Business teams always demand high-quality data at an accelerated pace and generate more ad-hoc requests, and Data Engineers are already overwhelmed with yesterday’s requests. This leads to Data Engineers applying temporary fixes and creating one-off data pipelines that are not extensible and are not built to last. This approach often results in a snowball effect of changes and bugs that require urgent attention, resulting in inefficient use of resources and increased costs for the organization.
So is there a way to create a cost-effective Data Engineering practice, not just by throwing more bodies into the problem?
These are a few things that might help to improve this situation.
It’s estimated that around 80% of the data generated by an organization is never used. This data bloat can result in increased storage costs and unnecessary processing time. It also takes precious time from your Data Engineering Team. They need to maintain that data and pipelines that someone asked to build, and now they are just there. Try to establish at least minimal ownership of the data. That will help to find who is using and, more importantly, not using data and perform a periodic housekeeping exercise.
Data Governance always helps
To minimize data bloat and ensure data quality, it’s crucial to establish at least some Data Governance procedures. But don’t try to boil the ocean. Agile Data Governance is a thing. A data dictionary, data contracts, and data quality controls are the first things. Data Governance is a journey, not a destination. But every little step helps tremendously.
Build a Data Platform Foundation
It may sound like a big thing, but every Data Engineering team starts thinking about its own data platform. It might be your own set of standards and templates using standard tools, but the sooner you start building your own reusable components, the betteit’s’s going to be in terms of support and maintenance.
Create a Robust Semantic Layer
The semantic layer is usually underestimated. And many Data Engineers and managers think it’s only needed for big companies. Well, in 2021, the average number of SaaS apps companies used was 110, so even if you’re not in the data aggregation or data integration business, it’s essential to invest in a robust Semantic Layer earlier to avoid a big project later on.
Use No-Code Toolset
Many tasks that are traditionally assigned to Data Engineers could be accomplished with low-code or no-code tools by Data Analysts. By empowering Data Analysts with no-code tools, organizations can reduce the burden on the Data Engineering team and improve overall efficiency. This allows the Data Engineering team to focus on more complex tasks while Data Analysts can quickly and easily build and analyze data models.
Shifting the mindset from building individual pipelines to constructing a good foundation of the Company’s Data Platform that empowers multiple teams to build on top of it is essential for optimizing your Data Engineering department. Focus on Data Ownership, start outlining Data Governance procedures, and invest in a robust semantic layer. Those things will help a lot very quickly. In addition, equipping Data Analysts with no-code tools early can be a big win. These strategies can help reduce costs, improve efficiency, and quickly and efficiently deliver high-quality data.