IoT
Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World
The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore, such as managing large remote workforces – many of whom are new to working remotely and managing risk while attempting to conduct “business as usual.” Unfortunately, most businesses’ systems, processes, and internal controls were not designed for this “new normal.”
While there have been many predictions around Blockchain for the past few years, it is still not widely adopted. We are beginning to see an uptick in adopting Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. However, large-scale adoption has been elusive to date.
I believe we will soon see major shifts in mindset, investment, and effort toward modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, which will lead to faster adoption by SMBs and other non-enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.
Here are a few predictions:
- New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel innovations across industries, including Biotech and Pharma.
- Blockchain can provide data privacy, ownership, and provenance to ensure the data’s veracity.
- New legislation will be created to protect medical providers and other users of that data from being held liable for missing information or trends that could have saved lives or avoided other negative outcomes.
- In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using Medical IoT data, which could include Smart Contracts to ensure compliance (assuming a benefit is provided to the data providers).
- Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. I wrote about this in 2013.
- Data Governance will be taken more seriously by every business. Today, companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
- Comprehensive Data Governance will become a driving and gating force as organizations modernize and grow. Even before the pandemic, there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
- In a business environment where more systems are distributed, the risk of data breaches and Cybercrime Increases. That must be addressed as a foundational component of any new system or platform.
- One or two Data Integration Companies will emerge as undisputed industry leaders because of their capabilities in MDM, Data Provenance and Traceability, and Data Access (an area typically managed by application systems).
- New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems becomes even more important than it is today.
- Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
- Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
- This will also foster the growth of next-generation Business Ecosystems and more dynamic collaborations.
- Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).
All in all, this is exciting from a business and technology perspective. Most companies must review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.
The steps we take today will shape what we see and do in the coming decade, so it is important to get this right quickly, knowing that whatever is implemented today will live, evolve, and hopefully improve over time. Don’t wait for perfection, as the risks are too high.
IoT and Vendor Lock-in
I was researching an idea last weekend and stumbled across something unexpected. My view on IoT has been that it provides a framework for a rich ecosystem of hardware and software products and their use. That flexibility and extensibility foster innovation, which in turn leads to greater use and adoption of the best products. It was quite a surprise to discover that IoT was being used to do just the opposite.
My initial find was a YouTube video about “Tractor Hacking” that lets farmers make their own repairs. That seemed like an odd video to appear in my search results, but it made sense about halfway through. The video discusses not having access to software, replacement components not working because they aren’t registered to that tractor’s serial number, and the only alternative being costly transportation of the equipment to a Dealership to have a costly component installed.
I initially thought there had to be more to the story, as I found it hard to believe that a major vendor in any industry would intentionally do something like this. That led me to an article from nearly two years earlier that contained the following:
“IoT to completely transform their business model” and
“John Deere was looking for ways to change their business model and extend their products and service offering, allowing for a more constant flow of revenue from a single customer. The IoT allows them to do just that.”
That article closed with the assertion:
“Moreover, only allowing John Deere products access to the ecosystem creates a buyer lock-in for the farmers. Once they own John Deere equipment and make use of their services, it will be very expensive to switch to another supplier, thus strengthening John Deere’s strategic position.”
While any technology – especially platforms – has the potential for vendor lock-in, the majority of vendors offer some form of openness, such as:
- Supporting open standards, APIs, and processes that support some degree of portability and third-party product access.
- Providing simple ways to unload your data in at least one of several commonly used non-proprietary formats.
Some buyers may deliberately implement systems that support non-standard technology and extensions because they believe the long-term benefits of a tightly coupled system outweigh the risks of being locked into a vendor’s proprietary stack. But there are almost always several competitive options available; always consider all viable alternatives.
Less technology-savvy buyers may never even consider asking questions like this when purchasing. Even technologically savvy people may fail to consider IoT as a key component of everyday tools and services – thus failing to recognize the implications of a closed system relative to their purchase.
It will be interesting to see whether deliberate business strategies like these change because of competitive pressure, social pressure, or legislation over the coming years. In the meantime, the principle of caveat emptor may be truer than ever in this age of connected everything and the Internet of Things.
Discussions that Seed the Roots of Creativity
A few months ago I purchased Fitbit watches for my children and myself. My goals were twofold. First, I was hoping that they would motivate all of us to be more active. Second, I wanted to foster a sense of competition (including fair play and winning) within my children. Much of their pre-High School experiences focused on “participation,” as many schools feel that competition is bad. Unfortunately, competition is everywhere in life, so if don’t play to win you may not get the opportunity to play at all.
It is fun seeing them push to be the high achiever for the day, and to continually push themselves to do better week-by-week and month-by-month. I believe this creates a wonderful mindset that makes you want to do more, learn more, achieve more, and make an overall greater impact with everything they do. People who do that are also more interesting to spend time with, so that is a bonus.
Recently my 14 year-old son and I went for a long walk at night. It was a cold, windy, and fairly dark night. We live in fairly rural area so it is not uncommon to see and hear various wild animals on a 3-4 mile walk. I’m always looking for opportunities to teach my kids things in a way that is fun and memorable, and in a way that they don’t realize they are being taught. Retention of the concepts is very high when I am able to make it relevant to something we are doing.
That night we started talking about the wind. It was steady with occasional gusts, and at times it changed direction slightly. I pointed out the movement on bushes and taller grass on the side of the road. We discussed direction, and I told him to think about the wind like an invisible arrow, and then explained how those arrows traveled in straight lines or vectors until they met some other object. We discussed which object would “win,” and how the force of one object could impact another object. My plan was to discuss Newton’s three laws of motion.
My son asked if that is why airplanes sometimes appear to be flying at an angle but are going straight. He seemed to be grasping the concept. He then asked me if drones would be smart enough to make those adjustments, which quickly led to me discussing the use potential future of “intelligent” AI-based drones by the military. When he was 9 he wanted to be a Navy SEAL, but once he saw how much work that was he decided that he would rather be Transformer (which I explained was not a real thing). My plan was to use this example to discuss robotics and how you might program a robot to do various tasks, and then move to how it could learn from the past tasks and outcomes. I wanted him to logically break down the actions and think about managing complexity. But, no such luck that night.
His mind jumped to “Terminator” and “I, Robot.” I pointed out that Science Fiction does occasionally become Science Fact, which makes this type of discussion even more interesting. I also pointed out that there is spectrum between the best possible outcome – Utopia, and the worst possible outcome – Dystopia, and asked him what he thought could happen if machines could learn and become smarter on their own.
His response was that things would probably fall somewhere in the middle, but that there would be people at each end trying to pull the technology in their direction. That seemed like a very enlightened estimation. He asked me what I thought and I replied that I agreed with him. I then noted how some really intelligent guys like Stephen Hawking and Elon Musk are worried about the dystopian future and recently published a letter to express their concerns about potential pitfalls of AI (artificial intelligence). This is where the discussion became really interesting…
We discussed why you would want a program or a robot to learn and improve – so that it could continue to become better and more efficient, just like a person. We discussed good and bad, and how difficult it could be to control something that doesn’t have morals or understand social mores (which he felt if this robot was smart enough to learn on its own that it would also learn those things based on observations and interactions). That was an interesting perspective.
I told him about my discussions with his older sister, who wants to become a Physician, about how I believe that robotics, nanotechnology, and pharmacology will become the future of medicine. He and I took the logical next step and thought about a generic but intelligent medicine that identified and fixed problems independently, and then sent the data and lessons learned for others to learn from.
I’m sure that we will have an Internet of Things (IoT) discussion later, but for now I will tie this back to our discussion and Fitbit wearable technology.
After the walk I was thinking about what just happened, and was pleased because it seemed to spark some genuine interest in him. I’m always looking for that perfect recipe for innovation, but it is elusive and so far lacks repeatability. It may be possible to list many of the “ingredients” (intelligence, creativity, curiosity, confidence (to try and accept and learn from failure), multi-disciplinary experiences and expertise) and “measurements” (such as a mix of complementary skills, a mix of roles, and a special environment (i.e., strives to learn and improve, rewards both learning and success but doesn’t penalize failure, and creates a competitive environment that understands that in most cases the team is more important than any one individual)).
That type of environment is magical when you can create it, but it takes so much more than just having people and a place that seem to match the recipe. A critical “activation” component or two is missing. Things like curiosity, creativity, ingenuity, and a bit of fearlessness.
I tend to visualize things, so while I was thinking about this I pictured a tree with multiple “brains” (my mental image looked somewhat like broccoli) that had visible roots. Those roots were creative ideas that went off in various directions. Trees with more roots that were bigger and went deeper would stand out in a forest of regular trees.
Each major branch (brain/person) would have a certain degree of independence, but ultimately everything on the tree worked as a system. To me, this description makes so much more sense than the idea of a recipe, but it still doesn’t bring me closer to being able map the DNA of this imaginary tree.
At the end of our long walk it seemed that I probably learned as much as my son did. We made a connection that will likely lead to more walks and more discussions.
And in a strange way, I can thank the purchase of these Fitbit watches for being the motivation for an activity that led to this amazing discussion. From that perspective alone this was money well spent.
The Unsung Hero of Big Data
Earlier this week, I read a blog post regarding the recent Gartner Hype Cycle for Advanced Analytics and Data Science, 2015. The Gartner chart reminded me of the epigram, “Plus ça change, plus c’est la même chose” (asserting that history repeats itself by stating the more things change, the more they stay the same).
To some extent, that is true, as you could consider today’s Big Data as a derivative of yesterday’s VLDBs (very large databases) and Data Warehouses. One of the biggest changes, IMO, is the shift away from Star Schemas and practices implemented for performance reasons, such as aggregation of data sets, using derived and encoded values, using surrogate and foreign keys to establish linkage, etc. Going forward, it may not be possible to have that much rigidity and still be as responsive as needed from a competitive perspective.
There are many dimensions to big data: A huge sample of data (volume), which becomes your universal set and supports deep analysis as well as temporal and spatial analysis; A variety of data (structured and unstructured) that often does not lend itself to SQL based analytics; and often data streaming in (velocity) from multiple sources – an area that will become even more important in the era of the Internet of Things. These are the “Three V’s” people have talked about for the past five years.
Like many people, my interest in Object Database technology initially waned in the late 1990s. That is, until about four years ago, when a project at work led me back in this direction. As I dug into the various products, I learned they were alive and doing well in several niche areas. That finding led to a better understanding of the real value of object databases.
Some products try to be “All Vs to all people,” but generally, what works best is a complementary and integrated set of tools working together as a service within a single platform. It makes a lot of sense. So, back to object databases.
One of the things I like most about my job is the business development aspect. One of the product families I’m responsible for is Versant. With the Versant Object Database (VOD – high performance, high throughput, high concurrency) and Fast Objects (great for embedded applications like kiosks). I’ve met and worked with brilliant people who have created amazing products based on this technology. Creative people like these are fun to work with, and helping them grow their business is mutually beneficial. Everyone wins.
An area where VOD excels is with the near real-time processing of streaming data. The reason it is so adept at this task is the way that objects are mapped out in the database. They do so in a way that essentially mirrors reality. So, optionality is not a problem – no disjoint queries or missed data, no complex query gyrations to get the correct data set, etc. Things like sparse indexing are not a problem with VOD. This means that pattern matching is quick and easy, as well as more traditional rule and look-up validation. Polymorphism allows objects, functions, and even data to have multiple forms – something else that mirrors real life (just think about the variations of a peripheral device called a “printer”).
VOD and products like it do more by allowing data to be more, which is ideal for environments where change is the norm, such as: Cyber Security; Fraud Detection; Threat Detection; Logistics; and Heuristic Load Optimization. In each case, performance, accuracy, and adaptability are the key to ongoing success.
The ubiquity of devices generating data today, combined with the desire for people and companies to leverage that data for commercial and non-commercial benefit, is very different than what we saw 10+ years ago. Products like VOD are working their way up that “Slope of Enlightenment” because there is a need to connect the dots better and faster – especially as the volume and variety of those dots increases.
It is not a “one size fits all” solution, but it is often the perfect tool for complex data. More importantly, it is another tool to use in an ever-expanding data ecosystem.
These are indeed exciting times!
Ideas are sometimes Slippery and Hard to Grasp
I started this blog to be an “idea exchange,” as well as a way to pass along lessons learned to help others. Typical blog advice is to focus on one thing and do it well to build a following. That is especially important if you want to monetize the blog, but that is not and has not been my goal.
One thing that has surprised me is how different the comments and likes are for each post. Feedback from the last post was even more diverse and surprising than usual. It ranged from comments about “Siri vs Google” to feedback about Sci-Fi books and movies to Artificial Intelligence.
I asked a few friends for feedback and received something very insightful (thanks, Jim). He stated that he found the blog interesting but wasn’t sure of the objective. He went on to identify several possible goals for the last post. Strangely enough (or maybe not), his comments mirrored the type of feedback that I received. That pointed out an area for improvement, and I appreciated that as well as the wisdom of focusing on one thing. Who knows, maybe in the future…
This also reminded me of a white paper written 12-13 years ago by someone I used to work with. It was about how Bluetooth would be the “next big thing.” He had read an IEEE paper or something and saw potential for this new technology. He used the example of your toaster and coffee maker communicating so that your breakfast would be ready when you walk into the kitchen in the morning.
At that time, I had a couple of thoughts. Who cared about something that only had a 20-30-foot range when WiFi had become popular and had a much greater range? In addition, a couple of years earlier, I toured the Microsoft “House of the Future,” where everything was automated and key components communicated. But everything in the house was all hardwired or used WiFi – not Bluetooth. It was easy to dismiss his assertion because it seemed impractical. The value of the idea was difficult to quantify, given the use case provided.
Looking back now, I see that white paper as insightful. If it was visionary, he would have come out with the first Bluetooth speakers, car interface, or even phone earpiece and gotten rich, but it failed to present practical use cases that were easy enough to understand yet different enough from what was available at the time to demonstrate the real value of the idea. His expression of the idea was not tangible enough and, therefore, too slippery to grasp and value.
I believe that good ideas sometimes originate where you least expect them. Those ideas are often incremental – seemingly simple and sometimes borderline obvious, often building on another idea or concept. An idea does not need to be unique to be important or valuable, but it needs to be presented in a way that makes it easy to understand the benefits, differentiation, and value. That is just good communication.
One of the things I miss most from when my consulting company was active was the interaction between a couple of key people (Jason and Peter) and myself. Those guys were very good at taking an idea and helping build it out. This worked well because we had overlapping expertise and experiences, as well as complementary skills and perspectives. That diversity increased the depth and breadth of our efforts to develop and extend those ideas by asking the tough questions early and ensuring we could convince each other of the value.
Our discussions were creative, highly collaborative, and a lot of fun. We improved from them, and the outcome was usually commercially viable. As a growing and profitable small business, you must constantly innovate to differentiate yourself. Our discussions were driven as much by necessity as intellectual curiosity, and I believe this was part of the magic.
So, back to the last post. I view various technologies as building blocks. Some are foundational, and others are complementary. To me, the key is not viewing those various technologies as competing with each other. Instead, I look for potential value created by integrating them. That may not always be possible and does not always lead to something better, but occasionally it does, so to me, it is a worthwhile exercise. With regard to voice technology, I believe we will see more, better, and smarter applications of it – especially as real-time and AI systems become more complex due to the use of an increasing number of specialized chips, component systems, geospatial technology, and sensors.
While today’s smartphone interfaces would not pass the Turing Test or proposed alternatives, they are an improvement over more simplistic voice translation tools available just a few years ago. Advancement requires tools that understand context to make inferences. This brings you closer to machine learning, and big data (when done right) significantly increases that potential.
Ultimately, this all leads back to Artificial Intelligence (at least in my mind). It’s a big leap from a simple voice translation tool to AI, but it is not such a stretch when viewed as building blocks.
Now think about creating an interface (API) that allows one smart device to communicate with another, like the collaborative efforts described above with my old team. It’s not simply having a front-end device exchanging keywords or queries with a back-end device. Instead, it is two or more devices and/or systems having a “discussion” about what is being requested, looking at what each component “knows,” making inferences based on location and speed, asking clarifying questions and making suggestions, and then finally taking that multi-dimensional understanding of the problem to determine what is really needed.
So, possibly not true AI (yet), but a giant leap forward from what we have today. That would help turn the science fiction of the past into science fact in the near future. The better the smart system’s understanding and inferences, the better the results.
I also believe that an unintended consequence of these new smart systems is that they will likely make errors or have biases like humans as they become more human-like in their approach. Hopefully, those smart systems will be able to automatically back-test recommendations to validate and minimize errors. If they are intelligent enough to monitor results and suggest corrective actions when they determine the recommendation does not produce the desired results, they would become even “smarter.” There won’t be an ego creating a distortion filter about the approach or the results. Or maybe there will…
Many of the building blocks required to create these new systems are available today. But it takes vision and insight to see that potential, translate ideas from slippery and abstract to tangible and purposeful, and then start building something cool and useful. As that happens, we will see a paradigm shift in how we interact with computers and how they interact with us. It will become more interactive and intuitive. That will lead us to the systematic integration I wrote about in a big data/nanotechnology post.
So, what is the real objective of my blog? To get people thinking differently, foster collaboration and partnerships between businesses and educational institutions to push the limits of technology, and spark discussion about what others believe the future of computing and smart devices will look like. I’m confident that I will see these types of systems in my lifetime, and I believe this could happen within the next decade.
What are your thoughts?
Where Ideas, Experiences, and Lessons Learned Intersect 