Cracking the Code: Unlocking Data Lineage in Your Data Factory
The Lineage Challenge: A Factory Running Without Blueprints
In many organizations, code — whether SAS, SQL, Python, or otherwise — powers the data factory. It ingests raw materials (input tables), transforms them through a series of operations, and produces finished goods (reports, exports, dashboards). The factory works. The machines run. But over time, as delivery pressures mount, large volumes of code are deployed with limited documentation. The result? A production line that’s efficient but opaque.
You know the outputs are accurate. You trust the process. But when someone asks, “Where did this data come from?” or “What inputs were used to make this report?” — the answer often requires manual inspection, tribal knowledge, or reverse engineering. It’s like trying to trace a mahogany table back to its source, only to find the logs say “wood” and nothing more.
Why Lineage Matters: From Inputs to Impact
Understanding data lineage isn’t just about compliance — it’s about operational clarity. You need to know:
- What stock (input tables) entered the factory
- What machines (code logic) processed it
- What products (output tables) were created
- And which combinations yield the best results
If someone requests a high-quality mahogany table, you can’t substitute pine and hope it passes inspection. The quality of your outputs depends entirely on the integrity of your inputs and the precision of your transformations.
Whether you're debugging a report, onboarding a new analyst, or integrating with a governance platform, lineage gives you the visibility to operate with confidence.
The Solution: Automating the Factory Floor
To bring transparency to your code-driven workflows, you need a systematic approach — one that turns your data factory into a well-documented, auditable operation. Here's how:
- Inventory Scanning (Parsing Inputs and Outputs)
Start by scanning the code for key operations — whether it's DATA, SET, MERGE, PROC SQL, or equivalent constructs in other languages. These are your loading docks and shipping bays. Identifying them helps map the flow of materials through your factory.
- Resolving Dynamic Machinery (Macro Variables, Functions, Parameters)
Dynamic code — macros, functions, parameterized logic — acts like reconfigurable machinery. To understand the true flow, you need to “unroll” these components and reveal the actual paths your data takes. Due to the power of macro variables to make code reusable they are very common. Don’t worry there are way to get to the true value and using markers in the code and local input tables we can get them in the clear.
- Creating a Factory Blueprint (Structured Schema)
Once the lineage is extracted, structure it into a standardized format — such as a JSON schema. This blueprint should include:
- Source tables (raw stock)
- Target tables (finished goods)
- Column-level transformations (machine operations)
- Associated code snippets (assembly instructions)
- Integrating with Lineage Tools (Factory Dashboard)
Finally, integrate this schema with enterprise lineage tools. This transforms your codebase from a black box into a transparent, interactive dashboard — accessible to developers, analysts, auditors, and business stakeholders alike.
Conclusion: From Black Box to Blueprint
The challenge of data lineage isn’t unique to SAS — it exists across all code-driven environments. Whether you're in finance, healthcare, retail, or manufacturing, the need to trace data from source to output is universal.
In regulated industries like banking, standards such as BCBS 239 make lineage a compliance requirement. But even outside those frameworks, the benefits are clear:
- Operational Efficiency: Know what stock you have and how best to use it.
- Data Trust: Build confidence in your outputs by understanding your inputs.
- Scalability: Empower teams to troubleshoot, audit, and evolve workflows without guesswork.
Ultimately, unlocking lineage is about turning your data factory into a transparent, reliable, and future-ready operation — one where every product has a traceable origin, and every transformation is accounted for.
As professionals in the data space let’s not send a pine table when mahogany has been ordered!
If you want to have a look at the tool in action you can get it in SAS Help Center: Code Dependencies and we can look at tailoring the solution to your business requirements as this approach is more of a "white glove personalization". If you want to learn more about making your data factory work better read the next in the series for managing your inventory.
