Tuesday, August 30, 2016

The Disruptive Power Of Autonomous Vehicles

The first thought most people have on self-driving cars is how nice it will be to do email while you are stuck in traffic.  That will be nice and, although Tesla's autopilot technology is being scaled back due to some recent accidents, that's a pretty powerful step in the right direction.

Uber is making some big moves in the autonomous vehicle space.  One is to purchase an autonomous vehicle startup called Otto that makes technology for two things: 1) passenger cars and 2) an autonomous trucking business.  And, Uber is on the verge of launching a pilot in Pittsburgh where the car is completely autonomous, but there is a human behind the wheel for safety.

That means, you can expect to see driverless Uber's, which will be much, much cheaper (and still more profitable for Uber).  And, you can also expect to see big rigs next to you on the highway without a human in there at all.  OK, in the early stages, both clearly have a human for "emergencies", but it won't be long before the human is gone.  Here is a great article on this from Bloomberg.

I recently built an ROI model on whether it was cheaper for my family to become a one car household and for me to Uber everywhere.  I wasn't planning to do it, but I was curious.  As I went through that process, I realized driverless cars will change how we think about acquiring time in a vehicle and who we buy it from.

The biggest issue I had with Ubering everywhere is that I love my BMW 535 and Uber Pool or UberX just isn't my BMW.  So, what I wanted is a contract with BMW that guarantees me a certain number of car hours, a certain response time in a specific model of car.  The best company to provide that to me is actually BMW.

This offers today's car manufacturers a new business model where they own and pool the cars and lease them to customers just-in-time.  The pricing matrix has miles a year on one axis and speed of response time on another.  Want a car for 10,000 miles a year and you want it there in 15 minutes or less every time, that's $450/month.  Willing to accept a response time of 30 minutes, that's only $350/month.  It's not too far from today's leases, except that BMW pools the assets and provides BMW-as-a-service vs. my car sitting in the driveway or parking lot at work most of the time.

It works for premium brands and premium cars because there is a brand affinity or a real product differentiation.  It doesn't work as well for economy cars because the underlying motivation is value and it will be tough to compete with an autonomous UberX on a straight value play.

Ford recently announced their intention to build autonomous vehicles for a Ford Ride-sharing and Ride-hailing service.  That's a first step in this direction and it is presumably why they invested in Pivotal - that partnership is a way to ramp their software development competency very, very quickly.

Autonomous vehicles are a technology that is about to unleash a massive wave of disruption in the auto industry, trucking, retail, delivery and beyond.  We think Uber is disruptive today, autonomous cars and going to dwarf the disruption so far.

Wednesday, June 15, 2016

Robots Aren't Just For Cost Savings

A new burger place opened in my town and I was very excited to try it.  I went for lunch and had one of the best burgers I have ever eaten.  The bun was perfectly buttered and toasted on the grill so it crunched just a little with each bite, the meat was perfectly warm, pink and juicy in the middle and the special sauce, lettuce tomato took it over the top.

I gathered the rest of my family and headed back a few days later, telling them this place was amazing.  Unfortunately, my bun was not perfectly toasted, my meat was not perfectly cooked, it wasn't warm, and the special sauce couldn't save the overall poor execution on the rest of the burger.

I blame humans for this problem. A robot would have been able to provide a more consistent level of product quality by using precise measurements for cooking temperature and time, understanding all the items that are related in an order to bring the entire order together with precision.  A simple survey after the meal would allow the machine to use machine learning for continuous improvement.

A lot of people talk about robots and automation as a way to reduce cost for large corporations by replacing people with machines.  But, there is another, potentially more important upside to robots and automation and that is service consistency, product quality and topline revenue impacts. I don't think our burger place is about to buy a Momentum Machines burger robot, but it illustrates the point.

A few technology trends are compounding to make automation and robots more broadly applicable and these trends could very likely cause an explosion in real world applications of robots.  The first is the dramatic increase in computing power and speed, the second is the dramatic reduction in the cost of storing data.  Both of these trends are true in small form factors and at massive scale.

A very small robot can house terabytes of data and process billions of instructions per second.  A consolidated cloud environment can house limitless data at pennies per gigabyte and has limitless, scale-out computing power to analyze all that data, instantly.

This allows a robot to do things that a human can't.  It can analyze more complicated interdependencies in realtime.  It can execute with more perfect precision than a human can.  And, crazily enough, it can learn and continually improve, called machine learning.

Machine learning has a couple of modes.  The first is supervised machine learning.  That's where you tell the machine what the organizing principle or goal is, and the machine categorizes things into the model defined by a human.  The other is where you just give a machine a bunch of data points and the machine categorizes them into groups and can continually refine the categorization. (Interested in more on machine learning, check this out)

The more data we can give a machine about the situation at the time of an action and the more data we can give the machine about the output of that action, the faster the machine will learn and improve.  At some point, humans can't take any more data about the situation or output and so we continually repeat sub-optimal actions.  Robots typically won't do that - they will learn.

Robots are coming. They are coming soon.  And they will be able to make our lives better and reduce the cost of just about everything.  That's not really a question.  The real question we need to wrestle with is how our society will adjust to a world where we can automate almost every aspect of our lives and economy.

Friday, May 20, 2016

Will this become THE platform for IoT-based business processes

This week at Knowledge16 in Las Vegas, ServiceNow made some pretty cool announcements, a few of which are extremely relevant to the IoT space and to my last few blog posts.  The Day 2 keynote from Dan McGee is the best place to see the full details, but I'll give you  may take on it here...

ServiceNow introduced what they call The "Connected Experience".  It's a way for any enterprise to build business processes and workflows on top of the Internet of Things without a custom application for every business process.

The biggest problem with IoT today is that it's often thought of as a big dumping ground for all kinds of data that will be analyzed to find the great insight.  The reality of IoT is that it will be devices that publish specific events and data, which feed a specific business process (or workflow).  Tightly coupled devices & processes.

Today, that business process requires a custom application to be written for the consumption and use of that information, to drive the business process.  Even with the most "agile" and "extreme" development processes, that's a lot of custom apps, interfaces and code to manage which will be slow to develop and expensive to maintain.

The ServiceNow "Connected Experience" provides is a way to build structured workflows and business processes based on subscribing to the events and analyzing that data.  At it's core is a workflow engine, with an application interface for end users to see their tasks, to collaborate and conduct work. This has a modern web app and a mobile interface. Lastly there is an analytics engine to provide more predictive notifications and workflows.  A screen shot from the keynote shows the concepts and workflow in detail...

To make this easier to understand, let's use a couple of examples...  The first one is pretty close to the current ServiceNow business.  There's a server that fails in a branch office for a global bank.  The notification is sent, starting a workflow to repair the server.  The workflow rules notice this server is required for a mission critical app and the bank cannot operate without the application running, which causes and automated escalation process.

This escalation process is driven through notifications and visual task boards in the Connected Experience.  Those applications allow participants to collaborate in the application, including adding new participants and sharing context and knowledge in a structured way.  This is very new and it speeds the process and management oversight by replacing phone calls, emails and chaos with a simple web-mobile interface.

The second thing you can do, by looking into the CMDB is identify other branch offices that are susceptible to this same failure due to similar hardware, firmware and software configurations and trigger a series of workflow tasks to preventatively maintain those servers.  That's the predictive analytics component.

OK, so that's the traditional data center view and it's pretty close to the current world.  What about something a little more in the future of IoT.  A vending machine publishes it's low inventory status, which triggers a restocking action item.  But, the workflow checks the inventory of other machines in the area and cost optimizes the other stocking actions based on a myriad of variables including the driving routes, current inventories, inventory consumption rates, cost of fuel, etc...  The restocking item is updated with the precise inventories needed at each location and a driving map of the precise route the driver should take.

Sure, you could write a custom app for both of these situations, but it's faster to build in a platform fit-for-purpose.  It's also easier for end users to consume many different business processes in a single integrated interface vs. having to use 5-10 different applications.

I see a tremendous and groundbreaking opportunity for this Connected Experience to become THE platform for building business processes on top of any IoT platform and data set.  Time will tell, but it's a compelling vision from a market leader.

Wednesday, May 11, 2016

Disrupting SCADA and Beyond

SCADA or Supervisory Control and Data Acquisition is a system for monitoring and control of industrial processes and complexes from power generation, to heating and ventilation, to manufacturing and beyond.

Today, there are a host of vendors providing these systems and most of them have evolved from massive scale-up systems to smaller collections of networked sub-systems.  But, most of them are large proprietary systems and that are expensive and complicated to deploy.

This means SCADA vendors and the industrial telemetry and automation market is ripe for disruption.  Established vendors are clearly rushing to evolve their portfolios and build a more modern, Cloud and IoT based solution.  But, there is also a startup that has the potential to eat their lunch - Samsara.

What I love about Samsara is the how clean, clear and focused the company is.   Their products are "apple-like" in their elegance and simplicity.  And, they have four very clear initial use cases - Fleet Management, Industrial Monitoring,  Cold Chain Monitoring and Power Monitoring.

They provide a plug-and-play cellular gateway to stream sensor data from the site / vehicle to the Samsara cloud.  This gateway has proprietary software for added security and reliability.  They have applications that run on their cloud and provide visibility to devices and analytics of the data.

They also provide a range of sensors that can send data to the gateway.  These sensors can measure temperature, humidity and shock.  They have power monitors for measuring power consumption and efficiency. Lastly, Samsara can interface with third party sensors to monitor additional devices and data.

It's still early days for Samsara, but they raised $25M from Andreessen Horowitz last year and Marc Andreessen is on their board.  The founding team was behind Meraki, purchased by Cisco for $1.2B in 2012.  They clearly have the people, resources and experience to win.

Their aspiration is to make it so simple to deploy and analyze sensors that they can be broadly deployed for all kinds of use cases.  That is an exciting and big vision.  It's far bigger than disrupting the SCADA space, but that's probably one of the first steps.

Tuesday, May 3, 2016

From Consumer IoT To The Enterprise

Many of the most useful applications and interesting disruptions of the last several years have to do with a combination of the Internet of Things and new cloud-mobile applications.  The initial targets were primarily consumer-based and creators of these new devices and applications were building a new business to go after this space.

Consider four of best known disruptions in the last several years...

In the case of Fitbit and Nest, it's easy to see how they built a new smart device and an application that allows the consumer to control the device and analyze the data.  There is a lot more data being created by these devices than they are exposing to the end user, but a key reason these companies have been successful is that they surface all that data through a consumer-friendly cloud-mobile application that makes sense of the data and solves a customer need.

In the case of Waze and Uber, it's a little harder to see the IoT play because their smart device is not available yet - a car that drives itself or a car that senses traffic, obstacles and police cars.  So, for now, these companies have built applications on both sides.  One to create the data and the other to analyze and surface that data in a useful way.

The things that all four companies have in common is that they built the platform required to run these IoT and cloud-mobile apps, for their specific purpose.  They are building a business around these devices/apps and competing in the war for technology talent capable of building that platform.

As this IoT and cloud-mobile apps disruption is embraced by enterprises, there will need to be a core platform that supports IoT.  We need a new picture that reflects the enterprise and industrial IoT, different from the consumer IoT.

At the core of it is an IoT Data Platform.  This platform would likely cover four areas: Data Capture, Data Management, Data Security and Data Consumption.  Data Capture is a set of interfaces to pull/receive data from the things and send it to the infrastructure to be stored.  Data Consumption is a similar set of interfaces that will allow applications to get access to the data stored and managed by the platform.  Both of these functionality areas need to support broad access and the maximum possible compatibility with devices and application use cases. For Data Capture and Consumption, accessibility and compatibility trump proprietary value-add, so this is an area ripe for open source software and community-driven development.

The Data Management and Data Security layers are to, well, secure and manage this data.  Different data will have different value and different requirements for performance, retention, security, etc...  Data Management and Data Security is an area where technology vendors are likely to build "special sauce" that they want to monetize, so it will be interesting to see if these layers are as readily open-sourced.

Lastly, there needs to be a highly elastic, low cost, high performance infrastructure to power this platform and the data.  That is what people today call "cloud infrastructure", but that doesn't necessarily mean public cloud.  As the traditional infrastructure players build new technologies for this digital world, expect enterprises to choose to mix on-premise cloud infrastructure with off-premise cloud infrastructure to power their IoT deployments.

Some vendors will try to deliver this entire stack from IoT platform to infrastructure.  IBM seems to be headed this way.  Other vendors can offer a compelling solution for one part or the other.  Splunk seems to have a great opportunity in the IoT platform space and GE Predix has launched into this space.  Companies like EMC and Cisco are moving into the Cloud Infrastructure for IoT space.

As IoT grows more mainstream and digital transformation moves to the classic enterprise and industrial companies, this IoT platform space will quickly evolve.

Monday, April 25, 2016

The Next Wave Of Disruption

In the last several years, there has been a lot of excitement and wealth generated by the startup companies that are disrupting traditional industries. The obvious example is Uber and it's impact on the taxi and livery industry.  And there are many more - Tesla, Nest, Salesforce.com and the list goes on.

In each of these cases, the startups digitally transformed an existing industry.  At first glance, people focus on the application transformation - a new cloud/mobile app and a new business model that create a monumental shift in value for consumers and, in return, creates incredible value for the entrepreneurs and financiers.

In reality, most of these cases are about the combination of "the internet of things" (IoT) and a new application, paired together to create the outcome.  In the case of Tesla and Nest, that's easy to see.  They created a new, smarter thing and gave you an app.  The transformation was instantaneous.

What's harder to see is that the IoT connection is far deeper than that.  I would call Uber an IoT play.  Now, in that case, they didn't create a new device, they made an existing device network-connected - the black car, the taxicab, etc...  They did it by putting a mobile phone inside the device with a new app, operated by a human. When self-driving cars arrive, you can bet the humans will be out of the picture (more here) and the IoT connection will be clearer.

So, that's the last several years.  Next up is a massive wave of traditional companies combining IoT and new applications to create a quantum leap in value for their customers and for their companies and shareholders.  This will happen when the companies that already have an incredible network and penetration of "things" start harnessing those things and building new apps to work with them.

Consider how Coca-Cola is turning their vending machines into connected things (more here).  It allows users to create custom flavors and for Coca-Cola to learn from that.  Imagine what else they can do to create customer value - recognize you when you approach the machine, save your flavors, publish them to your network of friends, try your friend's flavors, vote on the best new flavors. To win, a startup has to invent a better soda franchise, then build some cool technology.  That might be tough.

GE is doing this with it's engines - changing the business model, delivering more value to their customers, lowering GE's cost and growing revenue (more here).  GE has many more industries with intelligent industrial equipment and this is a market that startups will likely not disrupt any time soon.

There are a lot of very exciting startups in Silicon Valley and around the world.  They will continue to do great things and create lots of exciting new applications that transform out lives.  But, what I am really excited about these days is the transformation that is coming to the existing market leaders and their competitors who are going to leverage IoT, the cloud and mobile apps to disrupt their own industries.