar — and reported the number of polygons (2,253), among other characteristics.

The Wolfram data-deciphering engine, however, was flummoxed by a table of occupational income figures plucked from the Bureau of Labor Statistics’ Web site. Dr. Wolfram suggested that it was confused by all the periods used to separate columns of numbers in the table.

This week is a beginning, Dr. Wolfram admitted. But, he added, “We’re starting to have the ability to understand data and images in the way we understand text queries.”

The text understanding of Wolfram Alpha has advanced steadily, with hundreds of subject domains added. They go well beyond the service’s origins, which built off the knowledge base in Mathematica, a popular math-formula software created by Dr. Wolfram. Serious math students, though, remain among Wolfram Alpha’s most avid users.

The subjects in the Wolfram Alpha database are now more useful to the average person. Type in “Tinker Tailor Soldier Spy showtimes,” and Wolfram Alpha delivers the schedule for local theaters. The movie times, Dr. Wolfram notes, come not from scouring the Web, but from a specialized information service.

Siri accounts for about a quarter of the queries fielded by Wolfram Alpha, whose staff has grown to 200. Several large companies in health care, financial services and oil and gas recently hired Dr. Wolfram’s private company, Wolfram Research, to do tailored corporate versions of Wolfram Alpha for them. Microsoft also licenses Wolfram Alpha technology.

Wolfram Alpha is one of a number of efforts to build greater understanding of the meaning of words — or semantics — into search, said Oren Etzioni, a computer scientist at the University of Washington. I.B.M.’s Watson, Apple’s Siri and Wolfram Alpha rely on structured databases of knowledge, while Google and Bing are trying to add more semantic understanding into general search engines.

The progress, Dr. Etzioni said, is good for the field. “It raises the stakes for everyone around the table,” he said.

via nytimes.com

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