Why Are We Moving Towards A Data-Centric World?
We live in a world overflowing with data — it is the water of the digital age. How did we arrive at this state of affairs? Ironically, it won’t be necessary to quote stats and figures. Let’s walk through some thoughts and facts to consider as we construct a holistic view of the world that we currently inhabit. From there, the answer to the question should seem obvious.
First of all, let’s understand our definitions — it’s important not to get lost in the hype around neologisms or new spins on old words. In today’s sense of the word, data is merely a record, a stored value. This is so crucial to understand because it allows us to draw immediate parallels to archives and to banks. When you write in a notebook, you’re making a record; when you make a transaction, you make a record. In that sense, we have always lived alongside data — it was just never centralized to the extent that we could analyze it properly. Before the networked era enabled by the internet, data lived in silos and was transitory. All it took was an accidental (or intentional) fire and meticulously stored data was gone literally forever.
With the infrastructure of the internet, data not only intermeshes but becomes resilient through decentralized storing practices — the internet is a hydra with many heads and if you slice off one head, two more grow in its place. All of sudden, data starts to accumulate and takes on a life of its own. In light of the notion of accumulation, let’s run with the original metaphor of water. Before the internet, the rain of naturally occurring data never collected into bodies of water — rain drops hit the barren ground and were absorbed, out of conscious sight though ever-present. However, websites — with their servers — are like buckets that collect the rainwater of data and enable it to accumulate. When we move from static websites to dynamic social networks, those buckets become rivers, lakes, and oceans — ever more interaction and ever more data.
With this in mind, we are swimming in data — this is our current reality, not even the future. We are swimming in oceans of data and this data set only accords for digital phenomena. Can you imagine how vast the volume of data will be when we extend the web of the internet to in-real-life phenomena through the Internet of Things (IoT) industry? This is already happening too. I can easily take a Raspberry Pi or an Arduino board, equip said board with a sensor, and connect the board wirelessly to a server to collect data on foot-traffic in a store — and this is with technology that I’d call old by today’s standards. We are truly only at the cusp of what a data-centric world means and it will only accelerate in the coming decade.
When we understand the current state of affairs, it becomes paramount to optimize your situation for the materializing reality. The logic of how data is stored quantitatively makes the understanding of statistics paramount — as such, statistics is the engine of the current movement in data science. All the tools and technologies of data science are branches on the trunk of statistics. The tools, I promise you, will ever evolve — as R is replacing MatLab — but the core logic will remain largely the same. With this in mind, do yourself a favour and learn some statistics.
The second optimization we can strive for is to recognize the blind spots in the current movement of data science. A quantitative focus of the measurements and the bias towards spreadsheets makes it very important to understand what decision criteria we are using to inform our data collection practices. Quantified data only makes sense in terms of the contextual reality that informed the data’s collection. If I have the numbers 56, 21, and 97, whether or not they are percentage grades or temperatures makes a world of difference for the interpretation of the data. This brings us to most important point in this piece: action.
We don’t collect data for the sake of collecting data — we collect data to inform our actions against a backdrop of uncertainty. Even if you do collect data for a living, I do hope you’re able to make a living by selling said data to people who will be able to act on it. Thus, to answer the core question in this piece, we are moving towards a data-centric world because such a world — ideally — enables us to act with greater certainty and smaller margins of error. I say “ideally” because humanity is still not perfect at knowing precisely what data to collect in certain situations; moreover, many people and firms profit from information asymmetry where it is in their best interest for others to operate in opacity. Nonetheless, because of the strong incentives in place towards failing less and losing less money and/or time, a data-centric world is one we are approximating with every passing second.
Originally Posted at LevelTO.com
