Solving The Private Company Identifier Problem With OpenData
In an age where data drives decision-making, from investment risk modeling to AI workflows, consistent, reliable entity identifiers are foundational. Yet despite the proliferation of data platforms, uniquely identifying private companies remains one of the most persistent challenges in data engineering.
Many current solutions fall short, relying on proprietary IDs or self-generated keys that fragment the ecosystem and introduce costly mapping challenges. Instead, the industry needs identifiers that are unique, open, machine-readable, and interoperable, making data integration and analytics far more efficient. We believe OpenData.org, and complementary open standards, solve this problem.
Why Current Private Company Identifiers Solutions Are Inadequate
At its core, an identifier is a label that allows systems to recognize the same object across databases and applications. Without a common identifier, enterprises face:
- Data Silos - Data assets that can't be joined, limiting their potential impact
- Mapping Headaches - Inconsistent company references with different proprietary identifiers across vendors
- Manual Reconciliation - Mismapped entities that leads to flawed insights and decisions
In practice, even something as seemingly simple as linking a startup’s funding profile with regulatory filings can require a tangled web of transformations across internal and external join keys. Unique, open identifiers are the connective tissue that make cross-dataset analysis tractable.
A number of identifier approaches exist today, but they all have trade-offs:
- Proprietary IDs: Curated by credit bureaus, these are closed from general usage.
- Internal Join Keys: Many organizations create their own surrogate keys. These are useful internally, but not portable externally.
- Informal Mapping Tables: Many engineering teams end up building bespoke maps between data sources, but they are error-prone and brittle.
Open Data as the Missing Infrastructure
Open data represents information that is accessible, reusable, and redistributable without restriction, exactly the attributes needed for a universal identifier ecosystem. OpenData.org offers a global, open identifier of entity records that can serve as a canonical source of truth. When leveraged as part of a data stack:
- Entities can be uniquely and permanently identified
- Data from disparate sources can be joined reliably
- Machine-readable identifiers scale for AI, analytics, and automation
This mirrors how open standards have succeeded in other domains and reduces the fragmentation caused by proprietary, closed identifiers.
What This Means for Data Teams
Adopting open entity identifiers enables:
- Faster Data Integration - No need for bespoke reconciliation tables or brittle heuristics. Identifiers are consistent across datasets.
- Better Data Quality - Open identifiers bring with them standardized metadata and governance that minimize ambiguity and duplication.
- Scalable Analytics - With shared keys, analytics platforms, machine learning models, and downstream products can operate over a unified entity graph.
- Lower Operational Cost - Less engineering time spent on cleaning, mapping, and maintaining idiosyncratic identifier systems.
Conclusion
Private company identifiers don’t have to be a persistent pain point. By adopting open, unique, machine-readable identifiers provided through platforms like OpenData.org and aligned with existing open standards, organizations can unlock a new level of interoperability, analytical power, and operational efficiency. The future of entity resolution isn’t closed and proprietary, it’s open, shared, and standardized.