The industry is distributing corporate applications towards decentralized data. The figure below does a good job in framing the challenge of "delivering trusted data" in decentralized ecosystems (e.g., IoT sensor data).
As applications (top right) and IoT sensor data (bottom left) are being brought together across disparate and heterogeneous components, trust is owned by nobody. This brings great risk to the companies that deploy the applications. Applications are designed to positively impact balance sheets, but accessing decentralized IoT data can result in negative economic impact:
- These applications are often meant to save money by improving operational efficiencies. If the data is incorrect, wrong, or malicious, the applications can actually increase expenses (e.g., by making incorrect predictions).
- Improperly labeled IoT data can be accessed by applications that have no business looking at that data. The violation of corporate or federal compliance laws can lead to large corporate fines.
- The rise in data marketplaces has the potential to bring new sources of revenue to many companies. If the data, however, is of poor or questionable quality, or if ownership is not clearly established, the potential payment price for that data will decline (or the data will not eligible for sale).
What can we learn from the successes of the enterprise approach to "delivering trusted data"? Can these lessons be applied in an IoT context?
Certainly within the walls of a corporate data center we see a high degree of trust being delivered between enterprise-class storage systems and corporate applications.
One reason for the synergy between enterprise data and applications is trust insertion. Consider the evolution of storage area networking, and how multiple "trust insertion techniques" were applied throughout the data delivery process, starting at the surface of the disk (520-byte sectors) all the way up to data validation at an application (validating hashes through content addressing). These trust insertion techniques allowed applications to deliver great business value without having to worry about the correctness, timeliness, or availability of the data.
This diagram highlights one dozen trust insertion techniques that facilitated the delivery of trusted data.These trust insertion techniques provided benefits like data integrity and correctness, availability, privacy, and deterministic delivery. Nearly all of these capabilities were "proprietary" or "closed". But data center operators didn't mind; they paid handsomely for proprietary trust insertion solutions to the tune of 100s of billions of dollars over many decades.
So what hypothesis can we bring forward to the IoT use case? Let's assume that the industry would accept (and value) trust insertion techniques that bring applications together with decentralized IoT data. Let's also assume that any "trust insertion" latency delays could be minimized with performance optimizations.
Can we build such a stack?
The answer is yes, but with one caveat: there is no way that one proprietary solution can own all of the trust; instead we need to weave together as many open "trust insertion" technologies as possible to help bridge the gap across the heterogeneity of an IoT ecosystem.
In future posts we will discuss how to build such a stack.
Steve
Dell Technologies Fellow
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