In fact, without a clear understanding of the field, adding any real value to organizations is impossible.
If there is a buzzword that gets people worked up today, it is “Data Science”. Just say the word and you will get a lecture on how it is the future. But among all those lectures on the emergence of the field, there is very little that anyone ever does to explain what it really is.
Consequently, many people are not really talking about Data Science when they think they are. So, really there is a vicious loop of misinformation that emerges and circulates among masses about the true essence of the concept.
Why Bother?
According to PWC, Data scientists are among one of the most sought-after jobs presently. Not only are they an essential resource in existing operations but play a huge role in upcoming businesses. Secondly, it caters to a plethora of different fields and can aid decision-making for essential business decisions. Whether it may be deciding how much a decision to automate will save the organization or doing a cost-benefit analysis to simply buy a logo, you can make any decision with its help.
What is Data Science?
Having already explained the implications of the concept in several different fields, it may come as no surprise that data science is an interdisciplinary field. It combines knowledge of statistics, algorithms such as machine learning, modelling, scientific research and business intelligence to lend insights into large quantities of data.
More often than not, professionals in the field rely on specialized software and programming languages to aid in the analysis. In addition to that, professionals have a strong grasp over the intricacies of mathematics and statistics.
What it is Not
However, the very fact that it is so widely applicable, most people come to think of data science as anything and everything that has to do with data. For instance, some of the most commonly misattributed ideas are:
1.Excel Models
Microsoft Excel is one of the most widely used spreadsheet tools for data wrangling around the world. It not only offers an easy interface to manage data but also comes with several analytical tools that help evaluate decisions. That very factor leads people into believing that modelling tasks over Microsoft excel are data science.
It is not!
2.Python/R
Contrary to believing modelling over Excel, another misconception is that simply using a programming language is data science. More often than not, the two most attributed languages are Python and R. Much like modelling over excel, an individual language is not the entirety of the concept!
3.Dashboards
The corporate world uses a number of different presentation techniques. Some of the most impressive and dynamic of these are dashboards produced with software such as Power BI, Tableau and so on. However, simply visualizing data, much like simply manipulating data, cannot and is not considered to be data science.
4.Machine Learning
Machine learning is a powerful technique for creating models and generating insights from data. Given its complexity, many people are led into believing that machine learning is data science. While this myth is slightly more difficult to debunk, it can simply be said that that machine learning originates from Artificial Intelligence. Data science simply uses machine learning as a tool.
Conclusion
To add real value to an organization by way of data science, any executives must know exactly what the concept really entails. Once understood, it could go a long way into debunking any myths and adding real value.
About the Author
The writer works with a digital agency specializing in the design and development of creatives where you can buy logo designs and create them.
