With the internet of things (IoT) we’ve realized that we can amass data from our sensors, smart objects or “things” that know more about our surrounding world than we do or, at least, what we have programmed them to know!
Running the well-oiled machine
Our smart objects or things can routinely harvest data about the wellbeing of any ecosystem and consequently alert us if anything is askew. In fact, we can pretty much do what we like with this data but, largely, there are some who are still scratching their respective heads as to its value and significance.
There are those who emphasize the importance of “data visualization” and, whilst this has relevance for those who need to know, ordinary everyday consumers or users shouldn’t care, since our smart objects are programmed to take care of things and only alert us if something is perhaps odd. More so, there are engineers who need to lift the lid and take a closer look inside to ensure the mechanics are well-oiled and running smoothly.
There’s no need to poke or prod
Naturally, data visualization is more than ones and zeros – it’s about creating a representation of your smart objects’ world and translating this into something relevant and palatable for humans to digest. We invest an insurmountable measure of time in wrestling with understanding the importance and relevance of our data and how best to consume it. It’s been mentioned numerous times that data is the new currency and, whilst we wonder how we should interpret the value of this new digital resource, we may have already missed a story or two. Likewise, we also need to understand our target audience; ultimately who needs to see or use this data?
And this is primarily what I allude to in my title, “smart data.” You see, to a greater extent, we shouldn’t have to poke or prod data on its journey through an ecosystem – we should “let it be” to undertake its duty – something we’ve tasked it to do in our programming and product design.
Human interaction is unavoidable
Now that’s one perspective of data and its purpose, but there is of course data that may impart behavioral patterns when consumers shop and whatnot; there’s data that sustains a smart city and there’s data that empowers your smart home. You may have a series of sensors connected to a patient, where you’re monitoring their vital statistics which, in turn, may alert healthcare professionals or physicians as to their ongoing remote treatment and care.
Indeed, smart data has many stories to tell, but we may not necessarily be privy to its journey. Moreover, in the evolution of smart objects or things, we may need the support of “smart agents” – autonomous entities that have been empowered to make decisions for us. However, in our current design doctrine human interaction is still needed.
“… human interaction at some point is required, whether that’s from the onset where a smart object needs to be configured and defined with our initial criteria, to a human reacting to information, such as re/acting upon new data. The next generation of smart objects may be the smart agent, a hybrid device that’s capable of acting autonomously and may solely undertake decisions on our behalf.” The Handbook of Personal Area Networking Technologies and Protocols, Cambridge University Press, 2013.
Can we realistically predict the future?
I offered this supposition some time ago where smart agents can become pivotal in the evolution of our “things” irrespective of their role within an ecosystem or whatever purpose they serve. We’re on the edge of a realization of artificial intelligence (AI) where we have an ability to empower such technology to make educated decisions, based upon the smart data collated from both their environment and what they might learn from each other. In particular, smart agents within an ecosystem will diligently share data with other agents to ensure the harmonious running of the ecosystem. Of course, we’ve also empowered our smart agents to learn – a true cause and effect paradigm, in turn, slowly diminishing the need for human intervention and, again, realizing a truer definition of “machine learning.” Agents will also use blockchain technology to provide a ledger – an historical reference to what they have learned and might know for future situations – yes, predictive analytics is another reality.
Our smart data is a diverse collection of values that offer many insights into the various journeys undertaken by our smart agents. Armed with such knowledge, I often conjecture: “With data, if we understand the past and know the present, can we predict the future?” It’s something that has yet to be answered and proven; however, in time, I’m confident that our agents will be capable of managing most eventualities without the need of human interaction.
Until next time …
With this in mind, and if we bestow this innate ability within our smart agents, what could possibly go wrong? But, as humans, we still have that measure of distrust with technology – an inescapable human trait – after all, technology is still made by humans and, alas, we make errors. As such, can we really let it go? In other words, AI is currently touted as the future and humanity’s destination, but I do have this nagging feeling that we’re not quite ready.
So, this is where a “How well has your smart data travelled today?” Dr. G signs off.
Originally published in Technically Speaking.
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