As always, the MIT Sloan CIO Symposium covered a lot of ground. Going back through my notes, I think it’s worth highlighting a couple sessions in particular—in addition to the IoT birds of a feather that I led at lunchtime. They all end up relating to each other through data, data security, and trust.
Big Data 2.0: Next-Gen Privacy, Security, and Analytics moderated by Sandy Pentland of the MIT Media Lab
There were two major themes in this panel.
The first was that it’s not about the size of the data but the insights you get from it. This is perhaps an obvious point but it’s fair to say that there’s probably been too much focus on how data gets stored and processed. These are important technical questions to be sure. But they’re technical details and not the end in itself.
I might be more forgiving had I not lived through the prior data warehousing enthusiasm of the mid- to late-1990s. As I wrote five years ago: "There are many reasons that traditional data warehousing and business intelligence has been, in the main, a disappointment. However, I'd argue that one big reason is that most companies never figured out what sort of answers would lead to actionable, valuable business results. After all, while there is a kernel of truth to the oft-repeated data warehousing fable about diapers and beer sales, that data never led to any shelves being rearranged."
However, the other theme is newer—or at least amplified. And that’s ensuring the security of data and the privacy of those whose data is being stored. One idea that Sandy Pentland discussed is the idea of sharing answers (especially aggregated answers) rather than raw data. See enigma.mit.edu as an example of a system that's designed to make it possible for parties to use and maintain data without having full access to that data. Pentland also noted that because systems such as this make it possible to securely ask questions across jurisdictional boundaries, they could help address some of the often conflicting laws about the treatment of personally identifiable information.
Getting Value from IoT
At my luncheon BoF table, we had folks with a diverse set of IoT experiences including Ester Pescio and Andrea Ridi of Rulex Analytics, Nirmal Parikh of Digital Wavefront , and Ron Pepin, a consultant and former Otis Elevator CIO. The conversation kept coming back to value from data. What data can you gather? What can you learn from it? And, critically, can you do anything with that data to create business value?
Per my earlier comment about data warehouses, gathering the data is relatively straightforward. It may not be easy, especially when you’re dealing with sensors that aren’t on your own property and therefore need dedicated networks of some sort. But the problems are mostly understood. It’s “just" a case of engineering cost-effective solutions.
But what data and what questions? Ron Pepin shared his experiences from Otis. Maintenance is a big deal for elevators. It’s also the main revenue stream; the elevators themselves are often a loss leader. Yet proactive elevator maintenance mostly consists of preventative maintenance on a fixed schedule.
It seems like a problem tailor-made for IoT. Surely, one can measure some things and predict impending failures. But it’s not obvious what combination of events (if any) are reliable signals for needed maintenance. There’s a potential for more intelligent and efficient maintenance but this isn’t a case where you can cost effectively just instrument everything—someone else owns the building—and the right measurements aren’t obvious. Is it number of hours, number of elevator door reversals, temperature, load, particular patterns of use, something else, or none of the above?
Given the level of hype around blockchain, perhaps the most interesting thing about this panel by Christian Catalini of MIT Sloan was the the lack of such hype.
Interest, yes. Catalini described how blockchain is an interesting intersection of computer science, economics & market design and law. He also argued that it can not only make things today more efficient (which could potentially redefine the boundary of firms by reducing transaction costs) but also create new types of platforms.
That said, there was considerable skepticism about how broadly applicable the technology is. Anders Brownworth of Circle (which has a peer-to-peer payment application making use of blockchain) said that the benefits of blockchain are broadly in the area of time-based transactions, with interoperability, and with many able to audit those transactions. However, with respect to private blockchains outside of finance, “we trust all the people around the table anyway” and, therefore, the audibility that’s inherent to blockchain doesn’t buy you much.
In the same vein, Simon Peffers of Intel agreed that it’s "hard to let thousands of users have the same view of data with a traditional database. But some blockchain use cases would fit with traditional database.” He added that "There is a space for smaller consortiums of organizations that know who the parties are with other requirements that can be implemented in a private blockchain. Maybe you know who everyone is but don't fully trust them."
To sum up the panel: You’re usually going to be giving up some features relative to a more traditional database if you use blockchain. If you’re not making use of blockchain features such as providing visibility to potentially untrusted users, it may not be a good fit.
Photos (from top to bottom):
Sandy Pentland, MIT Media Lab
Anders Brownworth, Principal Engineer, Circle