Data Warehouse Construction Standards - Why

1. Why We Need Data Warehouse Standards

The adage “No rules, no success” underscores the critical importance of established standards in ensuring optimal team performance and high-quality deliverables. In the absence of such standards, operational efficiency and collaboration may suffer, leading to potentially chaotic outcomes.

Have you encountered similar issues in your work?

• Received a requirement and not sure which table to pull data from. Table A seems feasible, while table B also appears to work. Asked colleague A, and they said they always pull from table C. Spent a long time exploring these three tables but couldn’t match them up. Oh well, let me calculate from the source again, and then a new table D appeared.
• I’ve noticed that there are thousands of tables in our database, but I only use a handful. So, what’s the point of all these other ones? I asked my colleagues, but nobody seems to know. Should I just get rid of them? Nobody else is touching them anyway.
• I got tasked with investigating an error in our process after my boss asked me to take a look. But man, the code is a total mess! I can’t make heads or tails of it. Plus, I’ve been searching for what feels like forever but still can’t find the upstream dependencies. What a headache!
• My coworker bailed on our project, and now I’m stuck with their share of the work. I’ve been grinding away for weeks, but I just can’t seem to wrap my head around it. It’s like they dumped a ton of unfinished business on me, and I’m feeling pretty frustrated myself now. Maybe it’s time for me to look for a new gig too…

Our data warehouse team’s performance has taken a hit due to all the issues we’ve faced lately. Efficiency, output quality, job satisfaction – you name it. And let me tell you, it’s usually the hardest-working and most loyal employees who bear the brunt of all these problems. It’s just not right.

If you’ve ever worked in data development, you know the pain I’m talking about. I mean, who hasn’t experienced some of these frustrations, right? So, what’s going on here? In my humble opinion, it all boils down to a lack of standards or proper implementation. And hey, I get it – sometimes business demands are tight, and shortcuts gotta be taken. But, that technical debt better be paid off pronto. Blaming employees for that ain’t cool. Leadership needs to own up to it.

Think of a data warehouse like a digital construction project – it’s the intangible result of our data engineers’ hard work. Data standards are like the blueprints for building this system, serving as both the instruction manual and translator for data usage. And just like how you need quality control in construction, we gotta ensure data quality too. But here’s the thing – for our data system to really thrive, we need to move away from relying on individual judgment and toward standardized, tool-driven management. That way, we can scale sustainably and keep the system healthy.