Data Science Explained
Updated: Feb 4, 2022
With constant innovation happening around us ever since the end of the last millennium, it is important to note that a lot of it can be attributed to technological advancement, especially mathematical statistics, data analysis and big data. Considering massive amounts of data companies from all industries are involved with, it is no wonder that data science is one of the most debated topics, which consequently results in data scientists being in high demand. However, because of the complexity of the topic surrounding this field, many individuals - whether businessmen, students or data scientists themselves - are still baffled by its definition. This is why we at Asigmo have decided to break this barrier between data science and other disciplines, and a great way to do that is to educate anyone interested in the topic of data science and its importance through various upcoming articles, starting with an introduction to data science.
Data science is an area of study that involves extracting huge amounts of data, which can be either structured (quantitative, clearly defined and searchable) or unstructured (qualitative, usually stored in native format).
It enables one to transform a business problem into a research project, which can be translated back into a practical solution for one’s business. Although it might sound easy in theory, all of this is done with the help of complex scientific methods, processes and algorithms, from which data science has evolved.
To put it in simpler terms, let us borrow an analogy from Ronald Van Loon, often considered the world’s 1st influencer in data and analytics. He starts by imagining a basket of different fruits from all over the world. Our job as a chef is to make a unified dish like a fruit cake or a pie. To do this, we need to consider the nutritional value of every fruit in the basket and think of what tastes go well together. Just as our role as chefs in this fictional scenario involves multiple tasks, so does the job of a data scientist - they need to act similarly since they have to look at various types of data sources and figure out how to collect, process, store, distribute and maintain them. This enables them to find out the real meaning behind all the gathered information.
As IBM (International Business Machines Corporation) puts it, the combination of skills required of data scientists are rare and it is no wonder that they are currently in high demand. Based on their survey, job opening growth in the sector continues to grow at more than 5% per year, and it was estimated that over 60,000 new jobs in the field have opened in 2020. However, the prediction for 2026 is even more promising as the US Bureau of Labor Statistics expects more than 11.5 million new positions to open in the data science and analytics sectors. Not bad for an emerging discipline, right?
Benefits for your business
As already mentioned, data science can be very beneficial to businesses from all sectors. For example, with the help of predictive analysis, a company can structure its data and use it to predict what kind of outcome a business decision can cause. In the end, various negative scenarios can be avoided with a simple strategic business decision to hire a data scientist.
Furthermore, the data a data scientist provides does not have to be used only for planning future decisions. Since real-time data is used as input, real-time intelligence can also be provided. This gives managers an approximate or even exact overview of what is happening with their business at present and provides more decision-making opportunities faster. This can be especially beneficial when reverting bad business decisions or changing directions of insufficient marketing strategies while they are already undergoing execution.
Lastly, data science can provide businesses with insights into consumers’ wants in the market today. This can enable both the R&D department to design and develop, and later the marketing department to market products that are in line with consumers’ expectations. This leads to a smooth launch of new products and ensures prosperous growth for already established businesses, or those wishing to move further from the startup phase.
Implications for society
There are several sectors where data science has already made its presence and secured its involvement for the future. To name just a few, these sectors include the automobile industry, information technologies (IT), healthcare, defence, energy and banking.
For example, one of the hottest topics nowadays is the COVID-19 pandemic and its burden on our healthcare system. With the help of data science, however, large datasets of patients can be used to build a data science approach where diseases can be identified at early stages. Imagine if we were able to predict future outbreaks, save thousands of lives and avoid lockdowns that would otherwise close down countless businesses and bring our economy down on its knees. Luckily for us, all of this can already be avoided with the help of data science.
Furthermore, many people have already heard of the development of artificial intelligence that is going to be the basis of self-driving cars of the future. However, many are not aware that a big part of autonomous vehicle technology’s development can be attributed to the development of data science as well. In a way, it is a classic example of data science and artificial learning working hand in hand as the latter requires a large amount of information coming from high tech gear like altimeters, accelerometers, ultrasonic detectors and so on. Because the vehicle itself is not able to differentiate between various obstacles on the road, the captured data is then analysed by data scientists which turn it into programmable information that later ends up in a newer model of the autonomous car. This process makes the car “smarter”.
Of course, these were just two examples from two different sectors but the possibilities are endless. Data science’s implications are expected to be far bigger than we can anticipate at this stage.
To end on a note from Ronald Van Loon, the previously mentioned public speaker for various data science events, companies that fail to embrace and keep up with data science won’t be able to compete in the future. That is why it is of the utmost importance for companies to evolve and keep up with the competition in order to survive. With the astonishing amount of new job openings and the technological implications of the sector, we do not at all exaggerate if we say that the future of the sector is as bright as it was for the internet-related companies in the dot-com era. The only question is whether this field will also develop a speculative bubble, or will the majority of companies miss the train that will, consequently, benefit only a few.
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