Data is the new oil!
The sentence you may have heard many times. The statement is arguably true, for example in ancient times oil was the world’s most valuable resource. It was the key functionality of everything from the government to local companies. Without it, progress will stop and the economy will shrink.
Fast forward to 21 century, some of the world’s most valuable companies such as Google, Facebook, or Amazon base their entire business on the effective processing of personal data. Personal data is said to be the hottest commodity on the market in today’s network society. For example, the controversy by Cambridge Analytica who worked with Donald Trump’s election team was harvesting data on 50 million Facebook profiles to become an advertising campaign that could help swing the election. We could say that if you have data, you can rule the world.
Today, many companies are aware of the usefulness of data, from getting data until processing data to produce information that companies needed. So that comes the professions that are specifically related to data. So that comes the profession that is specifically related to data and I will discuss some of them. So these are the professions related to data:
Data architects work before the actual data exists. If the data is like a Building, before we make the Building we must first design what the Building will be like. Starting from the material used, the structure, to the design used. We certainly do not want the building not in accordance with our wishes so that the building will collapsed or damaged because we built it carelessly. Likewise with data, we certainly do not want our data to fall apart so that chaos will occur in the future.
Data engineers build and optimize systems that allow data scientists and data analysts to work. Each company depends on accurate and accessible data for each individual it works with. Data engineers ensure that data is properly received, transformed, stored, and made accessible to other users.
As an assumption, in oil and gas. Data engineer is the person whose role is to make pipes and drain crude oil into ready to use like gasoline. Data engineers are responsible for building data pipelines and often have to use complex tools and techniques to handle data at scale. Data engineers are more inclined towards a set of software development skills. The data engineer mindset is often more focused on development and optimization.
Data scientists are specialists who apply their expertise in statistics and building machine learning models to make predictions and become key business questions. It can be said that the data scientist has the duty to process data into insight.
A data scientist must be able to clean, analyze, and visualize data as well and be able to train and optimize machine learning models. In each company, the direction for data scientists may be different, for example the role of data scientists in car companies has different data requirements than restaurant companies. We take the example of a fintech company, data related to money, such as how much you can save, how much it costs, and also make future predictions using machine learning.
Data analysts provide value to their companies through data, using it to answer questions and communicate results to help business decisions. Common work normally done by data analysts is in line with data scientists, such as data cleaning, performing analysis, and creating data visualizations. But data analysts focus more on analysis and data communication.
Just like data scientists, the grouping of data analysis depends on the needs of the company and in what field the company is. Data analysts can be titled Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst, etc.
Regardless of the title, the data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions. Data analysts have the potential to transform traditional businesses into data-driven.
Data scientists bring an entirely new approach and perspective to understanding data. While an analyst might be able to describe trends and translate those results into business terms, scientists will ask new questions and can make models to make predictions based on new data.
Data analysts must be an effective bridge between different teams by analyzing new data, combining different reports, and translating the results. In turn, this is what enables the organization to maintain accurate pulse checks on its growth.
“Data Engineer, Data Science and Data Analyst — What the Difference?”– Fahmi Salman Nurfikri Tweet