Why software engineering best practices matter so much in data projects

Here at Datashift, we've seen the role of Business Intelligence (BI) evolve rapidly in recent years.

Only six years ago, for example, BI projects were the primary data focus of many of our clients. At the time, a BI project typically involved ETL processes, a data warehouse, and a visual reporting tool.

However, with the rise of modern data platforms and cloud first strategies, we’ve identified the need for a broader range of recommendations and capabilities. In our increasingly data-driven world, BI has become just one part of a much larger data ecosystem.

A host of new challenges for data teams

The evolution to larger data ecosystems has brought about a significant shift in how data teams work. Increasingly so, data teams adopt a product philosophy. They work together to create and manage data products designed to meet the needs of data consumers in the broadest possible sense, whether internal or external customers.

The shift in how they work has presented data teams with a host of new challenges, primarily in terms of the skills and expertise they need. Whereas they used to focus mainly on analytical skills, with the goal of monitoring and logging, the rise of data products has made software engineering principles and best practices way more important nowadays. After all, data products must meet customer requirements and expectations, just like any other type of (software) product. Data products must be well-documented and ready to use. In addition, internal and external customers must also have the assurance that quick action can be taken should something go wrong on their side.

As a result, data teams must be well-versed in various skills and technologies. Two main areas stand out: DevOps and the focus on writing quality code.

DevOps

DevOps integrates and automates the work of software development and IT operations. It involves everything related to product release management, from unit testing to continuous integration, versioning, and continuous delivery (CICD). While monitoring and logging are still critical in a BI context, they are now supplemented by the need for automated testing and alerting to catch any issues before they become critical at a customer. In some companies, the related data engineering skill set requires a specific kind of engineer: the DevOps engineer.

Writing quality code

Another result of the rise of data products is the new focus on writing quality code. Coding conventions (such as PEP8, for example), templates, readability, and documentation are all critical to ensuring that software code can be easily read and understood by others. In addition, with multiple technologies and platforms now in use, it is more important than ever to adhere to such standards to guarantee that software code remains maintainable over time.

Embracing software engineering best practices in data projects

Ultimately, all those skills and best practices are critical to creating robust data platforms and data products that meet the needs of both internal and external customers. And as the industry continues to evolve, those skills will only become more critical over time.

By embracing those best practices and skills today, your organization can avoid costly mistakes, ensuring data projects are scalable, maintainable, and secure. Contact us if you need help to have your data projects deliver the best possible outcome. We're always there to help.