What is the difference between data engineering and data science? Is one a superset of the other? Is one even more important than the other? This blog will discuss these differences in-depth.
The exponential growth in data has provided companies with access to a broad range of information on their customers, market, channels preference, and others. According to an estimate, 2.5 quintillion bytes of data are generated daily. The vast volumes of data allow companies to improve the quality of their products and services by leveraging insights derived through analysis of different data types.
Data is a strategic asset, and it comes in various formats, which can be classified into two groups, structured and unstructured data. Structured data, typically categorised as quantitative data, has been predefined and formatted before being stored in a data storage, which is a relational database. Unstructured data, typically categorised as qualitative data, does not have a predefined format and is stored in its native format in a non-relational database. Alternatively, cloud data lakes preserve the raw form of unstructured data. Recent research has indicated that 80% of the global data will be unstructured by 2025, and even enterprises prioritise unstructured data management.
The different data types have to be processed through steps before companies can meaningfully use them. Data engineering and data science are key functions that help enterprises with data management and analytics to help them with data-driven decision-making.
This is the Ultimate Comparison of Data Engineering vs. Data Science in 2022.
Then you are in the right place.
Let’s get started.
What is data engineering?
The value that an enterprise derives from data depends on the accuracy of the data and the efficiency with which it can access the data, which incidentally are the two main objectives of the data engineering function.
Data engineering helps enterprises design and build data pipelines that transform raw data and transport it into a format that is in a highly usable state by respective end-users, who can be data scientists, business stakeholders, apps, and other users. Data pipelines are sequences of processing steps applied to data for a specific objective, wherein the output from a step is the input for the next step, which continues until the pipeline is complete. The pipelines source the data from multiple disparate applications and systems and collate the data in a single warehouse that becomes a single source of truth across the enterprise. It also has to ensure data governance standards are followed to ensure data is consistent and trustworthy, and only authorised users are granted access to prevent misuse.
Data engineering had evolved from "information engineering," which first gained prominence in the 1980s when personal computers became popular and accelerated the information technology applications in businesses. As data became available to businesses, information engineering emerged to utilise applications data in their business. Initially, the term referred to database design and analytics.
With the advent of the internet in the 1990s and the rise of consumerization of enterprise IT in the 2000s, data volume and types increased exponentially, upending the business landscape. Data-enabled enterprises to create new revenue streams, improve customer acquisitions and retention, and create targeted marketing campaigns with a better return on investments (ROI). This required enterprises to build strong data foundations to create a data-enabled competitive advantage for their businesses. Information engineering evolved into data engineering as the need for reliable and secure data became important. The key responsibility of data engineering is to create a data infrastructure to enable access to the right data at the right time in the right format for different users.
Why do enterprises need data engineering?
The lack of reliable data infrastructure is one of the important challenges enterprises face for the success of their data science projects. According to the CTO of IBM, only 10% of data science projects make it to the production stage, which also resonates with the Gartner prediction that 85% of all Artificial Intelligence (AI) projects would eventually fail.
The key reason is the data, which is fragmented across different applications due to the highly siloed nature of the organisations and the failure of the teams to collaborate. The data silo is a reality that delays accessing and connecting with different data sources. Even as some cloud-native systems ensure fast, secure access to data in real-time, integration with other enterprise applications and legacy systems still proves to be a challenge.
In the early days of big data projects, the responsibility was to build the necessary infrastructure and data pipelines as part of data science functions. As enterprises accelerated their digital transformations, the need for secure and fast access to data became important, which led to the emergence of a distinct data engineering function. It helps to create a solid foundation for the success of enterprise big data analytics projects.
What is data science?
Data science is a multidisciplinary field that extracts actionable insights from many data enterprises collected through multiple business and internet applications. The function combines programming skills, mathematics, and statistics knowledge with business domain expertise to identify patterns, extract meaningful business insights, and present it in a visually appealing format.
Data science encompasses data preparation that can include cleansing, aggregating, and manipulating to prepare it for processing. The next step in analysis involves developing and using algorithms and data models to identify patterns converted to predictions after proper validations. The results are presented in an easy-to-understand format as charts and graphs using data visualisation (BI) tools. Advanced data science tools have allowed businesses to use data insights for different business use cases, which were not possible earlier.
How can data science help businesses?
The common uses of data science include anomaly detection, forecasting, voice and face recognition, pattern detection, and recommendation engines.
Some industry verticals where data science offers distinct business value are:
Banking and Financial Services
Anomaly detection using AI and Machine learning (ML) techniques in banking helps fraud detection and financial services firms monitor every transaction. Data science-enabled risk management helps banks and financial institutions generate fraud decisions in milliseconds and potentially deliver up to $1 trillion of value each year for the global banking industry.
Data science helps insurance companies detect fraudulent claims and automate claim processing, enabling them to process and settle claims within hours. Insurance companies are leveraging this unique advantage as a differentiator in the marketplace.
Data science helps the IT department prevent cyberattacks and security intrusions and solve users’ technical problems. Machine learning algorithms trained on previously detected malware help to identify and detect new malware through pattern recognition.
Healthcare and Life Sciences
The role of data science in healthcare will have a long-lasting impact on our lives. It is helping researchers find new treatment options for incurable diseases like cancer by providing access to patient data across the globe and finding new patterns and trends to advance research faster. Data science helps the general population in preventive healthcare with real-time data collection and health monitoring.
Data science helps augment manufacturing companies' predictive maintenance capabilities with predictive analytics. It helps companies save money by preventing downtime and failure and extends physical assets' life, improving return on investments(ROI). The companies use data science to optimise delivery routes and improve fuel efficiency in their logistics division. For your further reading, check out our in depth blog on how machine learning (ML) is revolutionizing the manufacturing industry.
Data science is also changing the competitive landscape in the retail, communications and media, travel and hospitality, energy, and utility industries with different business use-cases.
Data science will continue to evolve, and its application scope across industries will expand. It is important for you to understand emerging data science trends to be able to leverage analytics technologies effectively for your businesses.
Data Engineering vs. Data Science: A Quick Comparison
||Create framework and APIs for processing, storage, and retrieval of data from different data sources
||Develops statistical models to draw meaningful and useful insights from the raw data.
||Build and optimize data pipelines. Performance of complete data pipeline
||Development and optimization of ML / Statistical models
||Data infrastructure covering data flow, storage, and retrieval system.
||Data analysis products such as data recommendation engines, reports, and so on.
||Enterprise applications and internet platforms
||Data scientists, business analysts, apps, and others
||Business stakeholders and decision-makers
||Expertise in programming language and middleware, along with hardware-related knowledge.
||Statistics, mathematics, computer science, and business domain knowledge are required.
As the telecom industry evolves to the 5G network, it will act as a catalyst for innovations and new business opportunities by connecting humans and machines at an unprecedented scale. The high internet speed and fast download of 5G technology will further increase the data volume available to enterprises, and the data will become even more valuable.
A robust and reliable infrastructure will be key to enterprise efforts to leverage data as a business enabler. Data engineering relevance in your organisation's scheme of things will continue to rise with the increased application of AI and ML, which require careful consideration of storage, networking, and data processing needs. Creating a flexible and scalable infrastructure and optimising costs through competitively priced services for different end-uses will necessitate a distinct data engineering function.
Data science success depends on not just technical excellence but also soft skills, collaboration, and transparency. The team needs to collaboratively work with other stakeholders to identify the right business problem to solve and then build the relevant model. Data science needs to combine technology expertise with domain knowledge to derive outcomes that support decision-making.
As the strategic importance of data in business increases, the difference between data science and data engineering functions will become more pronounced. However, the collaboration among the two teams will be important to improve the success ratio. Data science and data engineering, even though distinct, need to work together to enable enterprises to realise the full business value of their data.
Check out the top 25 Data Science tools according to Zuci Systems, and if you need thorough expert engagement in your Data Science project, consider our data science and analytics services.