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Ron Rock is CEO and co-founder of Microshare Inc., a data-sharing and governance firm based in Philadelphia that just closed a $5 million funding round and graduated from Microsoft’s ScaleUp program.
Smart cities. Smart cars. Smart buildings. We hear a lot of “smart” thrown around in today’s business models and use cases, and indeed in many instances this is justified. Sensors are appearing on dogs and cats and even kids. Your Fitbit is encouraging heart health, your Waze app advising the opposite, warning you that you’re passing up a chance to eat at the Cheesecake Factory.
But for me, the truly deep vein of data value being generated by all these sensors is in infrastructure, where every manhole cover, conference room table, locomotive compressor, jet engine, refrigerator door, soap dispenser, and many, many more maintenance-reliant entities will soon be instrumented. Efficiency and risk management is being transformed in leaps and bounds.
So our cars have a lot of sensors in them, jet planes have a lot of sensors in them, and there are lots of sensors and data in manufacturing. Yet we as consumers living our everyday lives feel somewhat isolated and removed from that. Partly, this is because of the cost of transmitting data from battery-operated sensors to wifi or cellular networks has prevented the revolution from being televised - or from reaching our Twitter feeds.
All that's about to change with low-power, wide-area network sensors. LP-Wan, as this is known, is now being deployed around the country to enable $10 sensors with ten-year battery lives to transmit endless streams of data for about $1 a year. Experts believe some 50 billion sensors will be deployed in the next five years. Imagine the potential. And the ubiquity.
But also the problems. It’s one thing for an 18-wheeler to collect data on the condition of each of those 18 tires and wheels; it’s quite another for your auto manufacturer or car dealership or local transmission shop to collect data on your driving habits. That data is valuable, not only to the merchants and companies involved, but in the larger marketplace for data as a potential revenue stream. But who owns it? And how can the complex transactional dynamics involved be tracked, governed and ultimately monetized without creating legal and compliance nightmares?
That’s where Microshare comes in. My team and I have been together for the last 20 years and have spent our entire careers thinking about data - how to bring data together from lots of large organizations around the globe, all with different regulatory constraints or different data formats.
The last 10 years has seen the rise of a whole new paradigm of data with cloud computing. So suddenly now I've got iOS and Android. I've got tablets. I've got the whole mobility world wanting to access data. I've got data still behind the firewall. I've got data in the cloud. And the question that we asked ourselves is, how do I devise a system to bring all that data together to create value? How do I ingest, store, and share the right data at the right time, with the right entity, with complete control, audit-ability and compliance.
Our solution embraces rather than fights the complexity of this new ecosystem. There is no fighting it, after all. It will evolve, mutate and continue to change as innovation follows innovation. As such, we have developed a 21st century framework for data ownership that accounts for reality - the multi-tenant ownership and individual data rights that the modern digital economy demands.
In today’s commercial and regulatory environment, with privacy and security fears raising the reputational and legal stakes for anyone generating, using, buying, or selling data, a more complex data overlay is required to prevent abuse and empower data owners to reap the rich insights and high potential revenue streams from their digital assets. We believe this will require a new framework for data ownership based on four separate ownership levels:
1. the Data Originator;
2. the Primary Data Owner;
4. Enabled Parties.
Each level of data ownership would be endowed with specific roles, permissions, and limitations and overstepping those boundaries without express consent of the appropriate data owners further up the chain would trigger alerts and, ultimately, expose the violator to financial or legal consequences. Inside our particular data governance platform, this data is managed through the entire ownership cycle at scale and with complete auditability.
Over the past few years, we have socialized these ideas with some of the largest technology firms on the planet, including IBM, Microsoft, Orange and AT&T. We even spoke with Facebook - over a year before its share price melted down due to its own hapless management of data. Sadly, they just weren’t interested. Happily, others had more foresight. As a matter of fact, we just graduated from Microsoft’s “ScaleUp” program for startups and are selling our solution through their vast global sales team.
So as we think about how this applies to infrastructure, let’s consider facilities management. We hear every day that the world’s leading facilities management companies are busy outfitting and retrofitting their buildings with IoT sensors. Depending on whose research you read, (there) will be anywhere from 20 to 50 billion sensors on the planet in the next five years. All of those sensors - no matter how benign the data may be, data around whether trash cans are full or not, soap dall soap dispensers, whether conference rooms are full or parking spaces occupied - all of it still needs to be locked down. As amazing as it may seem, even a mundane sensor is still a target for cyber terrorism or economic espionage.
Progress demands that we want to bring all of those different disparate pieces of data together to create a smart building, or even better, what we call a ‘sentient building,’ one that not only enables enormous maintenance efficiencies and resource and safety improvements, but also could order its own replacement parts - air conditioner filters, toilet paper, door hinges - by having a granular digital sense of what is and what is not ready for replacement. This means bringing all that data together to feed AI and machine-learning engines, all without crossing the various data privacy regulations that have come into effect, like GDPR in Europe, or those that look increasingly inevitable thanks to Facebook and others in the United States.
How do I think about bringing that data together? And be able to share it at the right time, with the right entity, with that complete control audit ability and compliance? That's what true data management needs to do. Bringing savings in maintenance and liabilities is one step. Avoiding the regulatory risk of violating personal or other data laws is another. And only by doing both well can any organization truly maximize the enormous potential value of the data that all these sensors collect by sharing and ultimately selling that data to what we call “Enabled Parties,” interested third parties, who have the micro-contracted proof of their rights to use that data in very specific ways.