Data engineering and data science are emerging as the most crucial tools used by businesses for mining data diamonds – the new password for industrial success in the digital age. However, the primary barrier is created when the basic differences between data engineering and data science are not realised by decision makers in organizations that are willing to leverage these technologies. This article elucidates the nuances of both these terms and help business executives understand where these tools can be used to dig deeper for real data diamonds and process them for the growth of their businesses.
The importance of data in the growth of businesses in the Information Age is not exactly a brand-new concept. In fact, the applications of various data processing technologies including data engineering and data science, and how they can transform business processes have been discussed since a very long time.
However, we are still far away from the world where data engineering and data science will be mainstream. A Gartner’s study dated back in 2017 found that nearly 85% of Big Data implementations have failed. Another McKinsey study finds that over 70% of the attempts of business to transform their operations with digitalization fail.
Understanding the Basics of Data Technologies to Mine the Most Valuable Data Diamonds
There are a wide range of reasons why most businesses today fail to fully leverage business intelligence and other tools integrated with data engineering and data science technologies. However, one of the most prominent one of them is that technical authorities, even in leading businesses, fail to identify the fundamental priorities and lack the understanding of the basics of these technologies.
It is immensely important to get the hang of the terms like data engineering and data science way before making a decision to implement them in business. This article will help decision makers in various fields to understand the difference between data engineering vs data science to ensure the effective utilization of data processing tools and prevent failures in different business applications.
The article will also facilitate them to realise which one of these tools must be implemented for realising better business productivity and efficiency, especially when the global outbreak of COVID-19 has caused a slump in the world economy.
Data Engineering vs Data Science: Defining the Concepts
The entire concept of data engineering revolves around the word ‘engineering’ in this term, which broadly depicts the operations related to designing and building data models. Data engineering is primarily related to designing and building of data pipelines and development of data processing, storage, and retrieval from multiple sources from where the data enters various frameworks.
On the other hand, data science is the broader term that involves methods and tools used for actual processing of data, which can be either structured or unstructured. Data science is a branch of computer science that explains how data can be effectively processed to transform it into useful and tangible business insights.
To explain in simpler terms, data engineers can be analogous to the plumbers of data pipelines that are involved in the process of data warehousing, and they are the ones who design the entire data flow across the organization. Whereas, data scientists are the real artists who do the actual data storytelling. Data scientists are involved in the process who actually process the data that is streamlined by data engineers to perform the actual business-oriented function on those datasets.
Identifying the Key Focus Areas of Data Engineering vs Data Science
The key focus areas in the field of data engineering include programming and knowledge about middleware and hardware. Data engineers are required to thoroughly understand programming languages such as C++, Python, R, Scala, and Java. Building Application Programming Integrace (API) is the main area where data engineers lay foundation for making data scientists’ jobs more productive.
Whereas, data science, as a discipline, is a combination of various branches of science such as mathematics, computer science, and statistics. With the in-depth understanding of these branches of science along with the knowledge of various programming languages, data scientists can improve focus on and the efficiency of data processing operations.
Prominent Responsibilities of Data Engineer vs Data Scientist in Industry
Data engineering mainly revolves around optimizing the data pipeline to ensure the consistent and smooth flow of data across various business units. Data engineers are primarily responsible for data governance and carrying out quality checks for different data models to ensure high performance of data pipelines. In other words, data engineers develop, build, test, and maintain all the data architectures to ensure that data scientists and all the other business units have access to the latest and most accurate data.
Data scientists in any organization are primarily responsible for crafting data-driven solutions and insights to solve multiple challenges that the business faces on different levels. The role of data scientist becomes more important especially when applications of advanced technologies such as Artificial Intelligence and Machine Learning come into the picture. By the virtue of implementing advanced data analytics tools integrated with these technologies, data scientists can dramatically improve a company’s existing line of products and services.
Do Data Engineers and Data Scientists Complement Each Other?
Though ‘data is the new oil’, most businesses fail to identify the appropriate tool to extract it from different reservoirs. So the final question is which one of the advanced data technologies such as data engineering and data science will help boost businesses across the world.
Currently, data scientists are witnessing high demand across the world; they are also paid handsome salaries. Changing roles on data teams in most organizations indicate that the role of data scientists mainly revolves around data analytics and modeling, as it ultimately helps the business growth by acquiring accurate and latest insights.
However, the recent advancements in data analytics, data engineering, and data science are bringing in new trends where data teams are undergoing tectonic changes. Most organizations are revising the role of data scientists for strategic development of business solutions, with the support of data engineering.
Businesses – including industry leaders – will have to contemplate their ‘do it all’ approach towards the role of data scientists in the organization. Instead, alliance between data scientists and data engineers will prove to be a linchpin for businesses to ensure effective performance of data pipelines and accuracy of data-driven insights.