Data Engineering Practices to Avoid

Data engineers are increasingly in high demand, especially as more company leaders realize it’s necessary to use reliable information for better decision-making. However, even the most skilled and experienced professionals can make mistakes. Here are some of them and how to steer clear of these blunders. 

Preventing Safe and Effective Data Collaboration

Data usage does not happen in a vacuum. The times when only a few people or departments have access to information are in the past. It’s now standard practice for employees throughout organizations to use and add to databases. As a result, data engineers must incorporate collaboration capabilities into their design and management of information pipelines. 

However, data engineers must also recognize how people may work with information in isolation. The best way is to use tools and create environments that let employees handle them safely while working independently or with colleagues. 

Failing to Learn How Businesses Will Use Data

Data engineers lose valuable opportunities and waste precious time if they don’t engage in productive discussions about how people use data and why. For example, some companies use it to track customer trends, while others do so to stop fraud.

Those are two valid but different reasons to rely on company data. A critical part of a data engineer’s job is learning about what business leaders and other affected parties hope and expect to do with the information a company has. From there, they can build solutions that surpass expectations and remain relevant over the long term. 

Underestimating the Ramifications of Poor-Quality Data

Low-quality information can lead to questionable results, making people lose confidence in data-backed operations. Sometimes, the problem can even cause reputational damage. For example, a 2022 study found nearly half of respondents most often measure data quality by the number of customer complaints received. Unfortunately, however, it could be too late when things are at that stage. 

Another worrisome takeaway from the research was that, on average, companies spend almost 800 hours per month resolving data-quality problems. Engineers should strongly consider spending their time differently by focusing on aspects that will lead to better-quality information from the start. Of course, no solution eliminates every error, but if data engineers can prevent most issues, they’ll have more time to spend on productive activities that help businesses grow. 

Overlooking the Need for Data Security

Resolving a hack can cost millions of dollars. Plus, statistics indicate small and medium-sized businesses are data breach targets 43% of the time. Yet, those entities typically have comparatively fewer resources to put toward recovery. The costs could encompass cybersecurity experts, public relations professionals, and regulatory fines, among other expenses. 

Data engineers are not solely responsible for keeping a company’s information safe. They’ll typically work with cybersecurity or IT teams. However, they must give ongoing input on maintaining information’s safety as it moves through the organization. Discussions should also center on strategies to keep unauthorized parties from accessing it, whether such attempts happen outside or within the organization.

Falling Behind With Data Access Options

Data engineers that mention which parties can access information and for what reasons must also ensure they encourage relevant individuals to use modern data access tools and strategies. Otherwise, people may find that the current processes keep them from doing their jobs well. Some could even try to circumvent the procedures out of desperation, putting businesses at elevated risks. 

A 2023 Immuta survey found 46% of data practitioners believed their organizations’ outdated access policies made it harder for people to work. Moreover, 51% of respondents said legacy policies prevented them from scaling their security options. These are just some of the many reasons why data engineers must continually highlight the pervasive problems of having access policies that no longer suit current needs. 

Investing in Overly Complicated Products or Solutions

The market has many options to help data engineers do their jobs better. The problem is that they’re not equally suitable, and there’s no guarantee that popular solutions are the best for particular businesses. Data engineers must strive for simplicity, making their code easy for others to understand and follow. 

Another best practice is to keep the approach as modular as possible. Then, if something breaks or otherwise functions unexpectedly, data engineers will find it easier to troubleshoot the issue and prevent future mishaps. 

Maintaining too Many Manual Processes

A data engineering career comes with numerous challenges. The role will likely prove even tougher for those who use manual processes when automated options exist. A 2021 survey of data engineers showed 97% burnout from their daily work. When asked about the reasons behind the overwhelm, some mentioned manual and repetitive processes for data preparation and pipeline management. Others said their colleagues ask for too many things without providing adequate time to meet their needs. 

Fortunately, automation is well-suited to many data engineering steps. Automation can assist in moving, collecting, and preparing the content for later use. Now is an excellent time for data engineers to explore this option, even if they currently have manageable workloads.

Be Proactive for a Smoother Experience

These are some of the most common data engineering mistakes, and knowing about them makes it easier to recognize and avoid these problems. Furthermore, doing that will make data engineers more likely to have mutually beneficial outcomes while working in their roles.