It was a long yearning of mine to write a short piece about political caricature in 19th century France. I will not. Not because I wouldn’t want to, or I wouldn’t think it’s interesting, but because you wouldn’t.
I know this, because upon getting inspired to the brink of writing, I asked 4Liberty’s excellent editor, Olga Łabendowicz, to furnish me with data as to what articles perform the best on the website.
And because I will not reveal trade secrets, suffice to say that the articles with the titles including the word “coronavirus” are doing better now than others, as do the one with such key words as “globalism” and “communism” (the latter in a context of course where it is criticized).
At this point, you might begin to wonder if the title is misleading, and serves only to lure you in. In order to find out, you have to give me the benefit of the doubt and read on.
What I promise, though, is that this article is about big data and data analysis.
Usually, there is a discrepancy between what authors want to write about and what readers want to read about. Ultimately, however, authors want to be read, so they should set aside their pet topics for the benefit of the numbers, or better still, bring the two together.
It has never been easier. Before, wide-range polling politicians and difference-makers had to “feel” the zeitgeist, tune in to the resonations with their antennas, and it pretty much required such a not exact method of needing a genius.
Then, polling became more feasible thanks to quicker travel both of personnel and information. It swayed the field some, giving an edge to those who heeded the numbers, if the numbers were not controlled by the biases in the questions. Then, lately, a new trend took shape.
Experts are loth to admit they don’t know, forcing them to say whatever seems trendy enough that they get the attention. When their predictions fail, they shrug, blaming an unaccountable external force. The same is the issue with polls.
With a lack of a better tool, their credibility was redeemed through the excuse of esoteric but contextually utterly meaningless words such as “marginal”, and “circumstance”,or worse “closet”.
People lie. People are reluctant to admit what really motivates them. People often don’t know. Because people are not machines. So why not use machines?
Algorithms succeeded where human lead polls failed. Many analysts couldn’t predict the victory of Donald Trump or Brexit. But when algorithms were used, especially in cooperation with humans, the forecast was more precise.
There is a myriad of data points humans are unable to analyze, but machines can. There are still things humans are better at, such as getting human intel, constructing methodology, and, most importantly, designing the machine learning and AI tools.
More precise data processors open up new horizons. Never has it been easier (though not at all easy) to monitor global trade. It was difficult enough to keep track of the dealing of a bazaar, let alone the stock market.
Now, software analyzes retail profits, traffic data, weather, and thousands of other factors to forecast which market is worth investing in, and which is ripe for shorting, as required in a globalized world.
Data analyzed by algorithms can even serve as the basis to model coronavirus, its spread, effect on both humans and the economy, and outline what actions against it will have what consequences. This is, of course, not that simple – take, for instance, the different approach Sweden is taking. Models are not always reliable, yet.
However, what is reliable is mass surveillance, the dark path communist China took, even before the pandemic. It monitors its subjects, and assigns them points based on their behavior.
Movement, internet usage, when they arrive to work, when they go home, or if they listen to music too loudly on public transport all determine social score. Algorithms monitor just about everything.
But so does Facebook and Google, albeit you sign up voluntarily, and the repercussions are not as severe as in China.
With tech companies, worst-case scenario, your data will be used as a factor in election or market forecast algorithms; best-case, you will be shown an advertisement that can actually benefit you.
Ultimately, humans will use the data. For a simple article even my meager faculties suffice to look at and analyze the humanly comprehensible amount of datapoint. If my decision to act on it this way and title the article thusly will work or not, is a different question. But I’ll sure as hell see and learn from it.
Data is available in more abundance than ever. Set aside wistful thinking and utilize it, the right way.
Also, misleading headlines are the anterooms for fake news, so did you really think I won’t mention coronavirus, globalism, and communism?