Better Insight Starts with Optimized Data from Babel Street

You probably already have a lot of data. Internal data, third-party data, publicly available information. In hopes of improving insight, you may consider buying even more.

That would be a mistake.

Data volume alone does not create clarity. Piling more information onto already-fragmented sources can obscure the signals that matter and slow risk investigations.

To navigate the data deluge, organizations need an intelligence foundation that combines trustworthy open-source information with differentiated content, then qualifies, enriches, and connects it. That foundation can help teams resolve entities, understand emerging situations, determine proximity to risk, and uncover relationships among people, businesses, locations, assets, and events.

As organizations adopt AI, they also need intelligence that applications, models, and agents can use responsibly. Structured and unstructured text, images, video, and audio must be accessible, contextualized, and governed, with provenance and source continuity preserved so outputs remain explainable and defensible.

For public- and private-sector organizations alike, the goal is data that reduces blind spots and helps practitioners identify emerging risk, fraud, vendor exposure, and other threats.

Yet much of today’s data environment makes that goal difficult to achieve.

Current data environment challenges

Many datasets remain inaccurate, incomplete, inconsistently structured, or difficult to access. These weaknesses hinder aggregation, obscure relationships, and make it harder to build a reliable view of risk.

Five recurring challenges illustrate the problem:

1. Data inaccuracy

Publicly and commercially available information (PAI/CAI) is often full of factually inaccurate information. The digital arena amplifies these inaccuracies. Investigations will inevitably falter when based on data sets that cannot separate what’s correct from what isn’t.

More subtle than errors of fact are the “not exactly true” data points that plague digital information. Months and days are transposed in birth records. Numbers are transposed in tax records, birth dates, and addresses. Information may also be outdated. Firmographic data may list John Smith as CFO of ABC Co. Ltd. And he was, up until nine months ago. He then quit to form his own business. And as for that company? Public information may list him as the owner, when he actually co-owns the business with Mary Johnson.

2. Incomplete information

The orange sedan used in a Utica liquor store robbery — New York State license plate number XYZ-1234, large dent on the rear left bumper — may seem like a good match for the tangerine sedan, large dent, used to rob a convenience store in Poughkeepsie. But with no plate number, the investigator can’t tell for sure. Not all data sets contain all pertinent information, a situation that slows investigations.

3. Differing data structures

Dates of birth, addresses, telephone numbers, vehicle identifiers, and other data points may be structured differently in each data source. Formats vary widely among databases, spreadsheets, PDFs, Word documents, scanned documents, proprietary applications, legacy systems, records management systems, cloud-based databases, and more.

These varying data structures and formats can hinder investigations. Imagine a police investigator searching the criminal history of Joseph Q. Jones. There are a lot of Joseph Q. Jones out there. The investigator is trying to find “his” Joseph Q. Jones in part by birthdate. One record listing the suspect’s birthday as April 15, 1981 is difficult to reconcile with others reading 4/15/1981, 15/4/81, 15 April 1981 — or even a report describing suspect Joseph Q. Jones as “roughly 45 years old.”

4. Aggregation failures

In part because of inaccuracies and inconsistent data structures, aggregation efforts can fail. Consider the field of identity intelligence. John Smith, the business owner, may appear in different databases as John Smith, John Jacob Smith, John J. Smith, J.J. Smith, and Jack Smith. But the investigator has no way of knowing that all these versions of John Smith are the same person until this entity is resolved. Entity resolution technologies can do much to coalesce these identities. Better still is to work from data streams that have already verified and collated this information before it enters corporate or governmental systems.

5. Lack of access

Certain governments limit data access by foreigners. The foreign-access information presented on websites hosted by a government inimical to the United States may vary dramatically from the information that government presents to its own citizens. More benignly, national and international law may prohibit the transfer of certain types of records (personally identifiable information and financial records, as two examples) from one nation to another.

Private companies — including telecommunications, financial, social media, shipping, and data-brokerage providers — also cover geographies unevenly. The result is inconsistent visibility across countries and regions. Organizations therefore need collection strategies that combine global and multilingual breadth with regional access, source qualification, and an understanding of local information environments.

Historical depth is another concern. Developing information can show what is changing now, while historical context helps analysts understand patterns, relationships, and how a situation evolved. Effective investigations require both perspectives—and a clear connection between them.

When organizations rely on volume alone, they are left with inaccurate or incomplete data points, incompatible structures, unresolved entities, and disconnected sources. Those weaknesses undermine intelligence about people, organizations, locations, events, threats, and their relationships — and they also limit the relevance, explainability, and defensibility of AI-supported analysis. The answer is not simply more data, but better-connected intelligence.

From data deluge to better risk intelligence and decision advantage

You already have abundant information. The advantage comes not from adding more data, but from finding the content that matters, qualifying it, connecting it, and making it usable for risk decisions.

The Babel Street Data Advantage™ is an intelligence foundation built for adversarial risk. It transforms differentiated global content into connected, evidence-grounded intelligence by qualifying sources, resolving fragmented entities, preserving provenance, and linking people, businesses, locations, vessels, events, digital identities, and threat indicators. This relationship-rich context helps organizations understand not only what is risky, but what is close to risk — directly, geographically, behaviorally, operationally, temporally, or through one or more connections.

Babel Street creates Data Advantage through a combination of differentiated access, source qualification, entity resolution, relationship intelligence, and risk-focused tradecraft:

Differentiated global content for risk intelligence

Babel Street identifies, accesses, and collects risk-relevant information that standard sources and generic collection may miss. This includes multilingual, regional, unlisted, deep-web, procurement, and adversarial-network content that can bring wider geographic and linguistic context to developing events, identities, organizations, and networks.

Regional data access and intelligence collection

Collection technology, secure infrastructure, regional access, and specialized tradecraft help reach online information environments that may be difficult or unsafe to access directly. This capability broadens the context available to analysts while supporting responsible collection and preserving the source continuity needed for defensible findings.

Risk-focused data collection for better intelligence

When standard collections do not answer a mission-specific question, Babel Street specialists can help develop focused collection strategies for the customer’s use case. The objective is not access for its own sake, but relevant content that can be qualified, contextualized, and applied to monitoring, screening, investigation, analysis, and reporting.

Entity resolution and relationship intelligence

Babel Street extracts and resolves entities from fragmented structured and unstructured information, then connects them with geographic, historical, behavioral, and organizational context. By linking identities, organizations, locations, events, digital assets, evidence, and indicators—and preserving provenance—the intelligence foundation can reveal direct and multi-hop proximity to risk that may not be visible in any single record.

The value of Babel Street data and risk intelligence technology

The Babel Street Data Advantage provides the differentiated, qualified, and connected intelligence foundation beneath the Babel Street Risk Intelligence Platform. Across the platform, that foundation carries source, entity, relationship, geographic, and historical context into monitoring, identity resolution, screening, investigation, analysis, and reporting. Instead of forcing teams to assemble alerts, records, maps, and relationships across disconnected tools, the platform helps them move from signal to understanding in a more coherent workflow.

That same intelligence foundation makes Babel Street applications, APIs, analytics, and agentic workflows more relevant, explainable, and useful. In Insights Investigator, for example, AI can work from qualified, citable, risk-focused knowledge while analysts remain in control of investigative direction and conclusions. More data alone does not create decision advantage. Babel Street does by transforming hard-to-reach global content into connected risk intelligence that helps organizations see hidden exposure, understand proximity to risk, and act with greater confidence.

Disclaimer

All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.