Beware the Data Warehouse
How the data warehouse helps and hurts your data initiative
The data warehouse can be your best friend, but it can also bite you when you don’t expect it. That’s as far as I’ll take this strained dog analogy. The point is: When you are leading a data initiative, you want to be mindful of the relationship to the data warehouse. This article gives you strategies for managing that relationship.
Your organization has a data warehouse. Or a data lake. Maybe a lakehouse? Or Jim, who collects database dumps as CSV files on an FTP server to compile the quarterly report. Whatever the level of maturity and technologies, most organizations have some process for collecting data for business intelligence. Or multiple. More importantly, there is a team managing the data warehouse.
If you are leading a data initiative to build new products or improve existing processes, that team can help you out but it can also make your life harder. Here are 7 reasons why and strategies for managing them.
1. Data Access
Data access is the foundation of any data initiative. You can’t build without data. The data warehousing team has likely already figured out how to get access to key data sources—which is a huge head start. You can learn from them, reuse ETL pipelines, or tap into relationships they’ve established with operational teams to negotiate your own access.
But tread carefully. It’s common for teams to shrug off your access requests with, "Just get it from the data warehouse." Sometimes that’s fine. But if you need live transactional updates and the warehouse only provides daily snapshots, you're stuck.
Use the warehouse team as an ally: plug into their ETL pipelines if they meet your needs. If not, use their relationships as a jumping-off point to request access directly. Set expectations early about why your project might need different access paths—especially for real-time or mission-critical datasets.
2. Data Technologies
The data warehousing team has already made a lot of decisions about how data moves from point A to point B. That includes vendor choices, contracts, and pipeline infrastructure. If you can reuse those decisions, it saves a lot of time.
However, those technologies might not meet the needs of your use case. Warehouses are often optimized for batch analytics, not real-time interactions or high concurrency workloads. If you need fast queries or streaming data, you may need an entirely different stack.
So, take inventory early. What tech is in place? What can you borrow? And where will it fall short? Build your business case for new tools before you find yourself defending choices against someone else’s legacy decisions when a VP asserts that “our data warehouse tech is sufficient”.
3. Data Governance
The warehouse team has probably already mapped out what data is sensitive, what counts as PII, and what needs to be locked down. In regulated industries, this work is substantial, and it can save you a lot of time.
But governance can be a swamp. If you rely on the warehouse team to manage all data compliance, you might get stonewalled. They may say, "Sorry, we can't give you that feed because our setup doesn't allow it."
Instead, learn what rules apply to which datasets and go directly to the source when needed. Collaborate with legal or data stewards to find a path that meets governance requirements without getting stuck in someone else's constraints.
4. Expertise
Data warehousing teams usually have strong data experts—people who know how to model, clean, and move data. This can be a huge asset. You can borrow ideas, ask for guidance, and avoid reinventing the wheel.
But this can also become a bottleneck. If your project becomes dependent on warehouse staff to make progress, you’re now blocked by their timelines and priorities. That slows everyone down.
Instead, aim to build a self-sufficient team. Use warehouse experts as advisors, not gatekeepers. Take what you can, but invest in your own data chops too.
5. Organizational Politics
The data warehouse team has clout. They’ve been around. They probably report to someone with budget power. Your project, however cool, is the new kid.
That can cut both ways. Their support can give you credibility and momentum. But their skepticism can raise roadblocks, especially if your initiative is seen as stepping on their turf.
Don’t trigger turf wars. Find ways to collaborate and align interests. Show how your work builds on their foundation rather than replacing it. And make sure leadership is clear on why your goals require different paths—and how those paths benefit the larger organization.
6. Standardization
If the data warehouse has been around for a while, they’ve probably established certain standards—for data formats, processes, governance, lineage, and more. These standards can be really helpful if they align with your needs.
But standards also come with overhead. They can slow you down, especially when you're still figuring things out. Early in a data initiative, speed and experimentation matter more than consistency.
Don’t get bogged down in standards negotiations. If a standard helps, great. If it doesn’t, make sure you can opt out. Premature standardization is just another form of premature optimization—and a common trap for data teams trying to move fast.
7. Cost
Last but most insidious: cost. Many data initiatives have been steamrolled because leadership gets nervous about the budget vis-a-vis the warehouse. "Can we just use the warehouse for this?"
On paper, consolidating everything into one platform sounds like a smart move. Half the cost, same data, right? But that argument misses the point. Your initiative isn’t duplicative—it’s additive. The warehouse supports analytics; you’re trying to build new capabilities.
Make that distinction clear early and often. Tie your costs directly to the value you’re creating. If cutting your initiative means losing that value, leadership needs to see that plainly—without getting lost in the technical details of speeds and feeds.
The data warehouse can be an accelerant for your data initiative. Borrow the data, technologies, standards, expertise, and governance that you need. But be mindful of the obstacles and trapdoors the data warehouse can put in your path. If you don’t manage the relationship carefully, it can become a source of friction. Be strategic. Borrow what you can, build what you must, and keep your initiative moving forward.