This data is then collected, and we process it over and over again for various purposes, such as analysing consumer behaviour, making predictions, creating marketing campaigns, etc.
By Iffy Kukkoo
16 Sep, 2017
Every time you perform a click, you share data. This data is then collected, and we process it over and over again for various purposes, such as analysing consumer behaviour, making predictions, creating marketing campaigns, etc. If that doesn’t sound exciting already, just think of the amount of data used in these analyses: about 2.7 zettabytes (2.7 trillion gigabytes) of data are collected annually!
Even for those vaguely familiar with the advancements in technology, during the past decade or so, processing data has become almost synonymous with using some of the AI tools at our disposal. Nothing unusual, of course, since, as predicted by IDC, by 2020, machine learning (ML) is expected to perform almost all of these functions. This, of course, comes with certain threats. Especially, when you have in mind the growth of the market.
Indeed, just two years ago, worldwide total data revenue was slightly more than $69 billion, a number which was expected to all but double last year, reaching $130 billion by the end of 2016. No wonder people are touting business intelligence as one of the most perspective fields of nowadays!
Analysing and manipulating big data means transforming quantity into quality. Consequently, it also means hundreds of so far unimaginable benefits and opportunities. Let’s have a look at few of them here.
Predictions are essential for such rapidly expanding fields as big data and artificial intelligence. Since each of them deals with things we have never had around before, it is only through predictions that we can develop the right approach.
Artificial intelligence, for example, due to possible future implications, hasn’t been exactly embraced even by such giants of the IT industry such as Elon Musk and Bill Gates; in fact, the question of ethics arises over and over again in the media whenever a new AI tool reaches the general population. With big data, on the other hand, the real problem is security. For one, will our laws be enough to control how algorithms process big data and, if not, wouldn’t this mean continuous invasion of privacy which, if in the wrong hands, can be used for malicious purposes as well? Not that AI is left out of this discussion.
Additionally, predictions are important for financial experts and stockbrokers. It’s trustworthy predictions which help investors and companies discover the most perspective niches. (Even though, in the near future, humans may have to make way for machines to maximize profits.) According to Gartner, global revenue in the business intelligence market should reach $18.3 billion in 2017, an increase of more than seven percent from last year.
The emergence of numerous cloud computing services has opened new horizons in the world of business. Software as a service (SaaS) was already a revelation for many businesses, but DaaS may be even bigger. In fact, it’s expected to integrate with, or even supersede, SaaS in the very near future.
As is the case with all “as a service” models, DaaS provides customers with the product in question (or data, in this case) on demand, overcoming organizational and geographical borders. With SaaS, you didn’t need to install software to use it; with DaaS, you don’t need to even have data.
The benefits of the model are as numerous as they are obvious:
Taking into consideration how fast DaaS has become so popular, it’s fairly reasonable to expect that the market will continue to expand exponentially, making DaaS even more convenient for businesses of any type and size.
Nowadays, most companies employ data scientists tasked with writing unique algorithms pertaining to the needs of related projects. This will soon become a thing of the past.
It’s becoming ever more cheaper and more efficient for businesses to instead purchase algorithms. This, in turn, means that the algorithm marketplace will soon experience substantial growth.
Companies worldwide are spending lots of money to hire data scientists (the average salary for a data scientist is $118,709), which usually work on a sample algorithm for quite some time. Just buying an algorithm at a marketplace should save the company both the money and the time, rendering the need for company-employed data scientists redundant. Instead, data scientists will start working for the marketplaces, and the marketplaces for the companies.
We went through this with apps. It’s the algorithms turn now.
As data needs increase, the need for low-cost and easy-to-use cloud platforms for both storing and processing data increases as well. And this is where hybrid cloud computing comes in. Using both local and off-site storages, hybrid cloud models provide companies with both high flexibility and better security for various manipulations with data.
As for particular tools, Hadoop and Spark should become even more popular than today. However, with popularity comes competition and it’s only a matter of time before another tool comes on the market, offering more functionalities and better interface. The latter one should endear these tools to the general population, allowing people without any coding background to benefit from using them.
The main problem with big data is working with it: at the moment, it’s as costly as is time-consuming. In fact, analysing the data is less problematic than preparing it. Collecting large datasets of unstructured information takes time and resources. And, if they want to use the benefits of big data, companies have no option but to risk both, even though no one can guarantee that the outcome of the analyses will justify the effort in financial sense.
FARS – which is an acronym standing for Fast, Actionable, Relevant, and Smart (Data) – is how we intend to tackle the setback. It’s the new paradigm: FARS is really how we expect big data to evolve in the nearest future.
At the moment, it may seem a bit far-fetched, but FARS is only a natural step forward, a way to circumvent the inconveniences and difficulties which hinder retrieving valuable insights and making trustworthy predictions when working with big data.
Many people expect automation to inevitably lead to job shortages. But, as we learned centuries ago during the Industrial Revolution, it may not be O.K. to be a Luddite. Because, simply put, the situation may not be as pessimistic as it appears at first sight. We just need to develop the right approach.
In fact, we can already reap some of the benefits of automation. Have you noticed, for example, how many people earn large salaries even though they didn’t even go to university? It’s not because they didn’t want to learn. They just went to a different type of university: tutorials, free courses, seminars, conferences It’s much easier today to get the proper training (and the desired job afterwards).
And this is only a small part of what the future may bring. For example, analysts are growing more and more certain that “the automation shift” may lead to trying out a universal basic income solution. For every world citizen.
As much as it is helping us, AI has helped hackers as well, and with the growing number of cloud services and API users, security is becoming an ever more acute issue. So much so that, in 2016, the European Union adopted changes to its data protection regulations. As stated in the data protection reform package: