Author Archives: ghotchkiss

A Tale of Two Research Philosophies

First published December 19, 2013 in Mediapost’s Search Insider

They only sit about five miles apart physically. One’s in Palo Alto, the other’s in Mountain View. But when it comes to how R&D is integrated into an organization’s strategy, there is significantly more distance between Xerox’s PARC and Google.

Xerox Alto computer

Xerox Alto computer

I recently visited both locations on the same day. PARC, of course, is the legendary research wing that created the graphical user interface, the personal computer, object oriented programming, the mouse, Ethernet and the laser printer. It was at PARC that Steve Jobs saw the interface that would eventually form the OS foundation for the Macintosh. Every time we touch the technology that today we take for granted, we should give thanks to the many people who have called the unassuming campus on Coyote Hill Road home.

But in 1969, when PARC was first created, there was a different attitude towards R&D. Research required isolation and distance from the regular business rhythms of the mother ship. Xerox could not have put more distance between its head office, in Rochester, N.Y., and its new research arm, 3,000 miles away. When it came to innovation, the choice of location was fortuitous. PARC, together with HP and other Silicon Valley pioneers, tapped into the stream of talent that was coming out of Stanford. In fact, PARC is located on land leased from Stanford. It soon became an innovation hotbed, thanks to the visionary leadership of Bob Taylor, who headed up the Computer Science division. But Xerox’s track record of bringing its own innovations to the market was dismal. As great as the physical distance was between PARC and the executive wing of Xerox in upstate New York, the philosophical distance was several times greater.

Google’s research efforts, under the leadership of Peter Norvig, is taking a much different direction, likely due to lessons learned from PARC and others.  Research is embedded in the ever-expanding Google campus that currently sprawls along Amphitheatre Parkway and Charleston Road. There is a free flow of traffic and communication between current product engineering teams (many riding brightly colored Google bikes) and those working on longer-term projects. The distance between “today” and “tomorrow” is minimized at every opportunity.

Norvig commented on this in a recent interview with me:

We don’t have a separate research entity whose job is to be isolated from the rest of the company and think about the future. Rather, everybody’s job, regardless of their job title, is to make our products better or invent a new product. So the distinction between being a researcher versus an engineer is not how academic you are, it’s not how forward-thinking you are  — whether you’re looking at this year or next year or the year after. It’s more in terms of the area that you work in. If you work in core search or in core distributed computer systems, then your title’s going to be software engineer, even if you’re a Nobel Prize-winning professor.

Google has taken a hybrid approach to research, in which even long-term projects are developed at production scale, minimizing the risk of projects failing during the technology transfer phase. Norvig touched on this in a recent article:

Elaborate research prototypes are rarely created, since their development delays the launch of improved end-user services. Typically, a single team iteratively explores fundamental research ideas, develops and maintains the software, and helps operate the resulting Google services — all driven by real-world experience and concrete data. This long-term engagement serves to eliminate most risk to technology transfer from research to engineering.

This was exactly the trap that PARC ran into, when some of the most innovative advances in the history of computing failed to significantly contribute to Xerox’s bottom line.  Google has thrown the doors open for internal research teams to access the full power of complete data sets and production scale systems while espousing the practice of agile development. The goal is to ensure that all innovation that happens at Google is not too far removed from the goal of either diversifying Google’s revenue stream with new products, or contributing to existing ones.

The Emerging Data Ecosystem

First published December 13, 2013 in Mediapost’s Search Insider

big-dataData is ubiquitous, and that is true pretty much everywhere. It was certainly true at the Search Insider Summit, where every panel and presentation talked about data. And not just any data — this was “Big Data.”  But what exactly is Big Data — just more data? Or is there a fundamental shift happening here?

I believe there is. When I think about Big Data, I think about an emerging data ecosystem, where the explosion of available data will exponentially increase the complexity of the ecosystem. This is not just more data, but a different environment that will require different strategies.

Typically, the data we currently use is either first-party data — the data that emerges as part of our business process — or structured third-party data, available from a rapidly growing number of data vendors. This is probably what most people think of when they think of Big Data. But I don’t consider data in this form a departure from the data we’re used to using. There’s more of it, true, but the process is already identified. It just needs to be scaled to deal with increased volumes.

Let me use one example from the recent Search Insider Summit. The Weather Company has recently launched a new division called Weather FX, aimed at taking the vast amount of weather data it has to create predictive models to help companies add weather-based variables to their own data sets. For example, ad targeting can now be weather-sensitive, ramping up campaigns and changing messaging based on predicted changes in weather patterns. While pretty impressive, this is a relatively straightforward use of data. The data feeds are well structured and have been “predigested” by Weather FX to make them easy to implement.

Big Data, at least in my interpretation, is a different beast altogether. Here, data is messy, often unstructured, hard to find and in raw form. To further complicate matters, it lives in disparate siloes that often have no market-facing interface. T It’s an organic ecosystem that bears more than a passing similarity to how we think of natural resources. This data needs to be identified, nurtured and harvested (or mined, if you’d prefer).

It’s this data that will lead to a true view of Big Data, a world of vast data nodes that require significant development before they can be used. Think of how the world was a century and a half ago, when a lot of raw stuff — wood, minerals, water, crops, livestock — lay scattered about our planet. At the time, there was little in the way of established manufacturing and distribution chains that transformed that raw stuff into consumable products. Over time, the chain emerged, but a lot of logistical challenges had to be addressed along the way. The same is true, I believe, for data.

But there’s another challenge with Big Data: It’s not always clear how to use it. It needs a framework. You can’t dump a ton of various metals and a couple barrels of oil into a big black box, shake it and expect a Ford Focus to drop out. You need to have a pretty clear idea of what your expected outcome is. And you need to have a long chain that moves your raw material towards your end product. In the early days of creating physical goods, these chains were often verticalized within a single organization, but as the ecosystem evolved, the markets became more horizontal. I would expect the same pattern to emerge in the data ecosystem.

If you create a conceptual framework within which to use data, you can determine which data is required and how that data will be used. You can pick your data sources, and identify the gaps and resource as required to address those gaps. Often, because we’re in the earliest stages of this process, we will need to explore, guess and iteratively test before the data will provide value.

This definition of Big Data requires new rules and strategies. It requires a commitment to mining raw data and integrating it in useful ways. It will mean dynamically adapting to the continuing data explosion. It will require blood, sweat and tears. This is not a “plug and play” exercise. When I think of Big Data, that’s what I think about.

360 Degrees of Seperation

First published December 5, 2013 in Mediapost’s Search Insider

IMT_iconsIn the past two decades or so, a lot of marketers talked about gaining a 360-degree view of their customers.  I’m not exactly sure what this means, so I looked it up.  Apparently, for most marketers, it means having a comprehensive record of every touch point a customer has had with a company. Originally, it was the promise of CRM vendors, where anyone in an organization, at any time, can pull up a complete customer history.

So far, so good.

But like many phrases, it’s been appropriated by marketers and its meaning has become blurred. Today, it’s bandied about in marketing meetings, where everyone nods knowingly, confident in the fact that they are firmly ensconced in the customer’s cranium and have all things completely under control. “We have a 360-degree view of our customers,” the marketing manager beams, and woe to anyone that dares question it.

But there are no standard criteria that you have to meet before you use the term. There is no rubber-meets-the-road threshold you have to climb over. No one knows exactly what the hell it means. It sure sounds good, though!

If a company is truly striving to build as complete a picture of their customers as possible, they probably define 360 degrees as the total scope of a customer’s interaction with their company. This would follow the original CRM definition. In marketing terms, it would mean every marketing touch point and would hopefully extend through the customer’s entire relationship with that company. This would be 360-degrees as defined by Big Data.

But is it actually 360 degrees? If we envision this as a Venn diagram, we have one 360-degree sphere representing the mental model of customers, including all the things they care about. We have another 360-degree sphere representing the footprint of the company and all the things they do. What we’re actually looking at then, even in an ideal world, is where those two spheres intersect. At best, we’re looking at a relatively small chunk of each sphere.

So let’s flip this idea on its head. What if we redefine 360 degrees as understanding the customer’s decision space? I call this the Buyersphere. The traditional view of 360 degrees is from the inside looking out, from the company’s perspective. The Buyersphere moves the perspective to that of the customer, looking from the outside in. It expands the scope to include the events that lead to consideration, the competitive comparisons, the balancing of buying factors, interactions with all potential candidates and the branches of the buying path itself.  What if you decide to become the best at mapping that mental space?  I still wouldn’t call it a 360-degree view, but it would be a view that very few of your competitors would have.

One of the things that I believe is holding Big Data back is that we don’t have a frame within which to use Big Data. Peter Norvig, chief researcher for Google, outlined 17 warning signs in experimental design and interpretation. One was lack of a specific hypothesis, and the other was a lack of a theory. You need a conceptual frame from which to construct a theory, and then, from that theory, you can decide on a specific hypothesis for validation. It’s this construct that helps you separate signal from noise. Without the construct, you’re relying on serendipity to identify meaningful patterns, and we humans have a nasty tendency to mistake noise for patterns.

If we look at opportunities for establishing a competitive advantage, redefining what we mean by understanding our customers is a pretty compelling one. This is a construct that can provide a robust and testable space within which to use Big Data and other, more qualitative, approaches. It’s relatively doable for any organization to consolidate its data to provide a fairly comprehensive “inside-out” view of customer’s touch points. Essentially, it’s a logistical exercise. I won’t say it’s easy, but it is doable.  But if we set our goal a little differently, working to achieve a true “outside-in” view of our company, that sets the bar substantially higher.

360 degrees? Maybe not. But it’s a much broader view than most marketers have.

Evolutionary Hotspots in Marketing

First published November 21, 2013 in Mediapost’s Search Insider

paramoscene1_7in

The Páramos Ecosystem

The Páramos are remarkable places: grasslands that sit above the tree lines in the Andes, some 10,000 feet above sea level. What makes them remarkable are the things that grow and live there — like Espeletia uribei,which looks like a huge palm tree, but is actually an overgrown member of the daisy family.

The Páramos just happen to be the place on earth where evolution happens the fastest.  There are other places where species evolve quickly, including Darwin’s Galápagos Islands, but scientists believe the Páramos are the hottest of the evolutionary hot spots.

The reason for this supercharged speciation is the climate, which makes them a very tough place to call home.  They’re located at the equator, so they get sunshine year round. But the elevation introduces harsh temperatures and extreme ultraviolet exposure. Also, the weather can change in a heartbeat. A few minutes can mark the difference between sunshine, mist and full-on storms.  This constant adaptive stress has resulted in biodiversity not seen anywhere else on the planet.

In biology, evolution is measured by the rate of mutation. In the business world, mutation equates to innovation. A new idea introduces a wild card into the competitive environment, just as a genetic mutation introduces a wild card into nature. It disrupts the status quo, either positively or negatively. That’s why it’s important for organizations to embrace failure. Openness to error encourages innovation, driving the competitive evolution of the company. Successful innovations can be game-changers, as long as you create a framework to identify unsuccessful innovations before they do irreparable damage.

So if we accept that corporate evolution is a good thing, and we want to increase our mutation/innovation rate, then it makes sense to seek our own organizational “Páramos.” These will be departments or divisions where volatility is the norm, rather than the exception. Stability is the enemy of innovation. Typically, these will be areas that require rapid reaction to external forces and adaption to new environmental factors. Much as we like to mythologize the lone genius toiling away in an ivory tower or R&D lab, the history of innovation shows that it most often comes from far messier, more organic sources.

In the Páramos, it’s the harsh, unpredictable climate that drives evolution. In a company, it’s the instability of the competitive marketplace that drives the forces of innovation. So it makes sense that the hotspots will be those areas of the organization that have the most exposure to that marketplace. Front-line touch points with customers, head to head contact with competitors and real world usage of your products or services are the externalities you’ll be looking for. That makes sales, marketing and customer service prime candidates for becoming your own Páramos.

The challenge is to enable innovation at this level. Typically, innovation in an organization is constrained (and unfortunately, often choked to death) by bureaucratic frameworks that build in “top-down” governance from executives who are traditionally miles away from the “Páramos” in the org chart. This is exactly the wrong approach. Mechanisms should be developed to encourage “bottom-up” innovation in these identified hotspots, with appropriate guidelines for identifying successful opportunities as quickly as possible, allowing organizations to fast-track the winners and cut their losses on the losers. These hotspots can become the strategic radar of the organization.

Darwin’s “dangerous idea” has completely changed biology. Currently, it’s causing everyone from psychologists to economists to rethink their respective fields. In the future, don’t be surprised if it has a similar impact on marketing and corporate strategy.

Google’s Etymological Dream Come True

First published November 14, 2013 in Mediapost’s Search Insider

Yesterday’s Search Insider column caught my eye. Aaron Goldman explained how search ads were the original native ads. He also explained why native ads work. This is backed up by research we did about 5 years ago, showing how contextual relevance substantially boosted ad effectiveness (but not, ironically, ad awareness). I did a fairly long blog post on the concept of “aligned” intent, if you really want to roll up your sleeves and dive in.

The funny thing was, I was struck by the use of the word “native” itself. For some reason, the use of the term in today’s more politically charged world struck a note of immediate uneasiness. On a gut level, it reminded me of the insensitivity of Daniel Snyder, owner of the Washington Redskins. There’s nothing immoral about the term itself, but it is currently tied to an emotionally charged issue.

As I often do, I decided to check the etymological roots of “native” and immediately noticed something different on the Google search page.  There, at the top, was an etymological time line, showing the root of “native” is the Latin “nasci” – meaning born. So, it was entirely appropriate, given Aaron’s assertion that “native” advertising was “born” on the search page. But it was at the bottom, where a downwards arrow promised “more,” that I hit etymological pay dirt.

Google showed me the typical dictionary entries, but at the bottom, it gave me a chart from it’s nGram viewer showing usage of “native” in books and publications over the past 200 years. Interestingly, the term has been in slow decline over the past 200 hundred years, with a bit of a resurgence over the last 25 years. When I clicked on the graph it broke it down further, showing that small-n “native” has been used less and less, but big-N “Native” took a jump in popularity in the mid-80’s, accounting for the mild bump.

Google’s nGram isn’t new, but its capabilities have been recently beefed up, providing a fascinating visual tool for us “wordies” out there. With it, you can plot the popularity of words over 500 years in a body of over 5 million books. For example, a blog post at Informationisbeautiful.net shows several fascinating word trend charts in the English corpus, including drug trends (cocaine was a popular topic in Victorian times, slowed down in the 20’s and exploded again in the 80’s), the battle of religion vs science (the popularity cross over was in 1930, but the trend has reversed and we’re heading for another one) and interest in sex vs. marriage (sex was barely mentioned prior to 1800, stayed relatively constant until 1910 and grew dramatically in the 70s, but lately it’s dropped off a cliff. Marriage has had a spikier history but has remained fairly constant in the last 200 years.)

I tried a few charts of my own. Since 1885, “Evolution” has beaten “Creation,” but it took a noticeable drop during the 30’s. Since 1960 both have been on the rise.  In1980, Apple got off to an initial head start, but Microsoft passed it in 1992, never to look back (although it’s had a precipitous decline since 2000.)  Perhaps the most interesting chart is comparing “radio”, “television” and “internet” since 1900. Radio started growing in the 20’s and hit its popularity peak around 1945, but the cross-over with television would take another 40 years (about 1982.) Television would only enjoy a brief period of dominance. In 1990, the meteoric rise of the Internet started and it surpassed both radio and television around 1997.

tvradiointernet

My final chart was to see how Google fared in it’s own tool. Not surprisingly, Google has dominated the search space since 2001, and done so quite handily. Currently, it’s 6 times more popular than its rivals, Yahoo and Bing.  One caveat here though – Bing’s popularity started to climb in 1830, so I think they’re talking about either the cherry, Chinese people named Bing or a German company that used to make kitchen utensils.  Either that, or Microsoft has had their search engine in development a lot longer than anyone guessed.

googleyahoobing

Yahoo Under the Mayer Regime

First published November 7, 2013 in Mediapost’s Search Insider

marissa-mayer-7882_cnet100_620x433OK, it has a new logo. The mail interface has been redesigned. But according to a recent New York Timespiece, Yahoo still doesn’t know what it wants to be when it grows up. Marissa Mayer seems to be busy, with a robust hiring spree, eight new acquisitions, 15 new product updates, a nice 20% bump in traffic and a stock price that’s been consistently heading north. But all this activity hasn’t seemed to coalesce into a discernible strategy — from the outside, anyway.

It’s probably because Mayer is busy rebuilding the guts of the organization. Cultures are notoriously difficult things to change. In any organization where a major change in direction is required, you will have to deal with several layers of inertia — and, even more challenging, momentum heading the wrong way.  In the blog post, design guru Don Norman agrees, ““The major changes she has made are not what the logo looks like or a new Yahoo Mail. The major changes are what the company looks like internally. She’s revitalizing the inside of the company, and what everyone sees on the surface are just little ripples.”

To be fair, Yahoo has been an organization lacking a clear direction for a long, long time. I remember speaking at the Sunnyvale campus years ago, when Yahoo was still being remade into a media property, under the direction of Terry Semel. There were entire departments (including the core search team) that felt cut adrift. Since then, the strategic direction of Yahoo has resembled that of a Roomba vacuum, plowing forward until it senses an obstacle, then heading off in an entirely new direction.

What was interesting about the recent Times post was the marked contrast to the rumors and kvetching coming from Mayer’s old digs: Google. There, the big news seems to be the ultra-secret party barge anchored in San Francisco bay. And a Quora thread entitled “What’s the Worst Part about Working at Google?” paints a picture of a frat house that has yet to wake up and realize the party’s over:

  • Overqualified people working at menial jobs.
  • Frustration at not being able to contribute anything meaningful in an increasingly bureaucratic environment.
  • Engineers with egos outstripping their skills.
  • Bottlenecks preventing promotion,
  • A permanent “party” atmosphere that makes it difficult to get any actual work done.

But perhaps the most telling comment came from someone who spent seven years at Google, who said that all the meaningful innovation comes from an exceedingly small group, headed by Larry and Sergey. The rest of the Googlers are just along for the ride:

Here’s something to ponder.  The only meaningful organic products to come out of Google were Search and then AdSense.  (Android — awesome, purchased.  YouTube — awesome, purchased, etc. Larry and/or Sergey were obviously intimately involved in both.  Maps – awesome, purchased. Google Plus is a flop for all non-Googlers globally, Chrome browser is great, but no direct monetization (indirectly protects search), the world has passed the Chrome OS by… etc. ) Fast-forward 14 years, and the next big thing from Google, I bet, will be Google Glass, and guess who PMd it.  Sergey Brin.  Tiny number of wave creators, huge number of surfers.

So we have Google, still surfing a wave that started 15 years ago, and Yahoo struggling to get in position to catch the next one. For both, the challenge is a fundamental one: How do you effect change in a massive organization and get thousands of employees contributing in a meaningful way? Ironically, it may turn out that Marissa Mayer has significant advantage here. If you’re bright, ambitious and looking to do something meaningful with your career, what would be more appealing: trying to shoehorn your way into an already overcrowded house party, or the opportunity to roll up your sleeves and resurrect one of the Web’s great brands?

Whom Would You Trust: A Human or an Algorithm?

First published October 31, 2013 in Mediapost’s Search Insider

I’vmindrobote been struggling with a dilemma.

Almost a year ago, I wrote a column asking if Big Data would replace strategy. That started a several-month journey for me, when I’ve been looking for a more informed answer to that query. It’s a massively important question that’s playing out in many arenas today, including medicine, education, government and, of course, finance.

In marketing, we’re well into the era of big data. Of course, it’s not just data we’re talking about. We’re talking about algorithms that use that data to make automated decisions and take action. Some time ago, MediaPost’s Steve Smith introduced us to a company called Persado, that takes an algorithmic approach to copy testing and optimization. As an ex-copywriter turned performance marketer I wasn’t sure how I felt about that. I understand the science of continuous testing but I have an emotional stake in the art of crafting an effective message. And therein lies the dilemma. Our comfort with algorithms seems to depend on the context in which we’re encountering them and the degree of automation involved.

Let me give you an example, from Ian Ayre’s book “Super Crunchers.” There’s a company called Epagogix that uses an algorithm to predict the box-office appeal of unproduced movie scripts. Producers can retain the service to help them decide which projects to fund. Epagogix will also help producers optimize their chosen scripts to improve box-office performance. The question here is, do we want an algorithm controlling the creative output of the movie industry? Would we be comfortable take humans out of the loop completely and see where the algorithm eventually takes us?

Now, you may counter that we could include feedback from audience responses. We could use social signals to continually improve the algorithm, a collaborative filtering approach that uses the power of Big Data to guide the film industry’s creative process. Humans are still in the loop in this approach, but only as an aggregated sounding board. We have removed the essentially human elements of creativity, emotion and intuition. Even with the most robust system imaginable, are you comfortable with us humans taking our hands off the wheel?

Here’s another example from Ayre’s book. There is substantial empirical evidence that shows algorithms are better at diagnosing medical conditions than clinical practitioners. In a 1989 study by Dawes, Faust and Meehl, a diagnosis algorithmic rule set was consistently more reliable than actual clinical doctors. They then tried a combination, where doctors were made aware of the outcomes of the algorithm but were the final judges. Again, doctors would have been better off going with the results of the algorithm. Their second-guessing increased their margin of error significantly.

But, even knowing this, would you be willing to rely completely on an automated algorithm the next time you need medical attention? What if there was no doctor involved at all, and you were diagnosed and treated by an algo-driven robot?

There is also mounting (albeit highly controversial) evidence showing that direct instruction produces better learning outcomes that traditional exploratory teaching methods. In direct instruction, scripted automatons could easily replace the teacher’s role. Test scores could provide self-optimizing feedback loops. Learning could be driven by algorithms and delivered at a distance. Classrooms, along with teachers, could disappear completely. Is this a school you’d sign your kid up for?

Let’s stoke the fires of this dilemma a little. In a frightening TED talk, Kevin Slavin talks about how algorithms rule the world and offers a few examples of how algorithms have gotten it wrong in the past. The pricing algorithms of Amazon priced an out-of-print book called “The Making of a Fly” at a whopping $23.6 million dollars. Surprisingly, there were no sales. And in financial markets, where we’ve largely abdicated control to algorithms, those same algorithms spun out of control in 2012 no fewer than 18,000 times. So far, these instances have been identified and corrected in milliseconds, but there’s always a Black Swan chance that one time, they’ll crash the economy just for the hell of it.

But should we humans feel too smug, let’s remember this sobering fact: 20% of all fatal diseases were misdiagnosed. In fact, misdiagnosis accounts for about one-third of all medical error. And we humans have no one but ourselves to blame but for that.

As I said – it’s a dilemma.

What Does Being “Online” Mean?

plugged-inFirst published October 24, 2013 in Mediapost’s Search Insider

If readers’ responses to my few columns about Google’s Glass can be considered a representative sample (which, for many reasons, it can’t, but let’s put that aside for the moment), it appears we’re circling the concept warily. There’s good reason for this. Privacy concerns aside, we’re breaking virgin territory here that may shift what it means to be online.

Up until now, the concept of online had a lot in common with our understanding of physical travel and acquisition. As Peter Pirolli and Stuart Card discovered, our virtual travels tapped into our evolved strategies for hunting and gathering. The analogy, which holds up in most instances, is that we traveled to a destination. We “went” online, to “go” to a website, where we “got” information. It was, in our minds, much like a virtual shopping trip. Our vehicle just happened to be whatever piece of technology we were using to navigate the virtual landscape of “online.”

As long as we framed our online experiences in this way, we had the comfort of knowing we were somewhat separate from whatever “online” was. Yes, it was morphing faster than we could keep up with, but it was under our control, subject to our intent. We chose when we stepped from our real lives into our virtual ones, and the boundaries between the two were fairly distinct.

There’s a certain peace of mind in this. We don’t mind the idea of online as long as it’s a resource subject to our whims. Ultimately, it’s been our choice whether we “go” online or not, just as it’s our choice to “go” to the grocery store, or the library, or our cousin’s wedding. The sphere of our lives, as defined by our consciousness, and the sphere of “online” only intersected when we decided to open the door.

As I said last week, even the act of “going” online required a number of deliberate steps on our part. We had to choose a connected device, frame our intent and set a navigation path (often through a search engine). Each of these steps reinforced our sense that we were at the wheel in this particular journey. Consider it our security blanket against a technological loss of control.

But, as our technology becomes more intimate, whether it’s Google Glass, wearable devices or implanted chips, being “online” will cease to be about “going” and will become more about “being.”  As our interface with the virtual world becomes less deliberate, the paradigm becomes less about navigating a space that’s under our control and more about being an activated node in a vast network.

Being “online” will mean being “plugged in.” The lines between “online” and “ourselves” will become blurred, perhaps invisible, as technology moves at the speed of unconscious thought. We won’t be rationally choosing destinations, applications or devices. We won’t be keying in commands or queries. We won’t even be clicking on links. All the comforting steps that currently reinforce our sense of movement through a virtual space at our pace and according to our intent will fade away. Just as a light bulb doesn’t “go” to electricity, we won’t “go” online.  We will just be plugged in.

Now, I’m not suggesting a Matrix-like loss of control. I really don’t believe we’ll become feed sacs plugged into the mother of all networks. What I am suggesting is a switch from a rather slow, deliberate interface that operates at the speed of conscious thought to a much faster interface that taps into the speed of our subconscious cognitive processing. The impulses that will control the gateway of information, communication and functionality will still come from us, but it will be operating below the threshold of our conscious awareness. The Internet will be constantly reading our minds and serving up stuff before we even “know” we want it.

That may seem like neurological semantics, but it’s a vital point to consider. Humans have been struggling for centuries with the idea that we may not be as rational as we think we are. Unless you’re a neuroscientist, psychologist or philosopher, you may not have spent a lot of time pondering the nature of consciousness, but whether we actively think about it or not, it does provide a mental underpinning to our concept of who we are.  We need to believe that we’re in constant control of our circumstances.

The newly emerging definition of what it means to be “online” may force us to explore the nature of our control at a level many of us may not be comfortable with.

Losing My Google Glass Virginity

Originally published October 17, 2013 in Mediapost’s Search Insider

Rob, I took your advice.

A few columns back, when I said Google’s Glass might not be ready for mass adoption, fellow Search Insider Rob Garner gave me this advice:“Don’t knock it until you try it.”  So, when a fellow presenter at a conference I was at last week brought along his Glass and offered me a chance to try them (Or “it”? Does anyone else find Google’s messing around with plural forms confusing and irritating?), I took him up on it. To say I jumped at it may be overstating the case – let’s just say I enthusiastically ambled to it.

I get Google Glass. I truly do. To be honest, the actual experience of using them came up a little short of my expectations, but not much. It’s impressive technology.

But here’s the problem. I’m a classic early adopter. I always look at what things will be, overlooking the limitations of what currently “is.” I can see the dots of potential extending toward a horizon of unlimited possibility, and don’t sweat the fact that those dots still have to be connected.

On that level, Google Glass is tremendously exciting, for two reasons that I’ll get to in a second. For many technologies, I’ll even connect a few dots myself, willing to trade off pain for gain. That’s what early adopters do. But not everyone is an early adopter. Even given my proclivity for nerdiness, I felt a bit like a jerk standing in a hotel lobby, wearing Glass, staring into space, my hand cupped over the built-in mike, repeating instructions until Glass understood me. I learned there’s a new label for this; for a few minutes I became a “Glasshole.”Screen-Shot-2013-05-19-at-2.09.03-AM

Sorry Rob, I still can’t see the mainstream going down this road in the near future.

But there are two massive reasons why I’m still tremendously bullish on wearable technology as a concept. One, it leverages the importance of use case in a way no previous technology has ever done. And two, it has the potential to overcome what I’ll call “rational lag time.”

The importance of use case in technology can be summed up in one word: iPad. There is absolutely no technological reason why tablets, and iPads in particular, should be as popular as they are. There is nothing in an iPad that did not exist in another form before. It’s a big iPhone, without the phone. The magic of an iPad lies in the fact that it’s a brilliant compromise: the functionality of a smartphone in a form factor that makes it just a little bit more user-friendly. And because of that, it introduced a new use case and became the “lounge” device. Unlike a smartphone, where size limits the user experience in some critical ways (primarily in input and output), tablets offer acceptable functionality in a more enjoyable form. And that is why almost 120 million tablets were sold last year, a number projected (by Gartner) to triple by 2016.

The use case of wearable technology still needs to be refined by the market, but the potential to create an addictive user experiences is exceptional. Even with Glass’ current quirks, it’s a very cool interface. Use case alone leads me to think the recent $19 billion by 2018 estimate of the size of the wearable technology market is, if anything, a bit on the conservative side.

But it’s the “rational lag time” factor that truly makes wearable technology a game changer.  Currently, all our connected technologies can’t keep up with our brains. When we decide to do something, our brains register subconscious activity in about 100 milliseconds, or about one tenth of a second. However, it takes another 500 milliseconds (half a second) before our conscious brain catches up and we become aware of our decision to act. In more complex actions, a further lag happens when we rationalize our decision and think through our possible alternatives. Finally, there’s the action lag, where we have to physically do something to act on our intention. At each stage, our brains can shut down  impulses if it feels like they require too much effort.  Humans are, neurologically speaking, rather lazy (or energy-efficient, depending on how you look at it).

So we have a sequence of potential lags before we act on our intent: Unconscious Stimulation > Conscious Awareness > Rational Deliberation > Possible Action. Our current interactions with technology live at the end of this chain. Even if we have a smartphone in our pocket, it takes several seconds before we’re actively engaging with it. While that might not seem like much, when the brain measures action in split seconds, that’s an eternity of time.

But technology has the potential to work backward along this chain. Let’s move just one step back, to rational deliberation. If we had an “always on” link where we could engage in less than one second, we could utilize technology to help us deliberate. We still have to go through the messiness of framing a request and interpreting results, but it’s a quantum step forward from where we currently are.

The greatest potential (and the greatest fear) lies one step further back – at conscious awareness. Now we’re moving from wearable technology to implantable technology. Imagine if technology could be activated at the speed of conscious thought, so the unconscious stimulation is detected and parsed and by the time our conscious brain kicks into gear, relevant information and potential actions are already gathered and waiting for us. At this point, any artifice of the interface is gone, and technology has eliminated the rational lag. This is the beginning of Kurzweil’s Singularity: the destination on a path that devices like Google Glass are starting down.

As I said, I like to look at the dots. Someone else can worry about how to connect them.

Bounded Rationality in a World of Information

First published October 11, 2013 in Mediapost’s Search Insider.  

Humans are not good data crunchers. In fact, we pretty much suck at it. There are variations to this rule, of course. We all fall somewhere on a bell curve when it comes to our sheer rational processing power. But, in general, we would all fall to the far left of even an underpowered laptop.

Herbert Simon

Herbert Simon

Herbert Simon recognized this more than a half century ago, when he coined the term “bounded rationality.”  In a nutshell, we can only process so much information before we become overloaded, when we fall back on much more human approaches, typically known as emotion and gut instinct.

Even when we think we’re being rational, logic-driven beings, our decision frameworks are built on the foundations of emotion and intuition. This is not bad. Intuition tends to be a masterful way to synthesize inputs quickly and efficiently, allowing us generally to make remarkably good decisions with a minimum of deliberation. Emotion acts to amplify this process, inserting caution where required and accelerating when necessary. Add to this the finely honed pattern recognition instincts we humans have, and it turns out the cogs of our evolutionary machinery work pretty well, allowing us to adequately function in very demanding, often overwhelming environments.

We’re pretty efficient; we’re just not that rational. There is a limit to how much information we can “crunch.”

So when information explodes around us, it raises a question – if we’re not very good at processing data, what happen when we’re inundated with the stuff? Yes, Google is doing its part by helpfully “organizing the world’s information,” allowing us to narrow down our search to the most relevant sources, but still, how much time are we willing to devote to wading through mounds of data? It’s as if we were all born to be dancers, and now we’re stuck being insurance actuaries. Unlike Heisenberg (sorry, couldn’t resist the “Breaking Bad” reference) – we don’t like it, we’re not very good at it, and it doesn’t make us feel alive.

To make things worse, we feel guilty if we don’t use the data. Now, thanks to the Web, we know it’s there. It used to be much easier to feign ignorance and trust our guts. There are few excuses now. For every decision we have to make, we know that there is information which, carefully analyzed, should lead us to a rational, logical conclusion. Or, we could just throw a dart and then go grab a beer. Life is too short as it is.

When Simon coined the term “bounded rationality,” he knew that the “bounds” were not just the limits on the information available but also the limits of our own cognitive processing power and the limits on our available time. Even if you removed the boundaries on the information available (as is now happening) those limits to cognition and time would remain.

I suspect we humans are developing the ability to fool ourselves that we are highly rational. For the decisions that count, we do the research, but often we filter that information through a very irrational web of biases, beliefs and emotions. We cherry-pick information that confirms our views, ignore contradictory data and blunder our way to what we believe is an informed decision.

But, even if we are stuck with the same brain and the same limitations, I have to admit that the explosion of available information has moved us all a couple of notches to the right on Simon’s “satisficing” curve. We may not crunch all the information available, but we are crunching more than we used to, simply because it’s available.  I guess this is a good thing, even if we’re a little delusional about our own logical abilities.