Every successful business in our digitally-powered global society relies on data. Almost all technology-enabled business processes generate essential data that is gathered, stored, and analysed to inform crucial business decisions, strategies, and products. This is why every modern business values the role of Data Engineers, Data Analysts, and Data Scientists as they are responsible for revealing the bigger picture in a sea of 0s and 1s.
As crucial to businesses as these roles are, there is still some confusion about what each entails, how they function together, and the true potential of their impact.
Let’s explore the key differences between a Data Engineer, a Data Analyst, and a Data Scientist and the skills required to take on the position.
Before we dive into the elements of these roles, let’s first understand how important data is in our technology-driven business landscape.
‘Data is the new oil’ is a term often used to emphasise data’s significance in shaping our modern world. Much like how the shift from coal to oil gave birth to modern industrialisation, data is what fuels the binary fabric of our digital society.
Every digital interaction you perform is monitored and tracked to generate valuable data. From the time you are on a site and where your cursor hovers to what your purchasing preferences are and what your user journey looks like (and everything in between) – it’s all gathered. That data is then used to either inform business decisions or train powerful technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processes (NLP), to name a few.
User data paints a picture of how a website, app, or product is performing in real-time. Based on the data, businesses can modify and tweak elements to improve services and adjust strategies on the fly.
In short, data is highly valuable in modern society as it:
As you can see, data is incredibly useful to drive business growth in the 21st century. This is why being able to gather, store, analyse, and use data is crucial for all digitally-powered businesses.
Data Engineers, Data Analysts, and Data Scientists each play an essential role in helping businesses understand data to inform valuable businesses decision and drive growth. Let’s find out more about what each role comprises.
Data Engineers develop data architecture, programs, and systems that collect, manage, transform, and structure a business’s data. They are tasked with generating data into meaningful layouts that help Data Analysts and Data Scientists perform their jobs. Basically, everything that happens to the data before it reaches the database is handled by Data Engineers.
Data analysts turn incomprehensible data into easily understandable and actionable insights to help teams make business decisions. Essentially, they explain the story that the data is telling to stakeholders and business teams using visualisations to provide clarity regarding performance. As data is a record of past interactions, Data Analysts study past performances to provide clarity about what was and what is.
Data Scientists perform advanced statistical analyses and study large datasets to reveal deep insights and uncover patterns. From this, they develop algorithms and make predictions that paint a bigger picture of business performance. If Data Analysts focus on what was and what is, then Data Scientists are able to predict what will be. Using Machine Learning (ML) and Deep Learning (DL), Data Scientists find patterns in data to make predictions that inform business decisions.
As highly technical professions, these roles require particular skills and knowledge to perform. Here is what you will need to know to become a data specialist.
Data engineering is a very technical function that requires strong programming skills and an understanding of algorithms, as well as knowledge of engineering and testing tools. As Data Engineers essentially develop and manage data pipelines, it is important to be skilled in data modelling and data generation. Software development skills are mandatory – Java and Python languages – and data maintenance tools such as SQL.
Data analysis involves gathering data and compiling various reports that display the data clearly. Thus, statistical knowledge and numerical expertise together with reporting, data visualisation, and cross-functional communication skills are essential for this role. As Data Analysts work closely with Business Intelligence teams, experience with data visualisation tools such as Tableu and Power BI is also a must. Technical coding and language skills with SQL and Python are also very important. An understanding of Machine Learning (ML) is very beneficial too. Apart from these technical skills, the role requires impeccable analytical and problem-solving skills and the ability to clearly communicate important information taken from data.
Data science involves data transformation and cleaning to identify and categorise patterns within the data. As such, fluency in mathematics, especially algebra and statistics, is a must. Data scientists need to pull insights from both unstructured and structured data which requires proficiency in coding languages such as SQL, NoSQL, Python, Ruby, and SAS. Data Scientists need to be experienced in using big data tools. Additionally, they must be able to write algorithms from scratch.
Data specialists are the handlers of modern society’s most valuable asset. Think of them as the diggers, miners, transporters, and distributors of modern-day oil. Without them, this precious element that drives our digital world would not be possible.
Each role works with data in a different way that requires a unique set of skills and expertise to perform. They all work together and would not be able to function without the other. So, understanding how each data specialist performs their function will enable you to understand just how important data is in driving our modern world forward.
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