Data Modernization: A Problem for Data Engineers?

Data Modernization: A Problem for Data Engineers?

Data engineering is a growing field that brings together the physical world with the digital one. The problem is that data engineers are in short supply, and there aren't enough of them to go around. This article explores the issue of data engineers, whether they're necessary, what's causing the shortage, and if there are some other solutions to this problem.

What is data modernization?

Data modernization is a process of modernizing data through technology and best practices. Data modernization aims to improve the accuracy, completeness, and timeliness of data. Data modernization can be accomplished in various ways, including adopting new technologies, applying best practices, and reorganizing data.

The benefits of data modernization are numerous. More complete and accurate data enables organizations to improve customer relationships, optimize operations, and reduce waste. Improved accuracy leads to better decision-making and improved business performance. Delayed or inaccurate data can also lead to costly errors, such as incorrect pricing or billing information.

Data modernization is a complex process that requires careful planning and execution. Data modernization can lead to decreased efficiency and increased costs if done incorrectly. To ensure that data modernization is successful, organizations are very much dependent on the data engineering team.

Data engineering sometimes tends to limit, delay and block access to data. The process of data requests being stuck in IT land for weeks is widespread, which often brings about unauthorized circumventions to get the job done.

The Data Modernization Challenge

As data grows in complexity and volume, organizations face the daunting task of modernizing their data infrastructure to make it more efficient and accessible. This challenge is especially critical for data engineers responsible for transforming raw data into valuable insights that help businesses achieve their objectives. But most data engineers are not domain experts in the business process they are trying to support.

This is where analysts come in, who have the knowledge to translate business requirements into technical specifications for data engineers to build, test, and maintain. This has led to a situation where data engineers have little understanding of the business problem they are trying to solve. At the same time, analysts know what they need but don’t understand how it works or how to use technologies to get the data processed. While this may sound like enough reason for someone without domain expertise to step up, we found another: these days, most businesses spend more of their software budget on engineering than analysis — and the vast majority of that budget is spent on cloud-based tools.

We can see this clearly in survey results, which show that almost three-quarters (72%) of all software budgets are spent on data engineerings, such as ETL and BI. Meanwhile, less than a quarter (21%) of budgets go to data analysis — and most of those are tier-1 companies with more than 10,000 employees.

For example, one company spoke aloud: “Our data engineering team spends 70% of its time developing new tools, then 50% maintaining the existing ones.” In other words, many people have been fooled into thinking the only way to be data-driven is to spend money on data engineering. It’s often better to invest in your analysts — who, as we all know, drive the value of data — and then literate your existing stakeholders. That way, you get much more bang for your buck!

A solution for improving data engineering team productivity

Data owners know what they want out of their data but don’t know how to do it. On the other hand, data engineers have the know-how but don’t know why it is needed. This creates a problem where data engineers are not getting the value they should be from the data. Data Engineering at scale is a challenging problem with many facets that need to be addressed. It starts with the data engineer and data architect who understand the business objectives, and then it goes to the system administrators who need easy-to-use tools. From there, it’s up to the data scientist who develops models. Finally, the data analyst analyzes and visualizes them for business users to understand how various aspects can be improved. On top of that, there are plenty of other roles involved in this process, including quality assurance, testing, security, monitoring, analytics, etc.

A no-code data platform removes technical barriers, empowering non-technical users to manipulate data. Instead of a wall of computer and programming terms, companies can easily browse and interact with their data without requiring coding knowledge.


Data modernization is an essential task for data engineers, as it aims to create a single source of truth for the data that lives within an organization. However, with the growing complexity of modern data systems and the ever-growing demand for data services, this goal has become increasingly difficult. No-Code offers a solution to help modernize data systems with a new, automated way of thinking and better practices that the data-driven organization can quickly adapt.

Enjoying This Article?

Receive great content weekly with the ai-surge cloud Newsletter! Talk to our Low-code Data Fabric expert today!