Every enterprise data warehouse gets messier over time. It’s just the nature of the beast. Quick fixes become permanent solutions. Temporary workarounds become critical processes. Requirements change, but old code stays because “it’s too risky to remove.”
In SAP Business Warehouse environments, this accumulated mess creates a hidden tax that most organizations don’t fully recognize until they try to make major changes.
What Technical Debt Actually Looks Like
In Business Warehouse environments, this complexity shows up in a few specific ways:
Transformation Rules Gone Wild: Years of business changes create layers of transformation logic. Someone needed a quick fix for month-end reporting in 2018, and now it’s somehow critical to the entire financial close process. Each new rule makes processing slower and creates more potential breaking points.
Data Models That Make No Sense: Business structures evolve, but BW data models just keep growing. You end up with hybrid structures that don’t really fit current needs or future plans. It’s like renovating a house by just adding rooms instead of thinking about the overall design.
Integration Spaghetti: Mature Business Warehouse systems connect to dozens of source systems through custom interfaces, many built before anyone thought about standards. Each one is a unique snowflake that requires special care and feeding.
Knowledge Silos: The really critical stuff often exists only in the heads of people who’ve been around forever. When they leave or go on vacation, simple changes become complex projects.
The Real Cost of Complexity
Here’s what’s interesting: research shows that technical debt compounds at about 15-25% per year. Systems literally become exponentially more expensive to change over time. In Business Warehouse, this shows up as:
Everything Takes Forever: Changes that used to take a few days now need weeks or months. You can’t just modify one thing without analyzing how it affects everything else.
Performance Gets Worse: Systems designed for smaller data volumes and simpler business rules struggle under current loads. Those overnight batch jobs keep getting longer.
Innovation Paralysis: The cost and complexity of making changes creates institutional resistance to new analytical requirements. The business learns to stop asking for things because IT always says it’s too hard.
Resource Drain: Most organizations spend 70-80% of their BW resources just keeping existing stuff running. There’s almost nothing left for new capabilities.
The Assessment Problem
Most organizations have no idea how bad their technical debt actually is because traditional monitoring focuses on whether systems are up and running, not whether they’re efficiently designed. This creates a dangerous blind spot. Technical debt often stays invisible until it becomes critical – like when you need to migrate to a new platform by a specific deadline.
To really understand what you’re dealing with, you need to look at multiple things at once:
What’s Actually Being Used: Which components contribute to business outcomes versus which ones just consume resources without adding value.
How Everything Connects: Understanding what breaks when you change something, and what the ripple effects might be.
Where the Bottlenecks Are: Finding the specific constraints that limit system performance and scalability.
How Complex the Logic Is: Measuring how much effort it takes to understand and modify existing transformation rules.
Data Quality Issues: Evaluating whether the information flowing through existing processes is reliable and accurate.
Your Options for Dealing with the Mess
Organizations approaching Business Warehouse modernization basically have three ways to handle technical debt:
Clean As You Go: Gradually remove unused stuff and simplify transformation logic while keeping everything running. This minimizes disruption but takes longer and might not fix fundamental architectural problems.
Pick Your Battles: Migrate the easy, high-value stuff first while keeping legacy systems for more complex requirements. This balances risk and reward but means you’re running hybrid systems for a while.
Start Fresh: Use migration as an opportunity to completely redesign data architecture according to current best practices and business requirements. This maximizes long-term benefits but requires significant investment and change management.
Each approach has trade-offs. The right choice depends on your specific situation, risk tolerance, and strategic goals.
Why This Matters Beyond IT
Technical debt isn’t just an IT problem – it’s a business constraint. When data systems are difficult and expensive to modify, business agility suffers. Market opportunities get missed because analytical support takes too long to implement. Competitive threats go unrecognized because existing reporting can’t provide the right insights quickly enough.
The 2027 BW support deadline creates a rare opportunity to address these accumulated constraints systematically. Organizations that approach this transition strategically – by first understanding and then addressing their technical debt – position themselves for significantly better business outcomes.
The question isn’t whether to deal with technical debt. It’s whether to do it proactively during planned modernization, or reactively when systems fail to meet evolving business requirements.
In the long run, the proactive approach is a lot less painful.
Extra Resources
- Migrating SAP BW and HANA Data Warehouses: Discover how SAP Datasphere and Business Data Cloud are reshaping enterprise data strategies
- Bridging the Gap: How SAP and Databricks are shaping the future of enterprise data
- Modernizing Data Platforms for AI Readiness in the Cloud: Discover strategies for transitioning legacy data system to modern cloud architectures capable of supporting advanced AI workloads.
