As a data analyst, I’ve always wondered who’s responsible for ensuring the cleanliness of our source tables. Is it the business expert who creates the data, the data engineer who loads it, or me, the data analyst who uses it for insights?
I’ve been in this field for about 3 years now, and I’ve seen my fair share of data quality issues – incorrect labels, wrong values, missing entries, and more. It’s frustrating, but it’s also a crucial part of our work.
The Importance of Data Quality
Good data quality is essential for reliable insights and decision-making. If our data is dirty, our analysis is flawed, and our recommendations are suspect. It’s like trying to build a house on shaky ground.
The Silver Layer Conundrum
In our data architecture, we have a silver layer where our raw data is processed and transformed. This is where data quality issues often arise. But who’s responsible for catching these errors?
Or is it the business expert who creates the data in the first place? They’re closest to the source, but they might not have the technical expertise to spot data quality issues.
Or is it me, the data analyst, who uses the data to build reports and dashboards? I’m the one who’ll notice if the data is wonky, but should I be responsible for cleaning it up too?
The Answer: It’s a Team Effort
The truth is, data quality is everyone’s responsibility. It’s a team effort that requires collaboration and communication across the entire data pipeline.
Data engineers should design ETL processes that catch errors and inconsistencies. Business experts should ensure their data is accurate and complete before handing it off. And data analysts like me should be vigilant about spotting data quality issues and speaking up.
Conclusion
Data quality is too important to leave to chance. We need to work together to ensure our data is clean, reliable, and trustworthy. So, who’s responsible for data quality? We all are.
Further reading: Data Quality: The Foundation of Data Science