Not BI, AI

A product business can double its revenue and quadruple its margins by moving to a service business. What is service? It's information, personal and relevant to you.  

Amazon delivers information that is personal and relevant to you, for example, with its recommendations: customers like you bought this book, or customers like you like this music. Now think about your favorite banking site and log in. I will contend that there’s very little personal and relevant information. The only reason you’re being asked to log in is for security reasons. After that you are really looking at a big shopping cart to move money from savings to checking, buy stocks, sell a bond, etc. 

Could the bank deliver information that’s personal or relevant to you? Could they say that people like you bought this stock, or people like you re-financed their mortgage? Yes, they could, so why don’t they? Well, you probably never thought about this, but the consumer Internet that Google and Bing let you see through search is believed to only be about 100 or 200 terabytes. That’s it. Now, I’ll guarantee your current IT systems have 10, 100, or 1,000 times that amount of information; so why can’t they deliver information that is personal and relevant to you? Well, I say they are held hostage by the SQL monster. So let’s just have a little fun here.

It’s the late ‘90s and I have several SQL engineers in the room. I come in with a brilliant business idea. My idea is that we are going to index the consumer Internet and we’re going to monetize it with ads. We’re going to be billionaires! Just guess what the SQL engineers would do?

The first thing they’re going to do is design a master, global-data schema to index all information on the planet. The second thing they’re going to do is write ETL and data cleansing tools to import all that information into this master, global-data schema. And the last thing they are going to do is write reports, for instance, the best place to camp in France or great places to eat in San Francisco.

Any of you who are technical are probably laughing right now thinking, “Well that’s a completely stupid thing to do.” But if you try and attack the problem using SQL and BI tools, you’re also going to fail.  

Furthermore, as you connect your machines, you have the opportunity to bring in large amounts of time-series data. Modern wind turbines have 500 sensors and the ability to transmit those sensor readings once a second. Most analytic techniques depend on the idea that the data scientist can try and visualize the data, but how is that possible if I have a 1,000 wind turbines and data for 12, 24 or 36 months?  How can we learn from that?

Artificial Intelligence (AI) has been increasingly in the news. Google’s DeepMind made headlines when the machine, AlphaGo, programmed to play Go, defeated Lee Sedol, one of the best players in the world, by 4 - 1. Amazon’s Echo and voice assistant Alexa is being widely praised for its voice recognition capabilities, and many people remember how Watson handily beat the best Jeopardy players in the world.

Things have been changing quickly and here is a great example. ImageNet is a database of millions of images. Beginning in 2010 the ImageNet Challenge was established to see how well a machine would do at object recognition. As a point of reference an average person will be able to achieve 95% accuracy. In 2010, the winning machine could correctly label an image 72% of the time. By 2012, accuracy had improved to 85%, and in 2015 the machine achieved 96% accuracy.

So why have things been changing so quickly?

First, we’re continuing to get more computing and more storage for lower and lower prices. Next generation compute and storage cloud services can provide thousands of computers for an hour or a day. AI and machine learning software require lots of computing during the learning phase. The second reason is the emergence of neural network algorithms. Third, it’s not possible to apply these advanced AI technologies without data, and lots of it. Consumer Internet companies like Facebook are able to use billions of photos to train facial recognition systems. AlphaGo learned from millions of games of Go and Alexa learned from millions of voice patterns.

While we’ll continue to see progress in replicating what humans do, we have the opportunity to apply these AI technologies to even more important challenges. Today, many of the machines that generate electricity, transport goods, farm food, or sequence genes have large amounts of data. If we were able to connect these machines and collect the sensor data from them, we would have the opportunity to use AI and machine learning technologies to operate a more precise planet. Imagine a future farm that can use fewer pesticides, which not only reduces the cost of the food, but also makes it healthier. A future power utility could be based on a vast array of solar panels, wind turbines, small hydro generators and batteries to generate more power, much more efficiently. A pediatric hospital could share the results of millions of MRI scans and diagnose patients far faster.

Next-generation machine companies could not only double their revenues and quadruple their margins, but build a better planet in the process.

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Timothy Chou, Ph.D.

Timothy Chou has lectured at Stanford University for over twenty-five years and is the Alchemist Accelerator IoT Chair.  Not only does he have academic credentials, but also he's served as President of Oracle's cloud business and today is a board member at both Blackbaud and Teradata. He began his career at one of the first Kleiner Perkins startups, Tandem Computers, and today is working with several Silicon Valley startups including as the Executive Chairman of Lecida, which is building precision assistants for the IoT using AI technologies. Timothy has published a few landmark books including, The End of Software, and Precision: Principals, Practices and Solutions for the Internet of Things, which was recently named one of the top ten books for CIOs.  He's lectured at over twenty universities and delivered keynotes on all six continents.

Not Machines, It’s the Service

If your company builds agricultural, power, construction, healthcare, oil, gas or mining machines you’ve probably heard about the Internet of Things.  All of us in the tech community are excited to tell you about our cool technology to run on your machine, connect it to the Internet, collect data from it, and then make predictions from that data using advanced machine learning technology.

But maybe the question you’re asking as the CEO of one of these companies is why should I care?  Isn’t this just stuff my geeky R&D staff cares about? How can it be meaningful to my business?  

I’ll be making the case that with IoT software; you can not only double the size of your business but also create a barrier that your competition will find difficult to cross.

Next generation machines are increasingly powered by software.  Porsche’s latest Panamera has 100 million lines of code (a measure of the amount of software) up from only 2 million lines in the previous generation.  Tesla owners have come to expect new features delivered through software updates to their vehicles.  Healthcare machines are also becoming more software defined. A drug-infusion pump may have more than 200,000 lines of code and an MRI scanner more than 7,000,000 lines. On a construction site a modern boom lift has 40 sensors and 3,000,000 lines of code and on the farm a combine-harvester has over 5,000,000 lines of code.  Of course we can debate if this is a good measure of software, but I think you get the point.  Software is beginning to define machines.

So if machines are becoming more software defined, then maybe the business models that applied to the world of software will also apply to the world of machines. Early in the software product industry we created products and sold them on a CD; if you wanted the next product, you’d have to buy the next CD. As software products became more complex, companies like Oracle moved to a business model where you bought the product (e.g. ERP or database) together with a service contract. That service contract was priced at a derivative of the product purchase price. Over time, this became the largest and most profitable component of many enterprise software product companies.  In the year before Oracle bought Sun (whilst they were still a pure software business) they had revenues of approximately $15B, only $3B of which was product revenue, the other $12B, over 80%, was high margin, recurring service revenue.

In the world of machines, you might wonder why General Electric is running ads on Saturday Night Live talking about the Industrial Internet.  Why are they doing this?  All you need to do is download the 2016 10-K (http://www.ge.com/ar2016/assets/pdf/GE_2016_Form_10K.pdf) and look on page 36.  Out of $113B in revenue they recognized $52B, or nearly 50%, as service revenue.  Imagine if GE could move to 80% service revenue, not only would the company be tens of billions of dollars larger, but also margins for the overall business could easily double. And let me remind you this is all done without connecting the product (software or machine).  Once connecte you can provide even more service and ultimately deliver your product as a service.  As we have already seen in high tech software and hardware moving to product-as-a-service is transformative.

So if you’re an executive at a power, transportation, construction, agriculture, oil & gas, life science, or healthcare machine company, how big is your service business?

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Timothy Chou, Ph.D.

Timothy Chou has lectured at Stanford University for over twenty-five years and is the Alchemist Accelerator IoT Chair.  Not only does he have academic credentials, but also he's served as President of Oracle's cloud business and today is a board member at both Blackbaud and Teradata. He began his career at one of the first Kleiner Perkins startups, Tandem Computers, and today is working with several Silicon Valley startups in roles from investor to executive chairman. Timothy has published a few landmark books including, The End of Software, and Precision: Principals, Practices and Solutions for the Internet of Things, which was recently named one of the top ten books for CIOs.  He's lectured at over twenty universities and delivered keynotes on all six continents.


Is it Time to Invest in IoT?

I published my first book, The End of Software, in 2004. At the time, I was president of Oracle On Demand, which served as a starting point for Oracle’s billion-dollar cloud business. In the book I discussed the fundamental economic reasons software should be delivered as a service.

As an example of new startups in the field, I discussed four companies, VMwareSalesforce,NetSuite and OpenHarbor. None of them were public companies when the book was published. Salesforce was still under $86 million in revenue. While I didn’t get all four correct, three of the four have gone on to be major companies driving the second generation of enterprise software.

It’s 12 years later. Some have said that enterprise software is a mature business; CEM, ERP, HR and purchasing software are now all being delivered as a cloud service. So is it the end?

I don’t think so. While second-generation software has helped reduce the cost and improve the efficiency of some enterprises, it has done little to transform our physical world. Power, water, agriculture, transportation, construction and healthcare have barely been touched. But that’s about to change.

Industrial machines or enterprise things are increasingly being instrumented and connected. John Chambers, former Cisco CEO, says 500 billion things will be connected to the Internet by the year 2025. While you may question that, we already know 100,000 wind turbines are connected with the capacity to send 400 sensors’ worth of data every five seconds. So we’re going to end up with a lot of smart, connected things.

Unfortunately, all our connection, collection, analysis, learning, middleware and application technology has been built to support applications for the Internet of People. Things are NOT people. Things exist where people aren’t. Things have much more to say and things talk much more frequently. A Joy Global coal-mining machine has vibration sensors that sample 10,000 times per second. We need a new generation of enterprise application, middleware, analytic, collection and connection cloud service products to build precision machines for mining, transportation, healthcare, construction, power, water and agriculture.

Some have begun to make the investments. GE Software was founded in 2011 with a $1 billion investment. CEO Jeff Immelt has declared that GE needed to evolve into a software-and-analytics company, lest its industrial machines become mere commodities. Immelt has set an ambitious target of $15 billion in software revenue by 2020. GE plans to achieve this through its new Predix software platform under the leadership of CEO of GE Digital, Bill Ruh.

PTC has taken an M&A path and invested more than $400 million in a series of companies: ThingWorx for $112 million, a $105 million acquisition of ColdLight andAxeda for $170 million. On the venture side you may not have noticed, but Uptake, a Chicago-based IoT startup, beat Slack and Uber to become Forbes 2015’s Hottest Startup. They raised $45 million at a $1 billion post-funding valuation.

I’ll let you be the judge of whether it’s time to invest in IoT. But if you’re an early-stage or even late-stage investor, it would be wise to be a student of this area as it promises to create as big a disruption as the second generation of enterprise software. And if you’re a startup with a vision to build products for things, not people, get started. Maybe in 12 years we’ll talk about you like we now talk about VMware, NetSuite and Salesforce.

- Tim Chou is the former president of Oracle On Demand, a computer science lecturer at Stanford and chair of the IoT Track of the Alchemist Accelerator. His book, Precision: Principles, Practices and Solution for the Internet of Things, will be released in May.