by Subhashini Rao
Digital transformation promises modernization and efficiency, but organizations often discover an expensive hidden cost: technical debt accumulation driven by insufficient business process knowledge. As companies migrate to cloud platforms like Snowflake, this debt manifests not in code, but in over-engineered architectures, redundant workflows, and misaligned infrastructure investments.
When organizations lack comprehensive understanding of their current business processes and data workflows, they inevitably fall into what is known as the legacy replication anti-pattern. Without knowing why specific data transformations exist, how frequently data is accessed, or which business rules remain relevant, teams migrate entire processing workflows that may no longer serve legitimate business purposes.
This knowledge deficit creates multiple layers of technical debt:
- Pipeline Redundancy Debt: Duplicated data processing across disconnected systems;
- Transformation Complexity Debt: Obsolete data enrichment steps unnecessarily carried forward;
- Infrastructure Sizing Debt: Over-provisioned compute resources based on flawed assumptions;
- Storage Retention Debt: Accumulating data that serves no active business purpose.
Real-World Impact: The Location Data Example
Consider Company A, which maintains a location database for marketing applications using APIs from vendor Company B. During their aggressive Snowflake migration, Company A:
- Migrates their on-premises location database to Snowflake;
- Develops new data pipelines to write API data to Snowflake;
- Incurs multiple cost layers, including continued API licensing and application maintenance; new pipeline development and maintenance in Snowflake; data integration tool costs; security and governance, and both legacy and new technical debt.
If Company B (Precisely, AccuWeather, subsidiary of Company A, etc.) maintained published location data on Snowflake Marketplace, then Company A can eliminate most costs through direct data sharing from Company B. With two divisions operating in silos, both technical debt and costs double, a common pattern in large organizations.
Strategic Technical Debt Reduction Framework
Organizations must strategically utilize modernization efforts to reduce overall technical debt through integrated data management practices.
Phase 1: Establish Foundation
First, organizations must work to develop a data governance framework that clearly defines roles, policies, and standards. The framework also should establish data quality metrics and create approval processes for data initiatives.
In addition, organizations must create a data cataloging initiative that captures inventory using existing data sources, documents metadata and lineage, and identifies current technical debt and redundancy.
Phase 2: Create Reusable Assets
After establishing a foundation, organizations can move into asset creation. This involves the development of published data sets, including identifying high value, commonly used data, applying governance standards and creating documentation and access controls.
As part of this process, organizations must conduct technical debt remediation, prioritizing based on business impact, consolidating redundant systems, and improving data quality.
Phase 3: Optimize and Scale
The final step begins with process redundancy elimination, including standardizing published data sets, retiring duplicate systems and establishing reuse-first policies. After that, teams can work toward continuously improving systems by monitoring catalog usage, tracking technical debt metrics, and refining governance based on feedback.
Measuring Success
Is the new system working? Key Performance Indicators can provide some guidance:
- Data Governance: Policy compliance rates, data quality scores, security incident reduction;
- Published Data Sets: Number of reusable data products, adoption rates, time-to-insight improvements;
- Data Cataloging: Asset coverage, user engagement, data discovery speed;
- Technical Debt: Remediation velocity, system complexity reduction, maintenance cost decreases;
- Process Redundancy: Duplicate system elimination, resource reallocation to innovation.
Ultimately, success requires treating these five elements as an integrated system:
- Data Governance provides the foundation and rules;
- Data Cataloging provides visibility and discovery;
- Published Data Sets provide reusable, quality assets;
- Technical Debt Management maintains system health;
- Process Redundancy Elimination maximizes efficiency.
Digital transformation failures often stem from treating technology migration as a technical problem rather than a business process optimization opportunity. Organizations that invest in understanding their current state, eliminating redundancies, and building reusable data assets will achieve true transformation rather than expensive technical debt accumulation.
The choice is clear: invest in knowledge and governance upfront or pay the compounding costs of technical debt indefinitely. Smart organizations choose strategic transformation over costly migration.
Does your organization need help taking on your tech debt and undertaking a successful digital transformation? Reach out to evolv.
Subhashini Rao is a seasoned data strategist and architect with 25+ years of experience. With deep expertise in cloud, big data, and AI technologies, she helps organizations unlock value from their data and boost engineering productivity. She is known for bridging technical excellence with real business outcomes, empowering organizations to unlock value from their data through innovative frameworks, optimization strategies, and scalable design patterns.
