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Top 100 iPad Rollouts By Enterprises & Schools [Chart Updated Aug 30, 2012]

Eric Lai

As part of my research into a forthcoming blog/map showing the largest iPad and other tablet deployments by schools and universities, I also updated my list of the largest publicly-known iPad deployments, including companies, governmental agencies, etc.

Notable additions include Rochester (MN) School District, Mansfield County Schools (Texas), Vancouver & CDI Colleges, Beaufort County (GA) schools, Farmington (MN) schools, Muncie (ID) Community Schools, Encinitas Union (CA) Schools, Hopkins (MN) schools, and many, many more.

Indeed, 62 out of my top 100 are K-12 schools.

My readers have probably seen some version of this list before, which I’ve updated every few months for the past year.

Besides the new schools on the list, the major differences with this version are:

a) I’ve expanded it from 50 to 100;

b) I’ve changed the way I’ve embedded the list, hopefully making it more attractive and readable.

If you want to copy and paste the below data but are having trouble, please visit the Google Spreadsheet.

Check out my entire list of iPad deployments here, and my Android list here.

Oh, and please send any missing deployments to me via ericyolai@gmail.com or via Twitter@ericylai.

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I may never make it to American Idol, but you can help me get to South By SouthWest 2013. Please vote for my panel about enterprise mobile apps here, thanks! http://spr.ly/6013TbE1  Deadline is August 31st, 2012!

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Eric Lai

About Eric Lai

Eric Lai previously worked in Enterprise Mobile Solutions Marketing at Sybase, an SAP company. His specialties include blogging, journalism, social media, marketing communications, content strategy and writing and editing.

Why New Technology Has An Adoption Problem

Danielle Beurteaux

When 3D printing became a practical reality, in the sense that the actual printers became more efficient, less expensive, and more accessible to the average consumer, there was an assumption that the consumer 3D printing market was going to take off. We’d all have printers at home printing…. what? Our clothes? Toys? Spare organs?

That has yet to happen. 3D printing company MakerBot just went through its second employee layoff this year, driven by a market that’s developing much slower than predicted.

That same thinking is in play with a somewhat more prosaic technology – digital wallets. Apple Pay was released this year, as was Samsung Pay. There’s also Google’s Android Pay. During an earnings call, Apple CEO Tim Cook said: “We are more confident than ever that 2015 will be the year of Apple Pay.” But that expectation has yet to be realized, at least vis-à-vis consumers.

Consumers aren’t using any of the digital wallets en masse. According to Bloomberg, payments made via mobile wallets – all of them – make up a mere 1% of retail purchases in the U.S. The reason is that consumers just don’t see a compelling reason to use them. There’s no real reward for them to change from SOP.

Both these instances highlight a problem with assumptions about mass adoption for new technology – just because it’s cool, interesting, and accessible doesn’t mean a market-worthy mass of people will use it.

Who is more likely to use mobile wallets? Emerging economies without a stable financial and banking systems. In those environments, digital payments present a more secure and quicker method for purchasing. These are the same areas where mobile adoption leapfrogged older technologies because there was a lack of telecommunications infrastructure, i.e. many never had a landline phone to begin with, and they went directly to mobile. The value-add already exists. (But there are also security issues, to which consumers are becoming more sensitive. A hack of Samsung’s U.S. subsidiary LoopPay network was uncovered five months post-hack. Although one was expert quoted as saying the hackers may not have been interested in selling consumer financial info but instead in tracking individuals.)

Here’s some interesting data and a good point made: mobile payments are most popular in situations where the buyer already has his or her phone in hand and the transaction is made even quicker than swiping plastic. For example, purchases made for London Transit rides are responsible for a good portion of the U.K.’s mobile payments.

Mass technology adoption is no longer driven simply by the release of a new product. There are too many products released constantly now, the market is too diverse, and the products often lack a true raison d’être.

Learn more about how creative and innovative companies are finding their customers. Read Compelling Shopping Moments: 4 Creative Ways Stores Connect With Their Customers.

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Mobile Marketing Continues To Explode

Daniel Newman

If your brand isn’t among those planning a significant spend on mobile marketing in 2016, you need to stop treating it like a fad and step up to meet your competition. Usage statistics show that today people live and work while on the move, and the astronomical rise of mobile ad spending proves it.

According to eMarketer, ad spending experienced triple-digit growth in 2013 and 2014. While it’s slowed in 2015, don’t let that fool you: Mobile ad spending was $19.2 billion in 2013, and eMarketer’s forecast for next year is $101.37 billion—51 percent of the digital market.

  1. Marketers follow consumer behavior, and consumers rely on their mobile devices. The latest findings from show that two-third of Americans are now smartphone owners. Around the world, there are two billion smartphone users and, particularly in developing regions, eMarketer notes “many consumers are accessing the internet mobile-first and mobile-only.”
  2. The number of mobile users has already surpassed the number of desktop users, as has the number of hours people spend on mobile Internet use, and business practices are changing as a result. Even Google has taken notice; earlier this year the search giant rolled out what many referred to as “Mobilegeddon”—an algorithm update that prioritizes mobile-optimized sites.

The implications are crystal clear: To ignore mobile is to ignore your customers. If your customers can’t connect with you via mobile—whether through an ad, social, or an optimized web experience—they’ll move to a competitor they can connect with.

Consumers prefer mobile — and so should you

Some people think mobile marketing has made things harder for marketers. In some ways, it has: It’s easy to make missteps in a constantly changing landscape.

At the same time, however, modern brands can now reach customers at any time of the day, wherever they are, as more than 90 percent of users now have a mobile device within arm’s reach 24/7. This has changed marketing, allowing brands to build better and more personalized connections with their fans.

  • With that extra nudge from Google, beating your competition and showing up in search by having a website optimized for devices of any size is essential.
  • Search engine optimization (SEO) helps people find you online; SEO integration for mobile is even more personalized, hyper local, and targeted to an individual searcher.
  • In-app advertisements put your brand in front of an engaged audience.
  • Push messages keep customers “in the know” about offers, discounts, opportunities for loyalty points, and so much more.

And don’t forget about the power of apps, whose usage takes up 85 percent of the total time consumers spend on their smartphones. Brands like Nike and Starbucks are excellent examples of how to leverage the power of being carried around in someone’s pocket.

Personal computers have never been able to offer such a targeted level of reach. We’ve come to a point where marketing without mobile isn’t really marketing at all.

Mobile marketing tools are on the upswing too

As more mobile-empowered consumers themselves from their desks to the street, the rapid rise of mobile shows no signs of slowing down. This is driving more investment into mobile marketing solutions and programs.

According to VentureBeat’s Mobile Success Landscape, mobile engagement—which includes mobile marketing automation—is second only to app analytics in terms of investment. Mobile marketing has become a universe unto itself, one that businesses are eager to measure more effectively.

Every day, mobile marketing is becoming ever more critical for businesses. Brands that fail to incorporate mobile into their ad, content, and social campaigns will be left wondering where their customers have gone.

 

For more content like this, follow Samsung Business on InsightsTwitterLinkedIn , YouTube and SlideShare

The post Mobile Marketing Continues to Explode appeared first on Millennial CEO.

photo credit: Samsung Galaxy S3 via photopin (license)

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Daniel Newman

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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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