Archive for the ‘Bidness, and Other Current Economic Realities’ Category

The internet is a dangerous place.  You never know who’s watching you.  If you’re in a coffee shop using the public Wi-Fi router there, you’re wide open… to hackers, Google, your internet provider or the person sitting next to you.  A VPN (virtual private network) acts like a curtain on your room.  They’re mostly open with daylight streaming in, but it’s nice sometimes being able to close them, when you need the privacy – or are simply and justifiably worried about unwarranted hackers and pranksters.

VPNs have become a popular tool today.  Once the domain of businesses seeking security and privacy, today it’s all too easy for hackers to infiltrate all of your devices, read your traffic or maliciously insert bugs, phishing efforts or other malware.  When private matters are involved like health, finance and personal communications, a VPN can be the best and cheapest method of maintaining your peace of mind and security.  By turning it on first before browsing, you’re far better protected.

Here’s a great explanation provided by Personal Technology columnist David Pierce of how a VPN works in simple terms:

“A VPN creates a ‘tunnel’ between your computer and the service you’re connecting to, using its software to make your connection direct and private.  Once this tunnel is established, a VPN encrypts all of the data it sends to you and receives from you through the tunnel.  Even if hackers decide to snoop on your data, they wouldn’t see anything they’d understand.

“Because your VPN provider is actually accessing the internet for you, the sites you visit won’t receive accurate identifying data like your location or IP address.”

You may be in California, but if your VPN provider is in New Jersey, your IP address jumps from California to New Jersey, and you’re not likely to be found.

While using a VPN does not excuse otherwise practicing safe computing, like strong passwords and multifactor authentication, it does add a whole new level of internet safety.  Common sense ought to take care of most of the rest (e.g., not using your credit card on shady sites, that sort of thing…)

There are a number of inexpensive VPNs available to any user today.  They typically charge between $3 and $12 a month.  Most claim to store no data, though they may actually store a little, and often not directly attributable to you personally.  Some names suggested by Mr. Pierce include the Hotspot Shield Elite from Anchor Free; Private Internet Access from London Trust Media; NordVPN; and Freedome VPN from F-Secure.  Many of these are part of a broader suite of security products that may include a password manager or other useful tools.

Today, according to a survey by Wombat Security, about two-thirds of users use a VPN on a corporate and/or personal device.  The rest either don’t use one, or (about 20%) don’t know what it is.  That last group might want to read up as VPNs today work on phones, tablets and PCs all across the spectrum.  And their added measure of security just might make them rest easier.


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According to a career website focused on cybersecurity called CyberSeek, there are currently over 300,000 unfilled cybersecurity jobs in the U.S.  A separate 2017 study forecast a global cybersecurity worker shortage of 1.8 million by 2022.

Current proposals being floated in policy circles these days (according to The Wall Street Journal) focus on two themes: attract more motivated people, and train them faster.

According to the chair of Indiana University’s cybersecurity program, the core issue “is a lack of a focused talent pipeline.” Scott Shackleford of I.U. thus proposes a “Cybersecurity Peace Corps” which he suggests could place workers with nonprofits and other organizations who couldn’t otherwise afford them, and pay their salaries and training.  Unfortunately, Professor Shackleford’s idea would require an act of Congress.  But, he says, it doesn’t have to be a national initiative.  “You could easily see a state taking this on and experimenting” with corporate partners to serve “laudable causes,” he notes.

A former senior Defense Dept. official has similarly proposed a kind of cyber ROTC program, modeled after the Reserve Officer Training Corps long popular on college campuses, in which prospective officers go to college tuition-free to learn cyber skills in return for some years of military service.  With a cyber ROTC, a tuition-free exchange for a few years of service in the public sector could bring in young people who might not otherwise be able to afford an opportunity to gain entry into the world of cybersecurity or computer science jobs.

A bill was introduced in Congress last year that aims to establish tax breaks for employers who develop training in cybersecurity jobs.  Called the New Collar Jobs Act, it could enlarge the workforce by increasing available training by canceling up to $25,000 in college loan debt for those who hold cyber jobs in an “economically distressed area for one year.”

Companies today are increasingly setting up their own training programs, many of which require skills and certifications not taught in college.  The same proposed act would provide tax credits of up to $5,000 per employee to support such private training initiatives, according to Janaki Chadha, a Journal reporter.

No word yet on the status of that bill.  But there are hundreds of thousands of high-tech jobs on the line just waiting for such new economy initiatives to help provide funding for what promises to be a level of investment return that would be many times more than the modest investment required.

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In a recent article entitled “The Human Promise of the AI Revolution,” the former President of Google China and current CEO of Sinovation Ventures, Kai-Fu Lee, says that artificial intelligence (AI) will radically disrupt the world of work, but the right policy choices can make it a force for a more compassionate social contract.

By now it’s become clear that AI is going to be a disruptive force.  A lot of jobs in all colors of collars may not be safe (though some are, at least for now).  The whole world of autonomous driving is one quick example, where increasingly AI and software are starting to have a big influence on how we get around.  This is only the beginning.

Some are optimistic about AI’s promise of newer, better jobs that challenge our ingenuity and lead to bold new industries.  Others, notably Tesla CEO Elon Musk among others, warn of dire and ominous consequences.  Lee notes that the application of existing technology to new problems will hit many white-collar professionals just as hard as it hits blue-collar factory workers.

Lee is careful to point out AI’s strengths and weaknesses in order to propose those jobs that would be most affected.  For instance, while AI is “great at optimizing for a highly narrow objective, it is unable to choose its own goals or think creatively.”  AI may be superhuman in numbers and data, but it lacks social skills or empathy.  Hence driving a car or diagnosing diseases across massive datasets that are incomprehensible to mere mortals may play to AI’s strengths — not to mention fast food cooks and insurance adjusters — but home care nurses, most attorneys, hairstylists and CEOs are probably safe for the foreseeable future.

Despite the challenges, Lee remains hopeful, as he sees an opportunity for us “to redirect our energy as a society to more human pursuits: to taking care of each other and our communities.  While you can read his full thoughts in his book “AI Superpowers: China, Silicon Valley and the New World Order,” or in The Wall Street Journal’s Sept 15th “Review” section, the gist of his thinking goes like this…

While some propose a Universal Basic Income (or UBI) as a possible cure to the massive job dislocations many see coming, Lee thinks otherwise.  While UBI would provide a subsistence level of income for those so displaced (experiments with UBI are currently underway in several places around the world, with less than robust reviews it would appear), the very concept, says Lee, lacks the pride and dignity that work focused on enhancing our communities would provide instead.

So why not, in lieu of a UBI, create jobs (and pay people) instead?  Maybe a different form of available guaranteed income?  Lee proposes a kind of stipend, or what he calls the Social Investment Stipend, for those who devote themselves to three categories of labor: care work, community service and education.

Lee suggests these activities could form the core for a new social contract, and jobs would run the gamut from parenting and home schooling to assisting aging parents, or focusing on the efforts of non-profit and volunteer groups.  Service efforts could include leading after-school programs, guiding tours at parks, or collecting oral histories from elders.  Education activities could range from professional training for the jobs of the AI age to taking classes to turn a hobby into a career.

Lee admits many difficult questions remain.  But at least he’s asking them… posing suggestions for the new work landscape, and trying to fashion a viable solution from the thorny issue of job displacement that may be harboring under the guise of technological advancement, modern times and the new age of artificial intelligence.

In other words, at least he’s trying to get us to think, and talk, about it.

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Some practical advice today, courtesy of Steven Johnson, author of a new book titled “Farsighted,” which explores our human decision making processes, and the biases that often entangle us.  Fortunately, as Johnson finds, there are tools that can aid us in making up our mindsm as David Shaywitz succinctly put it recently in a Wall Street Journal article of Sept. 12, 2018.

One key strategy researchers have found is to systematically widen our thinking to define our options as broadly as possible, “seeking a full-spectrum appraisal of the state of things and a comprehensive list of potential choices.”  Then, winnow down the alternatives by playing out multiple scenarios, exploring what can go wrong.

This is exactly what planners did in 2011 when plotting to capture Osama bin Laden.  The planning was so thorough, so extensive, and considered so many possible options, failure points and contingencies, that it is said that the only item the Seal team neglected to think of was to bring along a ruler to confirm bin Laden’s height after the raid.  President Obama later gave chief planner Gen. William McRaven a tape measure, tongue in check, along with the plaque that commended him for his planning skills.

Decades of research in behavioral sciences show that the human mind is hampered by frequent cognitive biases that often lead us “to misunderstand the past, misconstrue the present and badly foresee the future,” according the Shaywitz.  But as Johnson notes, we shouldn’t despair.  While it may be difficult to rein in our intuition, there are tools than can help improve our decision-making abilities.

Johnson says that when confronted with real-life problems or choices, we tend to frame them in a narrow fashion.  Instead, he suggests, we should engage in an expansive mapping exercise, with participation from the broadest and most diverse group we can arrange.  Fringe ideas are welcome, as are suggestions that would not otherwise occur to those following the party line.  (The M.I.T. Media Lab makes a specialty of just this kind of thinking, but that’s an article perhaps for another day.)

But viewpoint diversity alone isn’t enough.  Groups often focus on “shared” information, so it’s important to design a process that exposes “unshared information” too, according to legal scholar Cass Sunstein.  Meet individually with decision stakeholders to get outlier input.  Also, notes Sunstein, most organizations don’t contemplate more than a single option when deciding.  There is a natural tendency he notes to move to a single framing of a decision.  To resist it, he suggests “considering what might be done if the presumptive path forward were suddenly blocked.”  (Much like, it’s assumed the bin Laden team must have done.)

Among Johnson’s additional strategies for predicting outcomes with precision: scenario planning, where you systematically explore different future versions and simulations — like commandos practicing a raid, or weather forecasters using math models.  Uncertainty cannot be fully winnowed out of any complex system, but scenarios and simulations can help you better prepare for unexpected surprises.

When it’s decision time, you can weigh the relative importance of different goals, or take a bad-outcome approach that examines worst-case scenarios.  But once all the information and data capturing are on the table, one approach may be an instinctive one after all: “Give your mind the free time to mull it over.  Go for long walks, linger in the shower longer, and let your mind wander.”

Fundamentally, Johnson notes, “choices concern competing narratives, and we’re lively to make better choices if we have richer stories, with more fleshed-out characters, a more nuanced understanding of motives and a deeper appreciation of how decisions are likely to reverberate and resound.”


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In our prior post we highlighted the comments of two well-known hedge fund investors as they describe the shift from a world often dominated by software to the new world of the model-driven business, wherein models power the key decisions in business processes, creating revenue streams and improved cost efficiencies.

The co-authors, hedge fund CEO Steven Cohen  and venture capitalist Matthew Granade, both of Point72 Ventures, and writing in August in The Wall Street Journal’s Opinion section, conclude their essay by pointing out five key implications of these models for business in the future, which we’ll summarize below.

First, businesses will be increasingly valued based on the completeness, not just the quantity, of the data they create.  Companies like Amazon and Tencent (cited in our prior post) don’t just collect massive reams of data about their customers, they know how to act on it.  It’s not just breadth of knowledge in other words, it’s also a ‘closed loop’ in which each recommendation a model makes, based on that user data, is captured and used to improve the model going forward.

Second, the goal is a ‘flywheel,’ they state, or a virtuous circle: Models improve products, products get used more, the new data improves the product (and the model) even more.  It’s a nearly frictionless process of continuous improvement.  Pretty close to a business holy grail if ever there was one.

Third, as incumbents (i.e., entrenched, major, competitive, winning companies) they will be “more potent competitors in this battle relative to their role in the software era,” insofar as they will have a meaningful advantage (operationally and profit-wise) this time around.   They have the trove of data competitors don’t, and they’ve learned how to monetize it.  And they keep getting better at it.

Fourth, just as the best companies have built deep organizational capabilities around managing people, tools, technology and capital, the same will now happen for models.  Just as software has become an ‘agile’ process in its delivery, the data-driven companies now on the rise are creating a new discipline of model management that affect the same domains: people, tools, technology and capital.  The models can now deliver, and that creates a critical competitive edge.

Fifth, companies will face new ethical and compliance challenges, according to Cohen and Granade.  With data becoming more comprehensive and important, consumer concerns over use and abuse are bound to rise – in fact, they already are.  Facebook lost over $100 billion in market cap in a couple of days in July because investors became concerned about its data assets in the face of increasing regulation.  That’s not likely to abate, and examples will only multiply.

Still, while software continues to “eat the world” (prior post), yesterday’s advantage becomes, in the authors’ words, “today’s table stakes.”  In the hunt for competitive advantage, the model-driven business will become ubiquitous.  For the average investor, software has been a great place to make money since Marc Andreessen’s famous “Why Software is Eating the World” essay seven year ago.  But in the next seven, the serious money is on the model-driven business.


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In a famous essay in 2011 Netscape founder and venture capitalist Marc Andreessen notoriously wrote about “Why Software is Eating the World.”  He was right.  Companies he identified including Amazon, Netflix, Spotify and others did just that – they used software to eat their industries.  Today, newer companies are repeating the feat, including Airbnb, Stripe, Uber and others.  All are digging in.

It is said that today, all companies are software companies.  Or they had better be.  Many have adopted software to maintain or extend their competitive dominance, even in industries as arcane as pizza to name just one, where Domino’s has achieved a dominant position thanks to its software focus.

A recent article co-written by hedge fund CEO Steven Cohen and venture capitalist Matthew Granade, both of Point72 Ventures, and writing in August in The Wall Street Journal’s Opinion section, begs the question: What’s next?  Their answer?  Models.

This new paradigm is defined as a shift from a world often dominated by software to one driven by ‘models,’ which power the key decisions in business processes, creating revenue streams and improved cost efficiencies.  It requires a mechanism (usually software-based) to collect data, processes to create the models from the data, the models themselves, and finally a mechanism to deliver or act upon the suggestions from those models.  To be specific, quoting the authors on the latest paradigm shift:

“These companies structure their business processes to put continuously learning models, built on ‘closed loop’ data, at the center of what they do.  When built right, they create a reinforcing cycle: Their products get better, allowing them to collect more data, which allows them to build better models, making their products better, and onward.  These are model-driven businesses… being created across a range of industries.”

While there is plenty of hype about big data and AI, the models, they state, “are the source of the real power behind these tools.”  They go on to say… “A model is a decision framework in which the logic is derived by algorithm from data, rather than explicitly programmed by a developer or implicitly conveyed via a person’s intuition.”

Get it?  It’s the data-driven model’s ability to learn from itself and its own mistakes – at a rate much faster than mere humans can do – that sets it apart as an increasingly rapid prototyping tool for building the better business.  Humans, it seems, need not apply.

A Chinese company called Tencent provides a brilliant example.  They have customer data across social media, payments, gaming, messaging, media and music, and information on hundreds of millions of people, all of which they put into the hands of thousands of data scientists in order to make their products better.

That unique data helps Tencent to power a model factory “that constantly improves user experience and increases profitability – attracting more users, further improving the models and profitability.”  That’s a model driven business.

Closer to home, Amazon used software to separate itself from physical competitors but it was their models that helped them pull away from other e-commerce companies, Cohen and Granade point out.  By 2013, over a third of Amazon’s online revenue came from its recommendations, the result of model-driven data usage, “and its models have never stopped improving,” as Bezos & Co. continue to find myriad ways to use machine-learning models.

The authors go on to extol the model-based leveraging changes now taking place across the business spectrum.  Key industries include agriculture, services and logistics.  The implications are vast, and Cohen and Granade sum it up by describing five key implications for the future of business … and we’ll cover those five in our concluding post.  Stay tuned…




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Customer satisfaction can be determined in any number of ways, from personal visits and other direct encounters with customers to surveys and data analytics.  Two key metrics include overall customer satisfaction rating, and a tactic used by many companies today called the net promoter score.  It measures strength of loyalty and a willingness to recommend you.

Net promoters scores are typically built on the offering of a single question: “On a scale of 0 to 10, how likely are you to recommend [our company, our product, our service] to a friend or colleague?”

Based on this simple 11 point scale, scores are divided into detractors (those giving a score of 6 or less); Passives, who score you at 7 or 8; or net Promoters, who answered with a 9 or 10.

The detractors have the potential for further reputational damage, and when recognized provide an important opportunity to learn more, understand, correct a problem (and thus ‘save’ a customer) or engage them in meaningful dialogue aimed at solving the problem and improving your score.

The passives are somewhat satisfied, but are vulnerable to switching to another provider or product.  They’re not likely to say anything bad about your product or firm, but they’re also not enthusiastic enough about your products (or you) to actively promote either.

Promoters, those who scored a 9 or 10, are your sweet spot.  They love your company’s products, services or people, and will often recommend them enthusiastically to others.  They’re worth their weight in gold, of course.

In addition to ‘top-level’ metrics that you can find inside your ERP system, you can consider determining metrics for each stage of a customer’s journey throughout your life with them.  Metrics that include sales trends, buying history, preferences, results of cust-sat surveys and overall breadth of product support for your products and services can be combined and investigated at various timelines along the way, at least for a random sampling of clients.  Just as an investigative exercise alone, the results can be enlightening, and most every client is capable of surprising us (for better and for worse) with their responses, once engaged.

It takes a bit of courage sometimes to work up to asking the net promoter question, or to survey your customers on their more specific levels of trust and satisfaction.  But the knowledge gained and insights provided actually make it easier for you to improve your offerings and increase customer retention almost immediately.  Viewed in that light, why wouldn’t  you do it?


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