The Real Price of Data Debt.

Data debt is the accumulation of outdated architectures, siloed platforms, and fragile pipelines that make every new data change harder and more expensive than it should be.

It is the data side of technical debt: legacy ETL, legacy warehouses, undocumented code and jobs, difficult integrations, shadow BI, and manual data quality fixes, and expensive data platforms that don’t really deliver on your strategy.

When that debt lives in the data stack, it directly constrains analytics, AI, and every transformation initiative that depends on trusted data for the organization.

The most visible impact of data debt is not technical; it is a business strategic impact:

– Time‑to‑market drag: Every new metric, dashboard, or AI use case takes longer because it has to navigate old and legacy data pipelines and conflicting definitions.

– Innovation ceiling: organizations say technical debt significantly impacts their ability to innovate. Abandoned AI pilots, delayed self‑service analytics, and a growing gap between business ambition and platform reality

– Firefighting: Many data engineers already spend most of their week firefighting failing jobs instead of designing better solutions for the business.

Then, apart from this strategic impact, we have the risk and compliance cost:

– Older systems are harder and more expensive to secure. Period.

– Data Silos are expensive; It is harder to prove data lineage, apply retention policies, GDPR practices or consistently enforce privacy and access controls.

– Fragile data platforms don’t just trigger overtime for engineers; they can halt reporting to regulators, disrupt revenue processes, and erode customer trust.

The question for leaders is no longer “Can we afford to modernize?” but “How much longer can we afford to fund a platform that consumes 30–40% of our capacity just to be there wasting our resources?”

That is why we built LACE. To help leaders to answer these questions. LACE makes data platform modernization financially realistic by shrinking the time, risk, and people cost that usually kill the business case.

Modernizing data platforms typically delivers reductions in hardware, software, and staffing costs over time, with total cost of ownership improvements of 20–40% documented across multiple studies.

The problem is the upfront spend: months of discovery, manual documentation that is outdated, impact analysis on 10 years code, and rewrite effort before value is visible. LACE attacks that “front‑loaded” cost so organizations can reach those savings faster and with less capital at risk. By automating understanding of legacy platforms and industrializing migration work, LACE turns modernization from a huge one‑off project into a sequence of smaller, affordable steps that pay for themselves as you go.

LACE acts in four blocks: Understand legacy code, design data models, business logic, and reference data extraction, and generate

LACE’s platform‑specific agents can document and analyze your entire legacy codebase in less than 1 hour, extracting:

Complete job and procedure lineage: what feeds what, what breaks if you move something.

Embedded business logic: calculations, validation rules, and reference data transformations.

Data dependencies: which tables are critical, which are shadows, which are abandoned.

LACE can also design medallion architecture data models with 98% accuracy, saving 90% of the time a data modeler would spend:

as it automatically ingests your legacy schema and business logic to infer dimensional relationships and proposes bronze → silver → gold (medallion) layer design patterns tailored to your source systems.

Also, business logic is scattered across ABAP, PL/SQL, Python, and stored procedures. It is embedded in ancient SAP BW transformations, SAS Data Step programs, or PL/SQL packages. Extracting it manually is painstaking and error‑prone—and if you miss a rule, your new platform delivers wrong numbers, and nobody trusts it. LACE can scan legacy code and automatically identify calculations, aggregations, validations, and conditional logic.

Once you understand what to migrate, actually building new pipelines is still largely manual. Your engineers write Spark jobs, transformations, or SQL scripts line by line, testing incrementally, and hoping they got the logic right. LACE can generate 80% of new data transformation code, dramatically accelerating pipeline delivery as it translates extracted business logic into production‑ready Spark.

Your engineers now focus on reviewing LACE outputs, testing, and optimization instead of doing the “heavy lifting”.