In a world of progress and digital revolution, the leading-edge tech companies have made it clear that mastering big data is the key to success. However, not all businesses have the resources to directly tap into this ocean of raw information. Yet all need to get exactly the right details out of it – in order to sell better, and provide better services to their customers. The percentage of companies that pay for “Interpreted-Data-as-a-Service” is most likely higher than the one of the companies that handle their own data needs on premise.
This is definitely one way to succeed in the modern business world – extrapolating and interpreting data. We might even say obsessing about it, when the results prove unsatisfactory – a sort of passion about numbers that holds the promise of the ultimate clarity, of the logical anticipation of the following key strategic steps.
Reducing the options, a way of better handling big data
Recently, Google’s search app on iOS got a Twitter-like “trending” feature, as well as the capability of displaying the estimated search results “even before (the users) press the search button”. Of course, the Android users of the Google App are already used with the “trending” suggestions, which appeared approximately one year ago – generating a long line of complaints.
Well, it seems like not all customers complaints’ are seen as constructive feedback by the company. Google simply goes on with certain features, regardless of the way the audience receives them. Probably, the higher purpose in introducing certain changes prevails.
And definitely a very important role of many of the latest changes, from algorithms to app updates or digital tools’ changes is related to what the machines need, in order to be able to process the global data and produce relevant results. For attaining a reasonable predictability, the algorithms need to evolve or – a more short-term alternative – to be fed logically organized data.
The randomness that usually characterizes human nature just isn’t that productive at the moment
Therefore, when searching for whatever goes through your mind on Google, its search app prompts various “nearest” categories, trying to eliminate unnecessary variation. Perhaps you call the same thing a different way, and in fact you are looking for exactly what your next-door neighbor is looking for. Perhaps you are susceptible of being influenced and you will end up looking for the same thing your neighbor looked for… the reasons you might take Google’s suggestions instead of using your own head are countless.
The outcome is what matters – big data becomes more logical, more efficiently digestible. Bots and algorithms can provide valid final predictions and results, businesses adopt these tools on a large scale. This technology is successful, even in its more rudimentary forms (as opposed to the highly advanced projects that try to materialize AGI)…
Digital precognition, data and businesses
Machine Learning is built on the (valid) assumption that the human brain works as a machine would, only it is an extremely sophisticated system. Well, this theory would find a lot of opponents, and even when accepted, it includes listing many exceptions, in order to fully describe the way we think and function.
However, the schematics of this concept are enough for the ML promoters to work on. Developing more and more advanced software tools, those who work in this field learn as they go along, combining the commercial value of each step and intermediary tool with their experimental value. The activity is both interesting and profitable.
Digital precognition, or the capacity of anticipating certain elements, from market movements to people’s reactions or choices, is already incipiently present in various software tools. Monitoring or cybersecurity tools, for example, are in some cases capable of alerting the users on the probability of an incident.
Therefore, we may say we already have achieved a certain degree of digital precognition. Also, by teaching people to organize their thoughts and think in a less complicated/sophisticated way, the point where machines meet human thinking gets closer and closer.
Out of curiosity, just ask yourself what each update in your favorite apps that does not feel quite comfortable is in fact trying to teach you?