Why Missing PLD Is a Compliance Issue, Not a Data Issue

Most analytics and marketing today run on location data. When precise or personal location details go missing, the knee-jerk reaction is to treat it as a data gap to fix. But that’s the wrong starting point.

Missing precise or personal location data (PLD) isn’t primarily a problem of incomplete or poor-quality data. Instead, it’s often a direct result of the system doing its job—enforcing compliance with privacy laws and consent requirements. In regulated environments, gaps in PLD don’t reflect technical failure; they reflect a deliberate design choice shaped by legal and operational realities. Understanding why these gaps exist leads not just to better systems, but to safer, more sustainable business practices. This article explains how missing PLD signals compliance at work—not data that needs “fixing.”

What We Mean by Precise or Personal Location Data (PLD)

Precise or personal location data (PLD) refers to information that ties an individual to a specific place and time. This includes GPS coordinates captured from devices like smartphones, device identifiers linked to exact latitude and longitude, or movement patterns revealing routines such as home and work locations. Because PLD often intersects with person-level data, it can become a strong proxy for identity when combined with timestamps, device IDs, or other signals.

PLD is inherently sensitive. Location data can expose private behaviors, routines, and identities, even when direct identifiers are removed. For instance, knowing someone’s visits to healthcare providers, religious institutions, or political events can reveal intimate aspects of their lives. Companies rely on PLD for multiple purposes: logistics companies use it for routing and delivery verification; marketers create audiences and perform attribution; analysts evaluate foot traffic and plan resources.

However valuable, PLD demands careful treatment. Mishandling or improper disclosure can compromise individual privacy and result in regulatory penalties. As such, PLD is subject to restrictive rules and operational safeguards.

The Legal Landscape Governing PLD

Several regulatory frameworks govern how organizations can collect, use, share, and retain precise location data. These rules impose operational duties rather than purely technical data quality requirements.

• FTC Enforcement: The U.S. Federal Trade Commission (FTC) has taken action against companies for mishandling PLD. In 2022, the FTC sued Kochava for selling location data that tracked visitors to sensitive locations, including reproductive health clinics and places of worship, without adequate consent (FTC v. Kochava). More recently, the FTC finalized an order against InMarket, prohibiting it from selling or sharing precise location data and requiring clear disclosures, explicit consent, and deletion mechanisms (FTC InMarket Order).
• California Privacy Protections: California’s Privacy Protection Agency (CPPA) enforces regulations under the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA) that impose strict rules on PLD collection and use (CPPA Regulations). These include requirements for explicit, informed consent, prohibitions on resale and sharing, data minimization principles, and user rights for withdrawal of consent and deletion.
• UK Privacy Guidance: The UK’s Information Commissioner’s Office (ICO) provides guidance under the Privacy and Electronic Communications Regulations (PECR). It states that collecting location data via electronic communications requires clear consent, transparency about data use, and restrictions on retention (UK ICO PECR Guidance).

Collectively, these legal frameworks require operational controls: explicit, documented consent; strict limits on sharing; minimization of data collected; and timely deletion upon request. Crucially, these compliance obligations shape how systems are designed to handle PLD. Missing data or data suppressed at various points in the workflow is often a direct result of these controls, not a technical failure to collect or process data.

Why Systems Suppress or Avoid PLD: Privacy by Design in Practice

“Privacy by design” is a foundational principle embedded in regulatory approaches. It means building systems that embed privacy protections at every stage, ensuring compliance by design rather than retrofit.

Key elements include:

• Minimal collection: PLD is collected only when necessary and with explicit, informed consent. Avoid gathering data “just in case” it might be useful later.
• Data minimization: Systems default to coarser location granularity, such as postal codes or regions, when precise coordinates aren’t essential.
• Consent gating: Access to precise location data is gated behind clear, user-approved consent mechanisms.
• Access controls and segregation: PLD is routed to strongly protected storage with limited access. This supports purpose limitation and reduces exposure.
• Aggregation and suppression: To prevent re-identification, outputs based on PLD are aggregated, and systems enforce minimum group sizes before data is made available externally.
• No backfilling: Missing PLD is not “filled in” by inference or linking with other datasets when consent is absent or withdrawn.
• Deletion pipelines: Systems track consent states and data lineage, enabling efficient removal or suppression of PLD throughout raw and derived stores.

Concrete examples of these principles in practice include:

• Clean rooms enforcing crowd size thresholds before allowing reports to exit secure environments to avoid exposing small cohorts (Amazon Marketing Cloud Aggregation Threshold).
• Rounding coordinates or expanding time windows to ensure less precise data use, reducing sensitivity while maintaining utility.
• Segmented data stores with restricted user roles ensure that PLD isn’t unnecessarily exposed or mixed with unrelated datasets.

All these controls contribute to intentional gaps or suppressed values in location data downstream. They indicate compliance controls operating correctly, not missing or broken data pipelines.

The Risk of Treating Missing PLD as a Data Problem

Many teams, driven by legitimate analytical or marketing goals, want complete, granular data to power models, audience building, or operational decisions. This desire can lead to dangerous shortcuts when applied to PLD.

Common pressures include:

• Analysts seeking to “fill in” missing data by inferring home or work locations from other signals.
• Marketing groups trying to enrich user profiles with third-party location data without explicit consent.
• Product teams pushing to simplify opt-in flows or default to capturing more data proactively.

Such actions carry substantial compliance risk:

• Inferring or reconstructing PLD without clear, informed consent violates legal requirements and privacy promises.
• Reselling or sharing enhanced location data risks regulators’ disapproval and potential enforcement actions.
• Attempting to “complete” datasets undermines the data minimization principle and can lead to re-identification.

The FTC’s Kochava and InMarket cases show how such practices can quickly lead to heavy penalties. California’s CPPA rules and UK PECR guidance reinforce that consent is mandatory upfront—not a checkbox to bypass later with inference.

This means treating missing PLD as a data completeness problem is a flawed approach. It risks turning compliance challenges into engineering issues, exposing organizations to regulatory and reputational harm.

What Operators and Builders Should Focus On

Designing compliant, scalable systems requires embracing data gaps in PLD as an intentional outcome, not a problem to be fixed.

Recommendations include:

• Make compliance a built-in feature: Clearly document which products and workflows require PLD and under what conditions. Confirm that collection aligns with explicit user consent and transparent purpose.
• Model consent state as primary data: Track consent status carefully, treat absence or withdrawal as definitive “no,” and enforce rules accordingly in data flows.
• Favor aggregated analytics: Use clean rooms and privacy-preserving technologies that enforce aggregation thresholds, minimizing person-level data export.
• Enforce end-to-end minimization: Retain PLD as close to edge devices or routing systems as possible. Use coarser spatial and temporal granularity for reporting and downstream uses.
• Operationalize deletion and suppression: Implement processes to remove or mask PLD promptly upon consent withdrawal, across all related datasets and analytics assets.
• Prohibit backfilling and data inference: Define strict policies disallowing reconstruction of missing PLD from secondary data. Educate teams that inference continues to process personal data in many jurisdictions.
• Measure compliance success: Track metrics like proportion of PLD with explicit consent, frequency of queries blocked due to small cohorts, and deletion turnaround times.
• Cultivate a compliance-first culture: Shift incentives away from “filling gaps” towards rewarding designs that respect minimization and consent. Celebrate “no data” or “no output” paths when appropriate.
• Vet vendors carefully: Insist on documentation showing consent processes, privacy protections, and aggregation thresholds. Avoid tools promising to magically “fill in” PLD.

Robust PLD controls simplify systems and lower operational risk. This approach balances the commercial need for location insights with legal and ethical obligations.

What This Looks Like in Practice

Several patterns illustrate the feasibility of compliance-driven PLD handling:

• Route-level precision for logistics: Precise location remains within routing or delivery apps for short retention periods. Business intelligence sees aggregated metrics such as delivery success rates by zone, not device-level traces.
• Consent-based marketing activation: User audiences are built strictly from individuals who granted explicit PLD permission, with enforced aggregation thresholds before activation or export.
• Privacy-safe attribution and measurement: Attribution engines operate within clean rooms that apply cohort aggregation and prevent person-level PLD from leaking. Amazon Marketing Cloud exemplifies this.
• Purpose-limited data warehouses: PLD is stored in isolated partitions with strong access controls. Query logs alert on attempts to access or export data representing small or unique cohorts.
• Regular deletion drills: Organizations test their deletion and suppression pipelines periodically to ensure withdrawal requests propagate through all data layers.

These patterns build trustworthy systems that respect privacy without sacrificing essential business insights.

A Note on Culture

Compliance issues arise not from single individuals but often from misaligned incentives across teams.

When dashboards penalize “missing” data, teams may be tempted to circumvent controls. Rewarding workarounds that expose small cohorts or reconstruct suppressed data increases risk.

The antidote is cultural reset: treat compliance practices as uptime metrics—successes to celebrate. Encourage “no” and “stop” decisions that prevent data misuse. Make minimization a design goal, not an impediment.

Culture is a system in itself. Building a compliance-first mindset is essential to resilient, sustainable data operations.

What Might Change — and What Probably Won’t

Regulatory expectations around PLD continue to tighten. While the U.S. federal privacy law remains pending, states and federal agencies increasingly enforce strict standards. The EU, UK, and other countries maintain rigorous requirements on location data consent, minimization, and aggregation safety.

At the same time, the commercial value of PLD remains high. Logistics, marketing, and analytics depend on location insights.

Balancing these forces is manageable if systems are designed with compliance as foundational. Improvements in clean rooms, consent management platforms, and threshold enforcement offer better tooling but cannot replace core design choices favoring minimization and privacy.

Conclusion: Compliance-Driven Gaps Signal Healthy Systems

Gaps in PLD are not bugs or failures. They are signals that systems honor consent, apply minimization, enforce aggregation, and respect withdrawal.

Operators must balance data granularity against regulatory demands, building systems where compliance is woven into every step.

When location data goes missing, the right question is why. If consent, minimization, or aggregation are responsible, those gaps represent healthy, lawful systems doing exactly what they should.

This perspective leads to better decision-making, safer businesses, and truly sustainable data practices.

References and Further Reading

• FTC v. Kochava press release
• FTC InMarket final order
• California CPPA regulations (CCPA/CPRA)
• UK ICO PECR location data guidance
• Amazon Marketing Cloud aggregation threshold

Disclaimer: This article is intended for operators and builders. It does not constitute legal advice.