By now, the statement ‘Data is the new oil’ has become a cliché. Amin Vahdat of Google said this, “The amount of bandwidth that we have to deliver to our servers is outpacing even Moore’s Law. Over the past six years, it’s grown by a factor of 50.” Science Daily reported in 2013 that 90% of world’s data were generated over the previous two years. When there is sufficient data, new insights can be mined that businesses can take advantage of. You may have heard of the infamous case where retail giant Target sent a teenage girl promotions for diapers to her home after algorithms determined she was pregnant. The big problem was Target knew before her dad knew.
Venture Capitalists investing in networked markets or platforms look at network effects as an ‘unfair advantage’. In network effects, the more the users of your product, the more valuable the product is. Facebook, Uber, AirBnB, Quora, LinkedIn etc. operate on network effects. You use LinkedIn only because others use it and as a result, there is no scope for a second big player in the world of platforms – the Winner takes it all. So, the higher the network effects, the more lucrative the investment from a Venture Capitalist’s point of view.
One of the best performing venture capital firms – Union Square Ventures had for a long time, the following thesis to guide their investments (they modified it in December last year):
Large networks of engaged users, differentiated through user experience, and defensible through network effects.
Matt Turck of FirstMark Capital made an interesting argument called Data Network Effects. Data can be a ‘competitive moat’ – an entry barrier. The more data you have, the better you will be than your competition. Why? More customer data, more correlations, more insights and understanding about your customer, the better will be your product which will lead to more customers and which in turn will lead to more data and the cycle repeats. Startups and not only giants like Facebook and Google are able to benefit because of democratization of Big Data tools and machine learning/AI.
And new data is not only coming from the traditional point-of-sale machines or from your mobile phones. Some interesting startups are building their businesses around new data sources.
Orbital Insight hopes to create market intelligence for decision makers and investors in industries like Retail, Agriculture, Real Estate, Commodities etc. Sounds like yet another consulting company with suited MBAs? Wrong. Orbital Insight applies machine learning on satellite images to glean new insights. It sources images from DigitalGlobe – a provider of commercial high-resolution Earth imagery. The startup uses its machine learning algorithms along with the power of hundreds of computers running on the cloud on millions of images to come up with interesting insights.
Its CEO James Crawford explains one example:
For example, we’ve used convolutional neural networks to count cars. We had humans mark a few hundred parking lots, taking a mouse and putting a red dot on all the cars. That provides the triggering mechanism for the neural network, and then the neural network learns what a car is. Then we processed a million parking lots through the neural networks and counted millions and millions of cars in those images.
The startup correctly predicted that Home Depot was going to have a blow-out quarter by looking at car counts during the past five years (the counting and analysis was automated of course).
The startup is in Mr. Crawford’s words, intent on answering:
…big-picture questions such as “How has the United States recovered from the recession in the last five years?” or “How did it recover in one state versus another?” or “How did Target do versus Walmart?” In other applications we’re looking at how China is growing, how many new building are going up in cities across China and how different regions are doing. It’s all about automating the algorithms so we can process thousands and thousands of images in parallel and get a sense of what’s happening on Earth on a macroscopic scale.
Are you a property owner trying to sell your house or an agent trying to close real estate listings? You can book a peculiar kind of service with a startup called Drone Base. Drone Base ‘pilots’ will fly UAVs (unmanned aerial vehicles) or drones around your property and nearby areas and you can then download the images for listings as well as analysis.
The below video is a pilot flying a drone capturing the neighborhood and surrounding areas around the property.
Here again data will be the main course of a startup like Drone Base. Eventually it is going to use the high resolution videos and images to provide services that will be very different from just listing properties. A whole lot of high resolution images can be classified and analyzed.
Farmers Business Network (FBN) is sells membership of $500 to farmers in the US. Farmers of a region join the platform and contribute images and other data. With a lot of data aggregated and analyzed, FBN provides insights on the farm owner’s fields. No more just relying on advice from the seed company or fertilizer company.
Interestingly, FBN just collects $500 life time membership fee for now. Venture backed, it is going to have a way to use its data for additional use cases in the future.
Hampton Creek is an ambitious food startup that wants to rethink food from the ground up. The company swaps meat based proteins for plant based ones while trying to retain the taste. The company currently has three products – Just Mayo, Just Cookie Dough and Just Cookies (all egg free products) and has distribution deals with Walmart, Safeway, Target and Costco. The company achieves better results because of its database of thousands of species of plants and uses machine learning algorithms to model likely ingredients in its products. The company is at the intersection of plant biology and machine learning (and good branding).
Microfinance loans are expensive, the average interest rate in 2011 being a huge 35%. The small size of the loans, high risk of default and huge operational costs push up the interest rate for those who are most in need of credit. FirstAccess is a startup in different countries in Africa that wants to solve this problem. FirstAccess partners with Telecom companies and Microfinance institutions. The company uses mobile phone data which contain demographic, geographic, financial and social data and prepares fast and actionable credit scores.
A typical recommendation is like the above and the credit is disbursed within minutes. No need for expensive loan officers.
Urban Engines wants to improve urban mobility with a very clever solution. The app uses the phone’s accelerometer and magnetometer sensors and recommends the routes one must take and the likely traffic through different routes. The company identifies traffic by improving accuracy on moving data – movements of buses, cars etc. The mapping service by the company uses Augmented Reality.
The consumer app is free but the data collected can potentially gold for Urban Engines. The company has a solution for Businesses to help them plan their fleet and logistics. The company also has solutions for city administrations who have to grapple with overcrowded routes every day and allows them to make smart decisions.
Another startup that targets city administrations is Veniam. Co-founded by the founder of Zipcar, the startup claims to develop the networking fabric for ‘Internet of Moving Things’. The ambitious startup attempts to blanket entire cities in seamless Wi-Fi. Basically, the engineers tap into a city’s existing internet infrastructure and at many points place wireless routers. Then a fleet of public transportation – buses, garbage trucks, police cars and the like are fitted with Veniam’s NetRider routers which receive wireless signals from access points, creating hotspots on the move. It creates a mesh network that covers the entire city and the public needs one login and no hot-spot hopping.
Veniam claims its technology offers 10x the range of standard WiFi, 100x faster connection setup and 12 lower cost per GB than cellular. Interestingly, the data transfer is two-way and data like location of pot-holes, congestion points etc. are logged to the cloud for later analysis. Though now the service focuses on providing public Wi-Fi, this can be used in the future by cities for things like city planning, security or more controversially for surveillance.
With new data sources and the possibility of data network effects, tech companies will increasingly become gatekeepers of unique data sets.
Nobody knows more about your interests, preferences, concerns (and a whole lot of things) than Google. Nobody knows more about your social connections (also called the Social Graph) than Facebook. Nobody knows more about your professional connections and networks more than LinkedIn (the Professional Graph). In fact, LinkedIn conducted an experiment on Kurt Wagner of Re/code to predict where he would be professionally five years down the line. LinkedIn compared his profile with its database of 300 million users to find users with similar skillsets and career paths.
These gatekeepers in my opinion will be highly valuable hence justifying their current high valuations. Therefore, logically new startups tapping into new data sources will be a lucrative investment, if they are going to be gatekeepers of unique data sets. And this will push more business models to specifically include a data strategy.
The genetic testing startup 23andMe is a great example of this. The startup mails to your home a simple equipment to collect your saliva sample. The company is cleared for some tests like Bloom’s syndrome and it also helps track your ancestry. The sample is then mailed back to the company. Meanwhile you set up an account on their website. The company tests the DNA from the sample and you will get the results on your account. 23andMe will potentially be the gatekeeper for another unique data set – your DNA. Nobody will know more about your DNA than 23andMe. The company already has partnerships with Pfizer and Genentech which will access its data.
Meanwhile startups and tech giants will continue to fight for unique data sets – both to make the product better and be a gatekeeper. And investors will find them to be great investments.