Let us shed light on the roles and responsibilities of these two figures that are often confused with each other.
In the vast world of big data there are different professional figures, and it is entirely plausible that a layman in the field, as perhaps you are, might find himself displaced by technical terms and various Englishisms, and might therefore misunderstand the specific duties of those doing one particular job versus another.
A marketresearch agency such as ours, which is constantly engaged in market analysis and competitive analysis for clients from a variety of industries, is particularly attentive to role definition.
In this regard, we would like to clarify within our blog a topic that is often the subject of confusion.
Let us therefore discover the two important figures of the data analyst and the data scientist.
Data analyst and data scientist: two important (but distinct) figures in the world of big data.
These two professions are increasingly sought after by companies, as are more and more students in the field business and science to wink at specializations in big data, driven not only by the allure of the job but also by the growing employment opportunities and the prospects for a good salary.
Both roles have great importance in market research, competitor analysis, and all those data study processes that can significantly determine the success of a business, including yours!
As we said in the introduction, it is a recurring mistake to consider these two professions one and the same, so if you thought so far that was the case, fear not. It is perfectly understandable.
But the reality is that, apart from sharing the word “date,” these two tasks require quite distinct skills and competencies.
You will soon find out what they are.
Testifying to this principle are the requirements sought by companies in job descriptions, where these are described in great detail.
By comparing two well-structured job advertisements in which these two figures are sought, you will find it easy and intuitive to understand that finding the same knowledge in one figure is conceptually difficult and that they are therefore two separate professions.
Regardless of what does a data analyst do and what a data scientist does-a topic we will address shortly-it is important that you understand that these two professions do not conflict with each other (as you might mistakenly believe) but rather cooperate within the same area of expertise with specific goals.
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That being said, let’s find out what are the differences between data analysts and data scientists and what is their role within a data study and analysis process.
Data analyst and data scientist: the differences between these two professionals.
With the term data scientist we often tend to identify generically the whole category of data professionals, but in fact this is a professional figure with very specific roles and skills, completely different from those of the data analyst.
The data scientist is one who has the necessary skills to study data in the narrowest sense.
In fact, he knows what data to search for (or have the data engineer, another figure working in the field, search for) because he plays the role of researcher. Generally, the job of data scientist requires specific knowledge in the field of statistics, but also in other subjects.
Indeed, these skills are harnessed and applied in the study of data in order to correlate them with each other to obtain specific information.
So the data scientist is the one who provides the data research that is useful for pursuing your goals.
The data analyst, on the other hand, is one who is responsible for understanding the data that emerges from a study and, following deep analysis, extracting insights.
Unlike the data scientist he is distinctly tied to the world of business, which he must know very well in order to be able to come up with the insights that are useful for his goals.
This figure is actually the link that binds those who deal purely with statistics and information technology and those who must then develop strategies.
The data analyst therefore has skills and expertise in the use of Excel, spreadsheets in general, and various advanced tools for data analysis and correlation.
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Data analyst Vs data scientist: the importance of the right choice based on need.
Well, now that you understand the substantial differences between these two figures you will certainly also be clearer on a concept that often eludes professionals: you don’t have to choose between one and the other!
If you are an entrepreneur and need to resort to the study and thedata analysis to come up with a winning and always up-to-date strategy for your business, the smartest and most productive decision you can make is to rely on a team of professionals, each specializing in his or her own tasks, and entrust each and every process to the competent figure.
Indeed, what sense would it make to choose to rely on a data scientist for a process such as analyzing and correlating data from a strategic perspective if he or she does not have the specific skills to do so?
Simple: none!
To bring up the most classic of examples, in building a house would you entrust the builder (however much he calls himself a handyman) to do the plumbing or the electrical work?
It would certainly not be the most responsible choice, as each process has its own peculiarities and levels of expertise, and if you aim to have a top-notch executed job, you have to entrust each task to a specialized professional.
To each his own in short.
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Explain your situation, problems and goals to our team of experts. You can rest assured that we have the right tools and expertise to provide you with all the information you need to take the best action in your business!
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