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Cloud Forecast: 18 Plus

Heather McIlvaine

Cloud Computing Forecast, GartnerAccording to Gartner, investment in the cloud will increase by 18 percent yearly through 2016, although it will still only make up 5 percent of total IT spending.

In its latest forecast of IT spending in 2012, Gartner has slightly improved its outlook for 2012 over last quarter’s predictions. Instead of an increase in spending of 2.5 percent, Gartner now expects 3 percent growth for 2012. Despite this adjustment, the growth rate is still well below 2011 numbers.

Feeling the financial crisis

For enterprise software, the outlook has taken a slight turn for the worse since Gartner announced its expectations last quarter. In April, Gartner was predicting 5 percent growth in this area for 2012. Now, it has reduced that number to only 4.3 percent. Spending for IT services, on the other hand, is on the rise. Here, an increase of one percentage point over last quarter’s figures to 2.3 percent, can be noted. Telecommunications is another area experiencing growth. Instead of only 2.2 percent, Gartner now predicts a 3 percent increase in spending.

One of the hottest areas for investment, according to Gartner, is public cloud services. In this area, analysts predict an average yearly growth of 18 percent through 2016 – much higher than the 4 percent increase in total IT spending that is expected for the same time period. Despite this rapid growth, a mere 5 percent of all IT spending will be allotted to the cloud.

Gartner releases its IT spending forecasts on a quarterly basis. The next one will come out in September.

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Heather McIlvaine

About Heather McIlvaine

Heather McIlvaine is the Editor of SAP.info. Her specialties include writing, editing, journalism, online research and publishing.

Innovation Without Boundaries: Why The Cloud Matters

Michael Haws

Is it possible to innovate without boundaries?

Of course – if you are using the cloud. An actual cloud doesn’t have any boundaries. It’s fluid. But more important, it can provide the much-needed precipitation that brings nature to life. So it is with cloud technology – but it’s your ideas that can grow and transform your business.USA --- Clouds, Heaven --- Image by © Ocean/Corbis

Running your business in the cloud is no longer just a consideration during a typical use-case exercise. Business executives are now faced with making decisions on solutions that go beyond previous limitations with cloud computing. Selecting the latest tools to address a business process gap is now less about features and more about functionality.

It doesn’t matter whether your organization is experienced with cloud solutions or new to the concept. Cloud technology is quickly becoming a core part of addressing the needs of a growing business.

5 considerations when planning your journey to the cloud

How can your organization define its successful path to the cloud? Here are five things you should consider when investigating whether a move to the cloud is right for you.

1. Understanding the cloud is great, but putting it into action is another thing.

For most CIOs, putting a cloud strategy on paper is new territory. Cloud computing is taking on new realms: Pure managed services to software-as-a-service (SaaS). Just as legacy computing had different flavors, so does cloud technology.

2. There is more than one way to innovate in the cloud.

Alignment with an open cloud reference architecture can help your CIO deliver on the promises of the cloud while using a stair-step approach to cloud adoption – from on-premise to hybrid to full cloud computing. Some companies find their own path by constantly reevaluating their needs and shifting their focus when necessary – making the move from running a data center to delivering real value to stakeholders, for example.

3. The cloud can help accelerate processes and lower cost.

By recognizing unprecedented growth, your organization can embark on a path to significant transformation that powers greater agility and competitiveness. Choose a solution set that best meets your needs, and implement and support it moving forward. By leveraging the cloud to support the chosen solution, ongoing maintenance, training, and system issues becomes the cloud provider’s responsibility. And for you, this offers the freedom to focus on the core business.

4. You can lock down your infrastructure and ensure more efficient processes.

Do you use a traditional reporting engine against a large relational database to generate a sequential batched report to close your books at quarter’s end? If so, you’re not alone. Sure, a new solution with new technology may be an obvious improvement. But how valuable to your board will you become when you reduce the financial closing process by 1–3 days? That’s the beauty of the cloud: You can accelerate the deployment of your chosen solution and realize ROI quickly – even before the next full reporting period.

5. The cloud opens the door to new opportunity in a secure environment.

For many companies, moving to the cloud may seem impossible due to the time and effort needed to train workers and hire resources with the right skill sets. Plus, if you are a startup in a rural location, it may not be as easy to attract the right talent as it is for your Silicon Valley counterparts. The cloud allows your business to secure your infrastructure as well as recruit and onboard those hard-to-find resources by applying a managed services contract to run your cloud model

The cloud means many things to different people. What’s your path?

With SAP HANA Enterprise Cloud service, you can navigate the best path to building, running, and operating your own cloud when running critical business processes. Find out how SAP HANA Enterprise Cloud can deliver the speed and resources necessary to quickly validate and realize solid ROI.

Check out the video below or visit us at www.sap.com/services-support/svc/in-memory-computing/hana-consulting/enterprise-cloud-services/index.html.

Connect with us on Twitter: @SAPServices

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Michael Haws

About Michael Haws

Michael Haws is the Vice President of HANA Enterprise Cloud at SAP. His specialties include Enterprise Resource Planning Software & Services, Onshore, Nearshore, Offshore--Application, Infrastructure and Business Process Outsourcing.

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Consumers And Providers: Two Halves Of The Hybrid Cloud Equation

Marty McCormick

Long gone are the days of CIOs and IT managers freely spending money to move their 02 Jun 2012 --- Young creatives having lunch and conversation. --- Image by © Hero/Corbisexisting systems to the cloud without any real business justification just to be part of the latest hype. As cloud deployments are becoming more prevalent, IT leaders are now tasked with proving the tangible benefits of adopting a cloud strategy from an operational, efficiency, and cost perspective. At the same time, they must balance their end users’ increasing demand for access to more data from an ever-expanding list of public cloud sources.

Lately, public cloud systems have become part of IT landscapes both in the form of multi-tenant systems, such as software-as-a-service (SaaS) offerings and data consumption applications such as Twitter. Along with the integration of applications and data outside of the corporate domain, new architectures have been spawned, requiring real-time and seamless integration points.  As shown in the figure below, these hybrid clouds – loosely defined as the integration of data from systems in both public and private clouds in a unified fashion – are the foundation of this new IT architecture.

hybridCloudImage

Not only has the hybrid cloud changed a company’s approach to deploying new software, but it has also changed the way software is developed and sold from a provider’s perspective.

The provider perspective: Unifying development and operations

Thanks to the hybrid cloud approach, system administrators and developers are sitting side by side in an agile development model known as Development and Operations (DevOps). By increasing collaboration, communication, innovation, and problem resolution, development teams can closely collaborate with system administrators and provide a continuous feedback loop of both sides of the agile methodology.

For example, operations teams can provide feedback on reported software bugs, software support issues, and new feature requests to development teams in real time. Likewise, development teams develop and test new applications with support and maintainability as a key pillar in design.
After seeing the advantages realized by cloud providers that have embraced this approach long ago, other companies that have traditionally separated these two areas are now adopting the DevOps model.

The consumer perspective: Moving to the cloud on its own terms

From the standpoint of the corporate consumer, hybrid cloud deployments bring a number of advantages to an IT organization. Specifically, the hybrid approach allows companies to move some application functionality to the cloud at their own pace.
Many applications naturally lend themselves to public cloud domains given their application and data requirements. For most companies, HR, indirect procurement, travel, and CRM systems are the first to be deployed in a public cloud. This approach eliminates the requirement for building and operating these applications in house while allowing IT areas to take advantage of new features and technologies much faster.

However, there is one challenge consumers need to overcome: The lack of capabilities needed to extend these applications and meet business requirements when the standard offering is often insufficient. Unfortunately, this tempts organizations to create extensive custom applications that replicate information across a variety of systems to meet end user requirements. This development work can offset the cost benefits of the initial cloud application, especially when you consider the upgrades and support required to maintain the application.

What this all means to everyone involved in the hybrid cloud

Given these two perspectives, on-premise software providers are transforming themselves so they can meet the ever-evolving demands of today’s information consumer. In particular, they are preparing for these unique challenges facing customers and creating a smooth journey to a hybrid cloud.

Take SAP, for example. By adopting a DevOps model to break down a huge internal barrier and allowing tighter collaboration, the company has delivered a simpler approach to hybrid cloud deployments through the SAP HANA Cloud Platform for extending applications and SAP HANA Enterprise Cloud for hosting solutions.

Find out how these two innovations can help you implement a robust and secure hybrid cloud solution:
SAP HANA Cloud Platform
SAP HANA Enterprise Cloud

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Marty McCormick

About Marty McCormick

Marty McCormick is the Lead Technical Architect, Managed Cloud Delivery, at SAP. He is experienced in a wide range of SAP solutions, including SAP Netweaver SAP Portal, SAP CRM, SAP SRM, SAP MDM, SAP BI, and SAP ERP.

How AI Can End Bias

Yvonne Baur, Brenda Reid, Steve Hunt, and Fawn Fitter

We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.

Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.

In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.

Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.

That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.

Bias: Bad for Business

When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.

Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.

As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.

Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.

AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.

That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.

At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.

Look for Where Bias Already Exists

In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.

There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.

Code Is Only Human

The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.

“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)

Don’t Do What You’ve Always Done

To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.

SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.

Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.

Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.

Look Beyond the Obvious

AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.

To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.

Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.

That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.

Context Matters—and Context Changes

Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.

Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.

Moving Forward with AI

Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.

In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.

Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.

O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.

As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Yvonne Baur is Head of Predictive Analytics for Sap SuccessFactors solutions.

Brenda Reid is Vice President of Product Management for Sap SuccessFactors solutions.

Steve Hunt is Senior Vice President of Human Capital Management Research for Sap SuccessFactors solutions.

Fawn Fitter is a freelance writer specializing in business and technology.

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Big Data: Better Than Big Muscles at Kinduct

Stephanie Overby

Travis McDonough has always been looking for a competitive edge. As an amateur athlete “on the small side,” he sought other ways—exercise, nutrition, strategy—to get ahead.

Today McDonough is the of CEO of Kinduct, a provider of cloud-based software that analyzes data from wearables, electronic medical records, computer vision solutions, and more to assess and make recommendations about physical human performance. Kinduct provides 100 professional sports organizations, including the five major sports leagues in North America, with intelligence to make decisions about their athletes and training programs.

Digital Fills a Gap

A chiropractor by training, McDonough owned and operated a network of sports rehabilitation clinics, where he found that patients retained only a fraction of what they were instructed to do through text or conversation. “As we treated athletes, we realized there was a gaping hole in the industry for technology [to fill],” he says.

McDonough first launched a company to create 3D videos designed to help his athlete patients better understand their injuries and the resulting therapy. The videos, delivered by text or e-mail, would illustrate what happens inside the human body when it experiences whiplash, for example.

“We quickly realized we couldn’t just be a content company and push information without understanding more about the athlete,” he says. Athletes and their trainers collected a massive amount of individual health and performance data that was available to be tapped from electronic medical records, wearable devices, and computer vision-based tracking systems that measure and record information such as how fast an athlete is running or jumping. “We needed to be agnostic and aggressive consumers of all kinds of data sources in order to push more targeted programs to our clients,” he says. So McDonough recruited his brother’s brother-in-law (vice president of product, Dave Anderson) to develop software to make sense of it all.

Innovate a Better Athlete

The software is suited for healthcare and military applications: the Canadian Armed Forces uses it to deliver exercise, wellness, and nutrition programs to its troops. But McDonough knew that the world of professional sports would provide his most eager customers.

Professional sports teams use Kinduct’s analytics to reduce injury and win more games.

“The sports world is willing to embrace innovation more quickly than other markets, like healthcare, that are slower-moving. And that’s where our passion lives. Many of us are sports fanatics and have been athletes,” says McDonough of the company’s 70 employees. Kinduct’s first customers were National Hockey League (NHL) teams, followed in short order by the National Basketball Association (NBA).

For its professional sports clients, Kinduct has uncovered more than 100 novel correlations. Most are closely guarded secrets, but several have become public. The company found, for example, that when a basketball player’s sleep falls below a certain threshold, there is a strong correlation with reduced free throw percentages two days later. That discovery led one NBA team (McDonough won’t say which) to focus on getting players to bed on time and making travel schedule changes to enable the requisite rest.

Kinduct software also found correlations for hockey teams. It demonstrated to a leading hockey team that better grip strength was likely to lead to harder and faster shots on goal. Moreover, when the system ingested three years of historical computer vision information, it found that a player’s ability to slow down dramatically affects the chances of soft tissue injuries, which are costly to professional sports teams and athletes. The software can send an alert when it spots a trend that could predict the possibility of such an injury.

We’re in this to go big. That means carrying a burn rate, hiring aggressively, and investing in research.

The software “will never replace the experts in the trenches,” says McDonough. “But we are able to arm coaches and trainers with the intelligence necessary to make more informed decisions. Technology will never replace the power of a good relationship.”

Think a Few Plays Ahead

Kinduct is based in McDonough’s hometown of Halifax, Nova Scotia, which boasts five universities, strong government subsidies, a low cost of living, and, for Kinducts’s predominantly U.S.-based customers, a favorable currency exchange rate. Despite these advantages, Halifax isn’t widely known for its digital innovators. “We’ve got a huge chip on our shoulder,” says McDonough. “We want to prove that we’re just as capable of becoming a global success as companies elsewhere,” such as Silicon Valley or London.

The Kinduct platform can help athletes or medical patients improve their condition or performance.

Nevertheless, McDonough spends significant time in Silicon Valley meeting with investors and looking at potential U.S. expansion (Kinduct closed a US$9 million Series A investment led by Intel Capital in October). “There’s a huge benefit to growing in Nova Scotia,” he says, “but we also need to be in the epicenter of the tech space.”

McDonough has big ideas for Kinduct’s future, thanks to the explosion of health- and fitness-tracking devices. “We can pull all the data in and, when we see a negative pattern, provide the user with the exact roadmap they need to follow to improve their condition or performance,” he says. “That’s equally as useful to a professional football player or an Olympic athlete as it is to someone recovering from a knee replacement or living with type 2 diabetes.”

Kinduct has 16 projects underway to measure the impact of the platform in helping individuals manage conditions like peripheral vascular disease and cognitive decline. “We want to show how the platform can empower and engage patients,” says McDonough.

Go Big or Go Home

Meanwhile, however, McDonough intends “to dominate the sports space. That is our bubble wrap of credibility, and we can leverage that to do other things.”

Focus was never a strong suit for McDonough, who struggled with dyslexia and ADD as a kid. “Thank God for sport, which helped to channel my energy,” he says. But that wandering mind, he says, has also been an asset. “Like a lot of ADD sufferers, I have a lot of imagination,” he says. For balance, he’s hired a leadership team that keeps him grounded, and he has assembled a board of experienced business and technology leaders. “They have the institutional knowledge in how to scale,” he says.

McDonough is blunt: right now, he’d rather be innovative than profitable. “We’re in this to go big. That means carrying a burn rate, hiring aggressively, and investing in research,” he says. “We’re lucky enough to be in locker rooms with these teams and close to some of the best in the business in terms of medicine and training and data science. That’s helping us to produce our future roadmap.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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