April 14, 2016
The Age of Datalogy, Anthropology & Evolving Insights
The great divide between Anthropology (looking back) and Datalogy (looking forward) lies in the predictive nature of data—the raw material that is propelling our economy.
Nothing New Under the Sun
There are multiple ways to look back and create a timeline about the evolution that a segment or an industry went through along the years, and the first thing that comes to mind is by establishing a direct comparison between the different technologies that were used along each period to help us address the challenges of such area. Let’s take transportation as an example. From this perspective, the changes were dramatic, the Romans brought us the VIA/VIAE (Latin for road/roads) the first networked infrastructure so to speak, allowing long distance travel and cross border trade, presumably starting on foot and evolving to animal-assisted transport, such as horses and carriages, stagecoaches, then to wind-powered vessels, such as sailing ships, then on to vapor-mechanic devices, such as trains, and finally to internal-combustion engines such as in cars, leading ultimately to travel with airplanes and spacecraft.
An alternative, more interesting way though to evaluate such transformation is by looking into the way it affected people’s life, and it’s consequences. If we use the same transportation example, it dramatically reduced travel time, allowing people to go to places that were unthinkable before in a life-time, and finally expanded trade to a worldwide operation, resulting in a massive availability of products and goods all around the world: the well-known globalization.
It’s no different with IT, and although the focus of such transformations is usually concentrated in the last 50 years, the truth is that processing information has been part of humanity since its very foundation. It probably goes back to when writing was first invented and human beings started to collect data about the world. After all, we always were, and still are, gatherers. What we gather has significantly changed—data has become the new got-to-have. (Much like the concept of “Noblesse Oblige,” digital data has a moral obligation to our industry…stay tuned for more about that in an upcoming episode.)
If we use the same transportation analogy, from the technology point of view the changes were really dramatic in the last 50 years, with the invention of computer systems in the post-WWII era. The same applies to the way people’s lives have been transformed. Nobody has said it yet, but it could be argued that the computer is as important to civilization as the invention of the wheel—if not moreso. We probably switched between anthropological eras when we started to use mathematical devices to abstract the concept of information, out of what we call today simply and almost too simplistically “the data.” By first storing and next manipulating data through a computer, we are able to extract conclusions out of it, with the combination of using pure mathematical logic.
But what is data? Let’s step back a little bit, and reflect about data itself. According to the dictionary, data is a plural of datum, which is originally a Latin noun meaning “something given.” In IT, this “something given” is essentially the result of some form of metrics of the real world, or a symbolic representation of some higher-level concept, but in either way, “data” is a way to represent facts of the world.
The importance of data can be easily detected if we zoom in the last 10 years, and look into the technology shift. If 50 years ago computers were devices available only for government and state agencies, nowadays almost every single person in the world has at least one (smartphone), if not multiple. This revolution started with the PC and then the Internet, evolving to digital content, eCommerce, and online trading—skipping some steps in between—finally to an interconnected worldwide network of people interacting with each other, or the so simplistically called “social media”, before we knew it the convergence of technology and technology was in the making.
Often overlooked in this analysis is the impact of applications, or apps as we tend to call them nowadays, with the advent of ever smarter and more capable mobile devices, app developers have high impact at hand capturing (Facebook) and directing our daily life (sharing-economy a la UBER or AirBNB) and digitizing/monetizing it.
This is the technological aspect of this shift. What about the way it changed people’s life? Of course, there are obvious changes that affected everybody, like having access to a smartphone that gives you the computer power at your fingertips (in your back pocket) that is hundreds of millions of times faster than the computer that guided Apollo 11 to the moon. And using such computer power to do very prosaic things, like calling a Taxi, or checking the weather. This, by itself, is a huge transformation.
It’s about the data, Stupid!
Let’s take a deeper look and say that the transformation is even bigger than that, and the reason is because this has shifted not only worldwide economy, but rather the main vector that is driving economy! Instead of simply having a different way to do trading, or buying and selling things faster and easier, with more capilarity than in the past, there is not a single business process or production line that is not (or will soon) being affected by the availability of data transformed into information (insight).
This is reflected in the way we’ve been doing data processing most of the time. In a recent past, data processing was done post-facto after an action had produced value or knowledge. It might look at all customers that were invoiced, which branch had more sales in the last quarter, and which product resulted in a better profit margin. These are mainly past events, whose activities or actions produced data, which was then processed and eventually transformed into information (insight) to fuel business decisions. In this sense, data was affecting business and, in a more macro way, the economy. It represented a passive set of evaluated metrics, representing what happened in the real world.
Nowadays, with a full, interconnected world of computers and people, such data is becoming more and more precious because it can be processed in real time—so it can influence business in real-time and not in hindsight. If you step into an appliance store, that same store wants to know your recent searches on Google or Amazon for products that they carry. They want to immediately identify potential offerings that, if properly worked out in real-time, could trigger your purchase decision right there (e.g.: customers who bought x also like y).
How valuable would it be if that same store was able to identify that you read multiple reviews of a particular refrigerator model across different sites, and check which is the lowest price that you probably found, and then send this information for a well-informed on-the-floor sales agent that could approach you with a “can’t refuse” offer: 10% off of the lowest price you found, or 1 year free extra warranty, because you also know that although most reviews were very positive about the product, the only thing that was being negatively evaluated was the brand post-sales service?
Privacy issues aside, you can see a major shift of influence that the data has in the economy, from a passive collection of metrics that eventually will be transformed into information, into an active, business driver that can change purchase decisions on-the-fly. This is a shift from an economy that uses data as information to a new data-driven economy entering the neodata age.
This is a very simple example of how the economy is transforming into a data-driven one, but we can enumerate many other factors that converge to this point: starting with big data predictive analytics, the Internet of Things with billions of devices feeding automated systems with real-time information, wearable devices monitoring and interacting with people in real-life. All of the allied to an unprecedented processing capability due to the horizontal scalability provided by cloud systems. Data moved from the backstage into the main stage of the theater of economy, and is no longer statically “given”, but rather it is the very true dynamic force behind these transformations. All these technologies will change the world in a way that has never happened before, simply because data now has the power to drive the economy around you. This will certainly impose new challenges, such as security, privacy and, moreover, an overwhelming sense of things getting out of control from the personal perspective. Nonetheless, I think this is the start of a very important era: long live the data-driven economy and by extension data-driven anthropology. Bill Clinton’s famous campaign message, “It’s the economy, stupid” can be paraphrased as: “It’s the data, stupid!”
Now as far as what we learned making the switch from anthropology to datatology: history tends to repeats itself, tends to reinvent itself, adjusting for challenges like a changing environment. Knowing that technology and economy are so interwoven these days, it is clear that anthropology and datatology share that very same ability, including pendulum and circular movements what once was “cool” will once again be “cool.” In the grander scheme of things, there is nothing new under the sun (Ecclesiastes 1:9). More about this in our next article.
The term “data science” (originally used interchangeably with “datalogy“) has existed for over thirty years and was used initially as a substitute for computer science by Peter Naur in 1960. In 1974, Naur published Concise Survey of Computer Methods, which freely used the term “data science” in its survey of the contemporary data processing methods that are used in a wide range of applications.
This Piece was a contribution of Gilbert Van Cutsem – Consultant and Advisor, NoSQL & Multimodel Database Technology, and Evaldo Horn de Oliveira.