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Thanks to new technologies and the inclination to digitization, millions of digital data are created daily. Companies feel the need to organize this data to extract the most important ones, enabling them to make the right business decisions. For that, you need data engineering. With the growth of open data and the evolution of computing and memory capabilities, the fields of application of data engineering have expanded significantly. It has now become at the center of the development of Industry 4.0.
In business, data helps organizations make business decisions. With new data engineering capabilities, Business Intelligence (BI), which once only allowed past transaction reporting, has evolved into a diagnostic and descriptive-analytical discipline that can operate on large amounts of data with remarkable precision and speed. Today, data engineers help companies accurately forecast sales, understand, and nurture customer relationships. In other words, those experts affect the revenue generated and allow businesses to stay competitive. So, what future developments and challenges await this vital field of science? Let’s find out.
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What is data engineering?
Since data engineering is a relatively new field of knowledge, it is worth recalling its definition and what specialists in data engineering services do.
Data engineering is a branch at the intersection of computer science and artificial intelligence. The IT systems around us are becoming more complex and generating vast amounts of complex data to interpret. AI allows you to make predictions using machine learning algorithms. In this way, we manage to valorize vast volumes of data (big data) within complex IT systems, taking into account the aspects of efficiency, reliability, and ethics. To achieve concrete results in industrial innovation, it is advisable to skillfully master and manage the complexity of information systems and the significant amount of data.
Data engineering is the surest way to achieve this goal because it enables highlighting and analyzing large amounts of data despite its complexity. Due to this, data engineering is currently used in many sectors, such as the pharmaceutical industry, finance, health, marketing, transport, and e-commerce. In addition, data engineering promotes the development of innovative solutions such as drones and autonomous cars and analytical or predictive applications that can accelerate several activities. The wide range of possibilities offered by data engineering means that we can use it to create new, innovative solutions in the future.
THE SCOPE OF DATA ENGINEERING ACTIVITIES
Data engineers are technical persons responsible for building, testing, and maintaining the data system, in particular:
- Ensuring data flow
- Data normalization and modeling
- Data cleaning
- Providing data availability
Data engineers are also responsible for finding the latest trends in datasets and creating efficient algorithms to make the data more useful. In addition, they generate data platforms that enable data scientists to analyze data and train machine learning (ML) models. Sometimes data engineers also need to analyze data and help data analysts integrate ML models with data pipelinesTherefore, data engineering experts must have technical skills, such as programming, mathematics, and computer science, and soft skills to properly communicate data trends to support business development.
Data engineering: future applications
Digitization affects all industry and service sectors, and therefore data engineers are necessary for companies from many different industries. On the market are also more and more SMEs and start-ups that offer specialized data engineering services, contributing to the development of many innovative applications, such as autonomous vehicles, drones for intelligent agriculture, or the invention of new drugs.
To date, data engineering has proven to be strategic in various fields. However, there is no doubt that we are starting to see new roles and applications for data engineering. To understand what the future lies in this exciting field of science, let’s look at two intriguing areas to which it can contribute effectively.
SAFETY OF AUTONOMOUS CARS
Currently, some companies use data engineering to develop a solution that allows autonomous cars to strengthen their safety by monitoring and predicting the movements of surrounding objects and people. Such a solution is possible thanks to the algorithm predicting the trajectory of moving objects.
SECURITY IN HIGH-RISK ENVIRONMENTS
Another equally notable example of data engineering is designing a mobile target recognition and tracking system to solve security and governance problems in high-risk environments. This solution is based on real-time image processing, considering modalities such as view stability or lighting conditions.
Data engineering for the development of AI and ML
Today, with the advent of cloud infrastructure and increasingly decentralized data teams, data engineers’ role becomes more critical as it allows us to optimize the different dimensions of data stacks. Thus, data engineering has a real impact on whether and how artificial intelligence and machine learning will develop because, indeed, to ensure their effective operation, it is essential to ensure data quality, especially training data.
ID engineers are specialists in capturing, storing, visualizing, and analyzing this large amount of information. They can implement appropriate strategies to control the data deluge that characterizes the information society in complete digital transformation. They know how to configure analytical or predictive applications by applying their knowledge and know-how to a wide range of data types to interact with different actors and professions. Therefore, data engineering is essential to the success of any Big Data project, especially in new technologies such as AI and ML. That explains the increase in job offers and the emergence of innovative applications.
Data engineering owes its well-established role to the popularity of AI and ML solutions. For example, you may have heard of the statistics in 2016 that data scientists spent as much as 60% of their time cleaning and collecting data. Data engineering removes these challenges from scientists and allows them to allocate resources to develop modern solutions. Moreover, by 2020, we were generating about 1.7 MB of new data per second per person on the planet. This data contains enormous values that we cannot gather without data engineering. So, there is no doubt that data engineering has an exciting future ahead of it, and its role and importance will only increase.