By Sally Schulte, Senior Product Marketing Director
When you go out for a meal, you assume that someone, somewhere, could tell you how the ingredients got from the farm to your plate—and prove to you that the food is safe to consume. And strict regulations like the FDA’s Food Traceability Final Rule and the EU’s Regulation 178/2002, coupled with sophisticated tracking systems, mean your assumption is probably correct. From the farm or ranch to the store shelf or restaurant, every step is logged, verified, and auditable.
But what about your company’s data? Like the food you consume, your company’s products and services exist in a heavily regulated market, chock full of checks and balances. But can you provide the same level of traceability for the data flowing through and being consumed by your organization that your local grocers can for the food on their shelves?
For most organizations, the honest answer is “no.” In most cases, the food on your plate is far more traceable than your data—and that’s a growing problem, especially as enterprises race to gain efficiency by sharing data across the organization, use data to drive automation, and feed data to train new AI tools.
This blog explains why data provenance has become a critical requirement for regulatory compliance, trustworthy AI, and sound decision-making. It explores how poor data traceability creates risk as organizations scale automation and AI initiatives, and why tracking data origins and usage is foundational to responsible business processes.
The Data Provenance Problem
Roughly 403 million terabytes of data are generated daily (that’s 4.03 x 1020) as part of today’s business processes, with the volume of data stored globally doubling approximately every four years. And there are innumerable data sources: customers, advisors, agents, approvers, third-party contractors, etc. But ask where a specific dataset originated, if it’s been manipulated in any way, or whether it’s up to date, and you’ll likely be met with blank stares.
This lack of “data provenance”—the ability to trace data back to its original source—has serious consequences. Without it, data can’t (or shouldn’t) be trusted. And if data isn’t trustworthy, it shouldn’t be shared, reused, or leveraged to train AI systems that depend almost entirely on quality inputs. (Related reading: “Why Data Quality and Data Literacy Matter—For Everyone in Your Organization.”)
Imagine training an LLM on data that was scraped from unverified sources, or worse, on information that violates data privacy laws or internal policies. The consequences go beyond ethical or compliance issues: The outcomes become technical and strategic nightmares. Models trained on untraceable data risk embedding bias, misinformation, or even legal liability.
Why Data Traceability Matters More Than Ever
Traceability ensures that organizations can answer three critical questions about their data:
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- Where did the data come from? — The original source, owner, and collection method.
- How has it been changed? — The transformations, filters, and enrichments applied along the way.
- Can the data be trusted? — Whether it meets quality, timeliness, and compliance standards.
Without this lineage, enterprises operate blind. Teams waste time debating data definitions. Business decisions rely on outdated or inconsistent numbers. And AI teams spend more time cleaning and verifying data than building models.
In regulated industries, traceability isn’t just good practice, it’s a requirement. From international laws such as GDPR and PIPL to regional regulations like the Sarbanes-Oxley Act in the U.S. and the Privacy Act in New Zealand, governments are holding companies accountable, requiring them to prove data origins and usage. Yet even with tight government requirements, full data lineage remains a challenge.
We’ve Solved Harder Problems, So Why Not Data?
The irony is that, as a global society, we’ve solved far more complex traceability challenges in other industries. Food, pharmaceuticals, and aerospace all manage supply chains that span continents, yet they can track every component from origin to destination.
If a batch of lettuce is contaminated, it can be traced back to the exact farm and field in hours. If a jet engine part is faulty, it can be identified and replaced before it causes harm.
So why can’t we do the same with data?
The answer lies in legacy systems, siloed architectures, and the sheer speed of data creation. But new tools—including metadata management platforms, data catalogs, and AI-driven lineage mapping—are beginning to change that.
In this context, the interoperability of forms automation solution SmartIQ™ lends itself to being a key factor in this improved connectivity, offering a way to trace precisely when and where information entered the organization, from whom and in what context. Companies can determine what steps were taken to validate identity, and they can replay data submissions in the context in which they were originally submitted.
Data Traceability: The Foundation for Responsible Business Decisions and AI Initiatives
As enterprises test the boundaries of workflow automation and explore how to safely integrate AI, trustworthy data will determine who succeeds and who stumbles—because you can’t build responsible business processes with data that can’t be traced to an authenticated, original source.
Just as we demand to know where our food comes from, the institutions protecting our financial and physical investments should demand the same transparency for the data being used to make critical business decisions. If they don’t, our steaks may be more traceable than our spreadsheets, and that should make every business leader a little uncomfortable.
Ready to improve data accuracy and traceability at the source? Explore our “Ultimate Guide to Forms Automation” to learn how you can streamline data collection, enhance customer experience, and ensure data provenance and compliance.
Frequently Asked Questions
1) What is data provenance?
Data provenance is the documented history of data, revealing its origin, movements, usage, and transformations. Data provenance offers a way to determine when information was provided, how it was provided, and by whom. This ultimately validates data’s authenticity and reliability, which separates it from data lineage (more focused on flow). Data provenance is focused on why and how data was originally created.
2) Why is data provenance important?
Tracing data to its origins and understanding how it has been used or applied is essential to meet today’s regulatory requirements, particularly in the financial services, healthcare, and insurance sectors. Data provenance helps ensure data is valid and can be used with confidence to drive business processes and decisions. Data provenance is also a fundamental pillar of most data governance strategies.
3) How does SmartIQ improve data provenance and traceability?
Interoperability—and the ability to automatically transfer data to the systems and people that need it, when they need it—is essential to improving overarching data traceability. By removing manual intervention and automatically passing data through secure connections, SmartIQ lets critical data provenance details follow information through a process, and those details can be stored and recalled for auditing and compliance in the future if necessary. Read the SmartIQ solution brief.
