The Future of Payments

When I was working at Citi Cards, I was under the impression that people were spending a lot of time figuring out what credit cards they should have. Were they going to get points or miles? Weren’t they going to be so excited that they could redeem their points with Amazon? Of course, working in a credit card company I was thinking about this all day and I lost sight of the fact that my customers had far better things to do with their time.

That’s why the Pymnts.com study on How We Will Pay caught my eye. The study highlighted a couple of key numbers I hadn’t thought about:

  • 61% of shoppers don’t enjoy the act of shopping
  • 66% of consumers would use a connected device to enable a seamless shopping experience

In short, most people don’t like shopping and find payments an even worse pain to deal with. The future of payments isn’t about making payments cooler (a la Venmo) it’s making them invisible. My friend Ashwin Shirvaikar described this as Internet of Things Payments in his section of Disruptive Innovations V.

But what does a future of transparent payments look like? Some examples are:

  • Uber already integrates payments seamlessly into its app. You don’t think about “paying” for an Uber. You think about booking a trip and the payment is part of that. It’s like express check out at a hotel.
  • Slice On-demand Insurance is an insurance platform for the “Gig Economy.” Slice provides insurance to Airbnb hosts and Uber drivers only when they are providing services. It integrates seamlessly into the buying transaction by providing insurance any time the host takes a reservation.
  • Parkmobile, a leader in mobile parking, has developed an integrated parking solution with BMW.  When parking at a Parkmobile enabled location, drivers will be able to begin a parking session directly from their dashboards without leaving their car to pay a meter. The parking session is terminated once the driver leaves the spot. 

But who should develop the future of payments? The Pymnts’ How Will We Pay survey asked this question to consumers. Interestingly enough, the top named company was Amazon.

The Pymnts’ How Will We Pay Survey. Note: Super Connected Consumers Have 6+ Devices That are Not Laptops, Smartphones or Tablets

So why does Amazon come up so high on this list? Because customers want an innovative shopping experience, not an innovative payments experience.

The best example of this is Amazon Go. Amazon Go is a prototype payments experience of the future. Customers go into an Amazon Go store, pick up their items and leave. Checkout is performed automatically when the customer leaves the store. While there are currently some issues around the price to create these stores (automation being more pricey than human labor) and theft due to shoplifting, this is a good view of the future of payments.

While those working in the payments industry think about payments all day, consumers see payments as an inconvenience. Some services like Parkmobile and Slice are already providing great payments integration. In the future, companies will be providing truly integrated services like Amazon Go.

Reframing Bad News to Inspire the Team

A leader’s job is to inspire their team. This is easy when things are going well. A good leader can keep morale up even when there’s bad news. But a great leader can find a way to use the bad news itself as inspiration.

I remember two years ago working at a financial services firm in an innovation group. John was the leader of the group. He started out the weekly team meeting by saying, “We got some news over the weekend. We redid our projections for the project launch and instead of the $50 million that Tony projected, we are only going to make $5 million in the first year.”

Continue reading “Reframing Bad News to Inspire the Team”

Alexa Blueprints: Personal Alexa Skills in Minutes

I like building Alexa Skills. Skills are Amazon’s name of the apps that run inside Amazon’s Echo and other Alexa products. Building skills is a good way for me to understand how Alexa works and it’s a pretty neat party trick to get Alexa to pretend that she knows me.

Building skills used to be difficult.  You needed to know how to program a voice user interface. Alexa soon came out with templates which made things easier but still required you to know how to program and so some basic system administration.  I persisted in building a simple trivia game and Amazon sent me a T-Shirt for having published one of the first 1000 skills.

My Shirt for Building One of the First Thousand Alexa Skills (Front)
My Shirt for Building One of the First Thousand Alexa Skills (Back)

I remember thinking, “Where can Alexa be useful to me?” At the time, my boys were just starting to learn peoples birthdays. So I started building a skill for family birthdays. This was a great way to drill them on an important topic until they memorized it.

Alexa would ask, “Who’s birthday is on June 20th? One … Daddy … Two … Mommy … ”

And the kids would respond, “Daddy!” (Actually, they probably said “One” because the technology wasn’t very good at picking up names at the time.)

The problem with this skill is that you needed to include all the birthdays into the skill itself. So I could build a birthday family trivia skill but it would have the birthdays for my family. That’s not a particularly useful skill for other people.

My wife Abigail had a good idea for a skill as well. She said, “It would be really convenient to have a skill for the babysitter to know everything about the house — like bedtimes, WiFi passwords and emergency phone numbers.” But we couldn’t do this either because the program needed to have OUR information inside of it.

Amazon solved a lot of this problem last month with Alexa Blueprints. Alexa Blueprints are a simple and convenient way to create a customized skill that’s just for you. Instead of programming to create the skill, you just type into the web forms and Alexa does the rest.

You Create a Blueprint Just by Filling Out a Form

There’s a number of pretty cool thing is that you can build with Alexa Blueprints. I can easily build my birthday trivia game, Abigail’s babysitter skill and even a Game Show complete with buzzers. My sons and I used the Mother’s Day blueprint to build a card for my wife. It took about 10 minutes and Voila — the Mother’s Day card was done!

Sample text for a 10 minute Alexa Mother’s Day Skill

Take a look at this video to see how you can create your own Alexa blueprint.

Update (10/1/18): I’ve also created another couple of skills for the boys. I’ve used the Flashcards Blueprint to create a vocabulary quiz for my son’s fourth grade class. It’s a great way for him to come up with the definitions for us to put in. Also, we use Whose Turn to decide who’s turn it is to do different activities. 

The Mother-in-Law’s Guide to Cloud Computing

This is part of my “Mother-in-Law’s Guide to Technology.” My Mother-in-Law is a very smart woman even if she isn’t a “computer person.” The goal of this post is to take a very big and treacherous sounding idea and bring it down to earth. I tried this before in a post which I’ve now renamed The Mother-In-Law’s Guide to Chaos Engineering.

Dearest Mother-in-Law,

You know when we visit a Target or a Wal-Mart in the suburbs and they have 30 checkout lanes and only 3 are open at any time? I always wondered why that happens. It even sparked someone to write a funny blog post about the phenomenon: Target Store Opens More than Three Checkout Lanes; Shoppers Confused.

On a Normal Day, the Store Has Full Time Cashiers to Manage the Base Volume. When More People Come In, Part-Time Cashiers Will Be Engaged.

How many checkout lanes should Target build? At first, I thought about how many customers Target has on an average day and that they built that number of cash registers. If they have 300 customers in a day they would need enough cashiers to serve 300 people. The problem is that the flow of people into the store isn’t constant. For example, if the peak time of day is at 4PM and there are 10 people in line, Target can’t tell those people to come back at a less busy time. So on a daily basis, they need to plan for this by making sure they have enough checkout counters (and cashiers) available to keep the lines down to a reasonable level even when it gets busy. The way most retailers do this is to have only a few full-time dedicated as cashiers for the slow times and some other part-time cashiers that mainly do another job but can jump in when the store gets busy.

But that doesn’t answer the question of how many checkout lanes they need to build. Target needs to have enough checkout lanes so that even on the busiest days, they can hire enough part-time cashiers to keep lines relatively short. This means that Target needs to build the number of checkout lanes that they need for the busy time of the year, not for the peak time of day. At Target, this is the Christmas shopping season starting with Black Friday. On Black Friday the store is filled with shoppers struggling to check out. This is the day that Target opens up all their checkout lanes. So even though they’re not used a good portion of the year, Target still needs to build the number of checkout lanes they need for Black Friday.

The Number of People on Black Friday is Much Greater Than That of a Normal Day. This Drives the Total Number of Checkout Lanes.

So what does this have to do with cloud computing? Cloud computing is like Target having these checkout lanes only where they’re needed, like on Black Friday. They wouldn’t have to pay for the cost of having these checkout lanes at less busy times of the year. They would be able to create new ones during the Christmas season and get rid of them at other times of the year. How does this work? Instead of buying checkout lanes (or in the case of cloud computing, computers), they just rent what you need. This means that Target can increase or decrease their capacity based on the actual need from your customers.

Now let’s make the jump from Target to Cloud Computing by defining a few Let’s define a few things:

  • Servers: These are computers that “serve up” the information you need. Just like the cashiers at Target, if a server isn’t available you’re going to have to wait in line.
  • Server Capacity: This is the total number of servers that can be available to provide information. Just like the number of checkout lanes at Target, once you’re out of checkout lanes, you can’t have any more cashiers.
  • Peak Request times: This is your Black Friday time when you the most requests.

Now you can understand one of the key benefits of cloud computing:

Cloud computing provides flexible server capacity to meet demand during peak request times and release that capacity at during other periods.

So there you have it. In the real world, Target needs to build enough capacity (checkout lanes)  to meet demand during peak request times (Black Friday). But the cloud computing model allows companies to greatly reduce their capacity during non-peak times because they can easily turn on or turn off this capacity.

Note: You can actually see a checkout model like this (sans the physical checkout lanes) at Apple stores. They can easily increase or decrease capacity because they don’t have any physical checkout aisles. This allows for flexibility by just adding or removing salespeople to the store with their mobile checkout devices. 

Additional Resources: For more information on managing lines check out this quick overview from FiveThirtyEight. For more on Cloud Computing, take a look at Google Cloud Platform training or Amazon Web Services training. You can audit classes for free.

 

 

The Value of Big Data: Why It’s Difficult to Monetize and How Google Does It

I recently attended a session on Autonomous Cars at the law firm Herbert Smith Freehills. It was an insightful session where the lawyers gave great presentations on legal issues they advise on, like M&A, regulatory and product liability. However, one non-legal item they talked about was the ability for car manufacturers to “monetize data.” The idea of monetizing data comes up often but it’s a lot harder than it sounds.

A decade ago, I was working for a large credit card company looking at new growth opportunities. We were convinced that we could become the most valuable company in the world. Our reasoning went like this. Google was worth billions of dollars. But Google’s value was based on what web links people clicked. We, as a credit card company, had data on what people actually bought. Because our data was more relevant to advertisers than Google’s data, we should clearly have been worth more than Google.

There was just one problem. While we had this data, so did Bank of America, Capital One, JP Morgan and every other bank. And everyone was looking to monetize their data.

Did I say one problem? It wasn’t just financial services companies looking to out-Google Google. The phone companies were in this game too. They were saying, “Hey, we should be the most valuable companies in the world. Google has data on where people go on the web, but we have data on where people actually are in the real world.” Suffice it to say, there was a lot of data around.

This reminded me of an article written about undersea cable capacity in the days of the telegraph. Andy Kessler shared the following cautionary tale:

After undersea telegraph messages were first sent between Newfoundland and Ireland in 1886, a half-dozen companies sprang up to relay messages between London and Paris and New York. Half the traffic was for stock trading. These companies charged up to $5 per word and could transmit 15 to 17 words per minute. Each thought it could generate revenues of $5 million dollars or more per year. It was easy to raise the $2 million it took to lay undersea cable and investors, who constantly dashed off telegrams themselves, were all too happy to lend money.

Each of these companies assumed that they’d have a monopoly on the market. But when many companies entered the market based on that same assumption, all of the excess capacity created a race to the bottom for telegraph message pricing, forcing many of the companies into bankruptcy.

So what makes Google different? I remember a discussion with stock analysts around that time. I had written a paper on Mobile Payments along with Citi’s Equity Analysts. The topic of data was very hot and various analysts asked me, “Who’s going to win the data game? Who has the best data?” I explained that the real differentiator, and what people will pay for, isn’t the data itself but what you can do with the data.

As the famous Harvard Marketing professor, Theodore Levitt said, “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!” In the data space, this would be, “People don’t want to buy data, they want to buy results!”

How Google Uses Big Data

The goal of a search engine is to find the most relevant documents. In the early days of search engines, things were relatively easy. You could:

  1. Examine Web Pages: Early search engines like Lycos and Altavista would look at web pages and determine which ones were the most relevant. They would do this by looking at factors like the number of times a word was repeated or whether the search term was in the title of the document.
  2. Curated Directory: Yahoo, on the other hand, had humans hand-curating the web into a giant directory. This was relatively easy when the web only had a few thousand pages.
My Interpretation of the Early Web. With Only a Few Pages, Choosing a Winner Wasn’t That Difficult.  

However, as the web grew, it became more and more difficult to manage search with these methods. Lycos and Altavista were overwhelmed. Not only was it difficult to distinguish between multiple similar pages based on the text in the page but there was also web spam that was trying to fool the search engines into promoting their pages. Yahoo had a problem hiring enough people to keep up with the quickly growing web. Both had doomed strategies.

The State of Web Search When Google Entered the Game. As the Web Started Exploding, Finding the Best Pages Became Increasingly More Difficult.
Google went down a different path. By using an algorithm called PageRank (after Larry Page), formerly called BackRub (oh those Googlers and their funny names), Google was able to make use of data that everyone else was overlooking. The links between pages were just as valuable as the data in the pages themselves. For example, any page can claim to be the authoritative page of IBM. But if 100 people point to IBM.com as the right answer, it’s easy to lift that one to the top.
Google Changed the Game by Using Links from Other Sites as a Measure of Quality

There are a few things to realize about Google’s use of data:

1.  Google didn’t have the “best” data. Yahoo had a more accurate method for categorizing the web. Having humans look at content gave better results for each individual page. Unfortunately for Yahoo, that method was too slow and expensive to sustain.

2.  The data didn’t cost Google anything. At the time, everyone was concentrating on the web pages themselves — not the linkages between the pages. This kind of information is often called “information exhaust” — information that’s a by-product of what you’re really looking for. It was already out there, free for anyone to use.

3.  It’s the capability that made the difference. While the data was free, it was up to Google to organize the data and make it useful. Going back to the jobs to be done metaphor, Google put this data to work solving a problem for users.

4.  More data is better. While other search engines were getting overwhelmed by the torrent of data from an explosion of web content, Google’s product actually benefited: The more links that can point to a quality web page, the better search results Google produces.

Google has been using this template for various other projects since they were founded. They can leverage data in some very creative and useful ways. Take location data for example. If you have an Android phone or Google Maps on your phone, Google is keeping track of your location data. You can take a look at your data here. The data is useful to me but it’s a bit odd seeing that Google holds a record of everywhere I’ve been.

An Example of Google Tracking Me Through the Day.

So how can Google use your location, along with that of others, to create value? Well, one way is to aggregate this data to show where there’s road traffic. If you have a lot of phones not moving, then you can flag that road as congested. But where else could Google use this data? Google added a feature to Google Maps that let you see how crowded a restaurant was at different times of day based on how many cell phones they found at the restaurant.

A Graph of Popular Times at Bubby’s Restaurant Compiled Through Location Data. Note the Popularity of Sunday Brunch.

It’s important to remember that Google did not have the best data to determine busy times at restaurants. Telephone companies and restaurant sites (e.g., Yelp, OpenTable) likely had better data. For example, OpenTable manages the reservations systems for many restaurants and actually knows how busy they are. But yet again, Google was the best at putting the data to work at solving this problem.

So let’s sum up. People still talk about monetizing data but their data isn’t as valuable as they think it is. There’s a lot of data out there that can solve problems and generate value. The tricky part is extracting the value from the data. Google did this in search and continues to do so in lots of other ways.

Note: Ben Thompson from Stratechery gave a similar talk about how Google works last week to kick off the University of Chicago Antitrust and Competition Conference.

Why I Love My Fitbit Alta HR

The Fitbit Alta HR is one of the few technology products that gives me almost everything I need and very little that I don’t. It’s a tour de force of good design. When I look at what I need on my wrist, it’s not really a smartwatch or even a fitness tracker but something else (maybe a “smart wristband”). Let’s take a look at the 3 features of the Fitbit Alta HR that are most important to me.

3 Features I Love About My Fitbit

  1. EASY Sleep and Exercise Tracking. I need to track how much I sleep and how often I go to the gym. I used to have a Fitbit Flex, which while being a good product, made me manually track my exercise and sleep. For example, to track sleep I was supposed to tap on the band when I went to sleep and when I woke up. This meant that I forgot many days and didn’t have good data on my sleep patterns. The Fitbit Alta HR makes use of its heart rate tracker to guess at when I go to sleep (my heart rate drops by a lot) and when I’m exercising (heart rate goes up). It can even figure out the type of exercise I’m doing (i.e., bicycling, walking, sports).
  2. Vibrating Notifications. For about 20 years, since I got my first flip phone, my friend Seth Gilbert and I talked about how difficult it is to make sure that we got our phone calls. We would put our phones on vibrate in our pocket and occasionally miss calls. Women had it worse because their phones were in their purses. With the Fitbit Alta HR, the wristband connects to my phone and will vibrate when there are calls or text messages. But notifications are limited to ONLY calls and text messages so I’m not bothered or even tempted by anything else (e.g., app notifications).
  3. Tactile Alarm Clock. A wise man once said, “The hardest thing about being married is not having your own alarm clock.” Not really. I like to wake up earlier than my wife so I can relax and let my mind settle. But if I set off an alarm clock, then everyone wakes up. Having a vibrating alarm clock allows me to wake up with a vibration that’s much nicer than the buzzing of the alarm and wakes me up without waking up the rest of the house.

How I Might Improve These Features

As a product person, it’s fun for me to think about how to make these features even better.

  1. Exercise Guidance (building on the feature EASY Sleep and Exercise Tracking). It would be great if my Fitbit could offer me sleep and exercise guidance in addition to tracking. Fitbit’s certainly going in this direction after having bought Fitstar and renaming it Fitbit Coach. Trying to put all of Fitbit Coach into something without a screen is difficult so I don’t think it’ll be done in the near term. However, I can see something simple like the Seven Minute Workout coming relatively soon.
  2. I Need You NOW (building on the feature Vibrating Notifications). In an emergency, you really want to get someone’s attention. For example, parents want to know where their kids are but they’re not checking their phones. One way that I’ve seen parents get their kids attention is to set off the “Find My Phone” alarm on their kids iPhone. The “Find My Phone” alarm is normally used when the phone is missing in a large house so it puts out a piercing screech which cannot be ignored. One way of bringing this to the Fitbit would be priority vibrations. For example, if someone calls multiple times in a row, the vibrations would increase in intensity so that the wearer could not ignore them. If we wanted to bring this to the next level we could add a small shock to the wristband though I’m not sure how well that would sell 🙂
  3. Wake Up and Relax (building on the feature Tactile Alarm Clock). The first thing I do when I wake up in the morning is turn on my meditation. It would be great if my Fitbit would wake me up and then go right into a 5-minute breathing exercise. The Fitbit Charge 2 HR already has a clever meditation routine for a small screen called breathing sessions. I imagine that my experience would go something like this:
    1. The alarm rings
    2.  I wake up and tap it to tell the watch I’m awake
    3. The guided breathing exercise starts. In … Out … In … Out
    4. If I’m not breathing at the guided breathing rate for 30 seconds, the Fitbit assumes I’ve gone back to sleep and tries to wake me up again.

Reasons I Like My Fitbit More Than an Apple Watch

The obvious comparison here is to the Apple Watch. “Why not just get a smartwatch?” you might say. Then you’d only have to have one thing on your wrist. Here are the reasons that my Fitbit is better than an Apple Watch — at least for me.

  • It’s Not a Watch. I really like my watch. It’s beautiful and far nicer than an Apple watch. Wearing both watches looks a bit silly because hey, how many watches does a person need? It’s also a bit annoying because it has the feeling of “Go Apple or Go Home” —monopolizing my wrist. It’s one thing for my technology items in my life to be Apple. I mean I love my iPhone and I love my MacBook Air but I don’t need everything in my life to be Apple.
  • Long Battery Life. Because it’s not trying to do too much, the battery can last a week without charging. This lets me wear it to sleep which gives me half of the features that I want in my wristband (sleep tracking and alarm clock). The Apple watch needs to be charged every night so misses these features.
  • Small, Light and Fashionable. Because it doesn’t have these Apple Watch features (especially as it doesn’t need a full screen) the Fitbit Alta HR can be small, light and fashionable. I can wear it to the sleep, to the gym and even do vigorous exercise I don’t notice it.
  • Limited Notifications and Features. While there’s another feature or two that I’d love to have, I like the fact that I’m not distracted or even tempted by them. I’m sure if I had an Apple Watch I’d be tempted to read the newspaper or check my appointments on my watch — which I really don’t need to do.

Overall the Fitbit Alta HR is an awesome device. It gets me 90% of the things I need and little else. When I started writing this post I thought about how weird it was that I was talking about a fitness tracker that I don’t really use for fitness very much. Then I came across Wearable’s Top Fitness Tackers of 2017 and saw that they rated the Fitbit Alta HR as their top choice. One of the top features for the favorite “fitness tracker” was sleep tracking!

Big Data vs. Small Actionable Analytics

“Big Data” is a technology buzzword. The idea is that we have so much data about people and the way they interact with a company, we should be able to generate new and interesting insights from this data that will solve business problems.

But there’s a catch. Big Data is just another form of analytics. In most companies, each additional piece of data provides less value than the previous piece of data.  In economics, this is called the diminishing marginal utility. So if the first piece of data (e.g., has this customer bought this product before) may be worth $1, the 50th piece of data (e.g., how old are the customer’s children) may worth less than a penny.

The First Piece of Data Gives a Lot More Value than the 50th Piece of Data 

Unfortunately, many people are unduly optimistic about the value that big data can provide.  They have this idea that they have so much data, if only they could search all this data, there’s bound to be something useful in there. It reminds me of one of Ronald Regan’s favorite jokes:

The joke concerns twin boys of five or six. Worried that the boys had developed extreme personalities – one was a total pessimist, the other a total optimist – their parents took them to a psychiatrist.

First the psychiatrist treated the pessimist. Trying to brighten his outlook, the psychiatrist took him to a room piled to the ceiling with brand-new toys. But instead of yelping with delight, the little boy burst into tears. “What’s the matter?” the psychiatrist asked, baffled. “Don’t you want to play with any of the toys?” “Yes,” the little boy bawled, “but if I did I’d only break them.”

Next the psychiatrist treated the optimist. Trying to dampen his outlook, the psychiatrist took him to a room piled to the ceiling with horse manure. But instead of wrinkling his nose in disgust, the optimist emitted just the yelp of delight the psychiatrist had been hoping to hear from his brother, the pessimist. Then he clambered to the top of the pile, dropped to his knees, and began gleefully digging out scoop after scoop with his bare hands. “What do you think you’re doing?” the psychiatrist asked, just as baffled by the optimist as he had been by the pessimist.

“With all this manure,” the little boy replied, beaming, “there must be a pony in here somewhere!”

If you have enough data, you’ll certainly find relationships between the data. But then you have a new problem − whether or not these findings will help you run your business. You can find lots of statistically relevant correlations that are completely spurious, like this:

So how should you use Big Data? Start with small actionable analytics − analytics that matter. That’s what I did when I joined a new group at a large company. Though the business unit had a chief data officer, our team didn’t have the analytics that we needed. So I implemented a simple plan for understanding the users, making sure we had good data and building iteratively over time.

Understand the Users

The first thing we needed to do was understand who was going to use the system. These users were going to define success for the project. We had two user groups:

  1. Senior Management: This was the easy one to identify. Senior management wanted some basic information to run the business.
  2. Power Users: However, we wanted to have a robust view of the data, not just high level reports. So we needed to look for our power users. And that’s when we found him. Let’s just call him Power User. He was based in London. Each month, Power User asked for a download of all the raw data and ran a 10 year old program in Excel to get the output he needed. The output looked great but the program was very fragile, hard to read and not extensible. But the output of this program gave us a great starting point.

Make Sure We Have Good Data

Analytics, first and foremost, is about the quality of the data. If you don’t have quality data none of your analytics will be right, whether it’s big data or anything else.  When I started working on this project there was a report that showed the volume of payments being processed. Every few months there was a massive spike in volume. This wasn’t something hard to see. It was a spike of a quadrillion dollars going through the system. By comparison, all the value of everything in the world is only $241 trillion. This report was a couple of years old and people reviewed it each month but no one mentioned anything. As it turns out, testing data was being included which is what messed up the numbers. Cleaning all of the data and ensuring its quality took a lot of time but was an integral part of the project.

Build Iteratively Over Time

We spent a lot of time with our users understanding what they wanted. We did most of this on the cheap. Instead of building out real applications, we created PowerPoint and Excel mock-ups to understand their needs. We went through many iterations to pin down what they needed. The real test was with Power User.  He was this really smart data geek and had been doing the same thing for 10 years. I remember holding my breath when I asked him for feedback. I was happily surprised with the reply, “Fantastic!!! This is really really good, guys, thank you so much!!!”

Don’t Over Build

Once we’d finished the first phase we’d expected to move our work out of Excel to a “real” business intelligence platform like Microstrategy or Cognos. However, we realized that the user group was so small and the flexibility they needed was so great that there was little value in moving off of Excel.

In Summary

Big Data is just another form of analytics. If you don’t do analytics well, you’re unlikely to find something magical under your pile of Big Data. It’s best to start small and really understand your basic analytics by understanding your users, ensuring your data quality and building iteratively.

The Secret to Google’s Self Driving Cars — Google Street View

For decades the US military was trying to create self-driving cars with little success. Once the private sector got into the game, these cars improved at a breakneck speed. In 2004, when the first DARPA Grand Challenge took place, no car in the world was able to complete the 150 mile course through the Mojave desert.  By 2016, self driving cars had driven over a million miles in regular traffic. The secret was not better computers or better cameras. The secret was better maps.

Peter Norvig, Google’s head of research, told the New York Times that Google Street View is the secret sauce behind Google’s self driving cars. He said:

It’s a hard problem for computer vision and artificial intelligence to pick a traffic light out of a scene and determine if it is red, yellow or green. But it is trivially easy to recognize the color of a traffic light that you already know is there.

I remember hearing that and thinking how convenient it was that Google happened to have Street View and that they could apply it to self driving cars. This would have been a classic case of “unlocking the power of data.” But then I learned the rest of the story.

Sebastian Thrun is the creator of Google’s self driving car and the founder of Google’s “X” lab. Google didn’t just “happen” to have Street View data lying around. Street View was created by Thrun after he met Larry Page at the DARPA Grand Challenge — the self driving car race. Thrun tells the story on CNBC’s The Brave Ones:

Larry came to the race itself and … came disguised with, like, a hat and sunglasses so he wouldn’t be bothered by everybody. But … he had a keen interest in this. Larry has been a believer in this technology for much longer than I even knew. And so was Sergey (Brin). And they really want to understand what’s going on,” Thrun said.

A later iteration of the car had cameras attached to its roof, so the team could review its progress each day, leading almost by accident to the development of Google Street View.

“We realized the video’s actually amazing. And we went to Google and said ‘we’d love to help you build Street View.’ And we kind of ended up – felt like an acquisition of a little start-up company, kind of Stanford transitioning into Google where me and four of my grad students then became Street View enthusiasts.”

“And we built up Street View and with a singular vision to photograph every street in the world.”

Street View became the first project within the secret Google X. “We had a separate building that no one knew about. At least for a year and a half, no one in Google had a clue we existed,” Thrun said.

So what did we learn? Data was the secret sauce for getting self driving cars to progress as well as they have. But it wasn’t a matter of finding a data set and applying it. It was about creating the data set for that specific purpose. Street View wasn’t a useful data set that was applied to self driving cars. It was the output of the mapping exercise that made self driving cars work so well.

One final addendum: When talking about Google Street View I have to add a link to an early version of Street View from 1979 that was created at MIT. The Aspen Movie Map (movie) used laserdiscs to simulate driving through the town of Aspen.

Smart Audio is Here to Stay: Some Takeaways from NPR’s Smart Audio Report

NPR and Edison Research have been putting together The Smart Audio Report. The study, presented at CES in January, gives a good look into how quickly smart speakers like Alexa and Google Home are entering the home:

  • It’s growing fast: 16% of Americans have a smart speaker − 128% growth since January 2017
  • Usage is growing over time: 84% use their speaker the same amount or more than the first month they owned it
  • They’re becoming embedded into people’s lives: 65% say that they would not like to go back to life without their smart speaker

The most interesting chart is a breakdown of the most frequently used activities by the time of day.

I haven’t done many of these things but I look forward to finding out more about them!

Fun with Patents OR The Possible Future of Amazon Alexa and Google Home

In the article Hey, Alexa, What Can You Hear? And What Will You Do With It?, The New York Times delved into some of the patents that Amazon and Google have filed for the future of their voice assistants (Amazon Alexa and Google Home). The article focused on privacy concerns by the group Consumer Watchdog that may or may not have understood what a patent is. The stuff that really freaked people out was the Amazon patent that focused on an “always on” capability where the assistants are always listening to the discussions around them.

It’s an interesting idea to use the conversations in the room to develop a better understanding of them; however, the language used clearly doesn’t take privacy into account. The patent was filed more as a future idea rather than something with all the kinks figured out.  But I can understand why some phrases from the patent Keyword Determinations From Conversational Data upset people. To paraphrase:

In at least some embodiments, a computing device such as a smart phone or tablet computer can actively listen to audio data for a user, such as may be monitored during a phone call or recorded when a user is within a detectable distance of the device. In other embodiments, voice and/or facial recognition, or another such process, can be used to identify a source of a particular portion of audio content.

I thought some of the other patents might provide a window into how Amazon and Google viewed the future. My favorite one was titled Monitoring And Reporting Household Activities In The Smart Home According To A Household Policy and was written by Tony Fadell, founder of Nest and one of the fathers of iPod.

This patent talks about various different ways to make a home “smart.” Today having a smart home means being able to control various devices, but what if you could set a goal (or policy in the words of the patent) and the smart home would partner with you to achieve it. To paraphrase the language of the patent it is:

A method for household policy implementation in a smart home, comprising: monitoring the household, analyzing household activities, taking actions and reporting the information. This system can help a family achieve goals such as how much screen time is used by family members, how often the household eats together and whether mischief might be occurring.

Ignoring the obvious privacy issues, there were some interesting things here. As a father, this was really interesting because it thought of the way to install parental controls over my entire smart home.

Let’s start with the overall partnership model. As the parent, I get to define a goal and the house will help me achieve it. How will this work? Let’s look at the example of tracking screen time. I’m kind of excited about a future where I can say “Limit my kids to 30 minutes of screen time.”

First, we need to monitor screen time. We need to understand who is in the room and what they’re watching.

Then we need to define our goals.

Finally, we take an action based on whether the goal is met or not.

Other factors may come into play. For example, if the child has been grounded they may lose their TV time.

Also, just because this was pretty funny, I have to include the patent’s “mischief detector” that detects mischief by  (again paraphrasing):

listening for low-level audio signatures (e.g., whispering or silence), while the occupants are active (e.g., moving or performing other actions). Based upon the detection of these low-level audio signatures combined with active monitored occupants, the system may infer that mischief (e.g., activities that should not be occurring) is occurring. Additionally, contextual information such as occupancy location may be used to exclude an inference of mischief. For example, when children are near a liquor cabinet or are in their parents’ bedroom alone, the system may infer that mischief is likely to be occurring.

While I probably won’t be using the mischief tracker any time in the future, the idea of setting goals for the household, and letting Amazon and Google help, is quite appealing.