Your data architecture is backward: Business process first, technology second
MAR. 3, 2025
2 Min Read
Drowning in data solutions but starving for results? Stop chasing tech trends and start with what matters—your business processes.
For IT leaders and solution architects, the pressure to modernize data infrastructure often leads to a common mistake: choosing technology before understanding business needs. This approach inevitably results in costly rework, data silos, and systems that don't deliver value.
This article provides a practical framework for designing data architecture that actually serves your business. The key? Start with your business processes, establish strong governance from day one, and let these guide your technical decisions—not the other way around.
New terms, same problems: Fundamentals still matter most
Every decade or so, a new data architecture emerges that claims to be the antidote to data complexity. Data warehouses were the answer. Then it was lakes. Now, lakehouses are in the spotlight.
But focusing too much on these buzzwords often distracts from the much more important work of building something that actually delivers.
The fundamentals of good data architecture haven’t changed:
- Your system needs to be able to handle both structured and unstructured data.
- Storage decisions should be rooted in what fits your data’s location and volume best. In some cases, that’s on-premises. In others, the cloud is a better choice.
- Integration should be prioritized above all else. No matter where your data lives, the success of your architecture hinges on how well your systems connect and share information.
- Continuous data governance must be established from day one to prevent silos and maintain consistency.
As Deny Watanabe, data engineer at Lumenalta, highlights, “Trends will come and go, but getting the basics right is what ensures your architecture delivers value where it counts.”
Success demands more than just following your business decisions
Business decisions are the foundation of solid data architecture, but they’re only part of the equation. The best solutions balance business needs with technical realities.
Your business processes are just the starting point
Business process mapping keeps your data architecture grounded in how your organization actually operates rather than theoretical capabilities.
This typically involves:
- Identifying decision points: What insights does leadership need to act? What operational data needs real-time access?
- Determining data needs: Which datasets are required at each decision point?
- Mapping out operational flows: How does my data move across systems and teams?
- Establishing governance checkpoints: Where do data models need review to maintain consistency?
Continuous data governance: The key to long-term success
One of the most overlooked aspects of successful data architecture is continuous data governance. This approach centralizes all data modeling decisions with a dedicated person or team, ensuring consistent naming standards, design strategies, and preventing the emergence of data silos.
The impact of poor governance can be severe. When different teams design their data structures independently, it often leads to:
- Unnecessary data duplication
- Redundant data synchronization processes that create new points of failure
- Inconsistent naming conventions that confuse stakeholders and complicate maintenance
- Increased technical debt from misaligned data models
While centralizing data governance may initially seem like it could slow development by adding an extra review layer, the long-term benefits far outweigh this cost:
- Consistent data modeling across all systems
- Reduced integration complexity
- Clearer documentation and easier onboarding
- Lower maintenance costs
- Better scalability
Source systems draw your technical boundaries
While business processes determine what your architecture should do, source systems put guardrails around what it can do. Where your data is housed, how much you have, and its overall quality all set limits on what’s feasible.
Forcing a one-size-fits-all approach is a recipe for frequent headaches—an on-premise system has different integration requirements than a cloud-native platform. Similarly, large, transaction-heavy datasets often need a data lake to handle scale, while smaller, structured data sets may run more efficiently in a warehouse.
Smart integration beats perfect architecture
Perfect data architecture is a myth, and pursuing it can do more harm than good. The best systems aren’t flawless—they’re built to work well within real-world constraints.
Your source systems are already telling you how to integrate
Fortunately, integration doesn’t start with a blank slate. Your source systems define what’s possible. Legacy systems often require custom solutions, while modern platforms allow for a more streamlined approach.
Data volume plays a key role, too. Large datasets demand efficient transformation strategies to keep things running smoothly. Staging layers can help manage this complexity, giving your architecture the flexibility to scale with your business.
The bottom line is that a smart integration plan should work within the constraints of your systems rather than fighting against them.
Dimensional models should mirror your business logic
Highly effective data models are built around business characteristics. Start with the key questions your teams need to answer and shape your model around them. You’ll be rewarded with faster queries, smoother performance, and a system that can adapt over time without costly overhauls.
Success lies in the space between business and technical reality
The best data architectures balance ambition with practicality. “A system designed in isolation, without considering real-world constraints, is destined to underdeliver,” notes Deny.
Avoid this fate by focusing on your business’s most critical decision points. What insights do teams need to act quickly? What data powers those decisions? Once you have that foundation, shift your attention to integration. Clean, consistent data is the foundation of reliable insights, and careful integration ensures nothing gets lost along the way.
And remember that designing an architecture isn’t a one-and-done process. The way your teams interact with data will evolve, and your architecture needs to evolve with it. Forget perfection—focus on creating a system that meets your needs today while being able to grow with you tomorrow. Most importantly, maintain strong data governance throughout this evolution to ensure your architecture remains consistent, maintainable, and aligned with your business objectives.
