The high level technology has been expanded
Technological achievements have impressed everyone the last three decades, especially when it comes to the storing in big electronic storages data without the need of paper and ink. This was the result of creating unmistakable databases of data, without the sloppy human hand and pointless effort. In this high level storages, the necessity of the data analysis field, has been erected. Industries of every field have evolved as a result to have the need of having someone to analyze their data to create a clear view of where they stand and where they are heading.
By manipulating the enormous and complicated structured and unstructured data are stored in the mentioned electronic views such as databases and cloud, different kind of techniques and methodologies have been developed to create a peaceful environment for all the analyst. Analysts in some cases are called "The story tellers" because are able from unrelated data to create meaningful information and dashboards providing at-a- glance key performance indicators that can say a story to the reader without any support from a third party or an expert. However, it is obvious that any kind of analysis by itself, cannot do a lot of things without the support of a human experts to declare all the above and point you in a targeted direction.
The 4 questions of data analytics
Based on the aforementioned references, analysis needs something more to be evolved. So, before I continue you can see clearly that someone need to define the "What happened?", "Why did it happened?", "What it will happen?" and "What should be done?". You have already realized who is capable to answer the above questions. Another field that is significant to report is the machine learning that is under the predictive analysis that it will be described in another post, but basically the primary purpose is to make predictions. Now, you can see that some other questions raised, like what to predict and why to predict it. So everything has always to do with what and why.
Data Scientist imaginary world
Data science which is one of the most popular fields in our days is erected to provide the above answers. However, the percentage of people that have this knowledge to implement it and use it correctly to reach the seeking outcomes, is very low. Let me explain you at first what exactly a data scientist is doing in its everyday job.
The main responsibility is to define specific type of data, then to choose wisely an algorithm and methodology to use, and finally to predict and evaluate the results based on past data and experience. The procedure is the creation of prototypes that will be embedded in the business software if you have enough imagination to develop it. Of course this is the big picture, the general idea and it does not applies in real world.
The business world of data scientist
More and more responsibilities go everyday under the watch of a data scientist. One of them and based on my opinion the most significant one ... is the analysis of the data before the modeling part. This analysis may determine the direction on which machine learning algorithm will be used and what predictions will be awaited based on the past analyzed data.
But before we get there, a data scientist needs to analyze the idea and understand the problem that needs to be solved. After that, features and properties that it will be used for the machine learning need to be declared and identified. Finally, the processing of those data to bring them in life in a realistic flow and format will be the last step before we throw them in the powerful ML models that already exist.
The procedure that has been mentioned above is called business analysis and pre-processing of data. With simple words, is called "Analysis of Data" and can cover most of the questions mentioned above. In light of this, it is observable that 60% of the workflow that is related to data analysis is under the responsibilities of a data scientist.
The truth behind data analyst and scientist
So, everyone can make the question, "why then do we need the data analysts?". The answer is simple, data science is a specific field that in most of times is not needed to implement an analysis task. Its not necessary to predict something in a task or a problem. Maybe you will use analysis only to visualize something or store data in a specific format. So we can observe that its not a necessity to have in your team a data scientist, however, a data scientist is always responsible to know the field of data analysis in order to implement the next steps of his job.
Conclusion
Always those 2 fields, analysis and science, coexist to produce an outcome. Every single scientist of every field needs to analyze its own tests by providing plots, tables and other measures to declare the most significant observations in a more efficient way. Every report or project that is created by scientist contains a big section that is called analysis, but when it comes to data science field the analysis is something that cannot be avoided and cannot be ignored. You cannot avoid the analysis in data science especially when you are creating information from raw data without the involvement of any human being. As a final result, you cannot be a data scientist without to analyze data, and process them in order to prepare them accordingly to your needs.
Remember that always humans are the head of all the relationships between the science and analysis. Its up to us to decide what and why the machine will execute or predict.
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