5 Strategic Mistakes To Avoid In Data Engineering In 2023

Several industries are investing in data science and engineering for the simple reason that it offers them a competitive advantage in their respective spheres. When you have easily understandable...
Data Engineering

Several industries are investing in data science and engineering for the simple reason that it offers them a competitive advantage in their respective spheres. When you have easily understandable data that can be analyzed, compared, and can form a basis for action, you have the right data engineering strategy in place. But you are still quite prone to making mistakes. Let’s have a look at what they can be so that you can avoid them in 2023:

Forgetting To Check Data Accuracy

As an enterprise, you might have to deal with various kinds of data, statistics, numbers, trends, and information. It can be social media data, search engine information, data related to orders, or financial data. The key here is to check for data accuracy. It is not a good idea to rely on information, especially numbers that cannot be cross-checked or verified.

Not Keeping Your Functions Simple

It is a very big mistake. Most data engineers would end up thinking that the common man can understand data structures and frameworks as well as they can. This is not the case. They must focus on designing simple functions that are aimed at performing specific tasks. This makes it easier for the consumer of that data to identify and analyze the information. The functions should also be made reusable which is a very good practice if you want to keep the data readable and usable for multiple parties.

Not Creating Adequate Backup

Having source control is very important. In a situation where you accidentally delete a file or a folder or have changed a code, it will become very difficult to revert to it if you do not have an adequate and relevant backup. When you decide to Outsource Data Engineering, you will be required to keep backup copies so that all your IT files can be retrieved to their original location. For example, if you change any data in a given table without creating a backup for it, you will have to manually update your table with your original values. This increases your data engineering costs.

Naming Your Code Function

The easiest way to make your data readable and usable by everyone is to use proper naming conventions. You can maintain all your data and records if you are clear and detailed about how you name your code functions. Usually, data engineers would go for a code that allows for self-documentation. This allows for an easy understanding of data and the entire framework becomes much simpler and more efficient.

Forgot To Think About The End User?

This is also a very critical mistake. Always think about the end user. Make sure that your data structure is user-friendly. Also, ascertain whether the end user is SQL-savvy or not. They should also understand a little bit about data models and should have tools and programs at their disposal so that they can consume that data more quickly.

Final Thoughts

These are technical and critical mistakes. If you can avoid them, you will have a very easy data structure that is not only efficient but also proves to be a cost-saving tool in the long haul.

Read More: Defining Data Encryption and Its Importance.

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