What to consider when evaluating a mobility dataset
Key takeaways
- Mobility data is defined by multiple factors, including scale, signal frequency, journey completeness, geographic coverage, accuracy, privacy, and usability.
- The best dataset depends on the decision you need to make, whether that’s assessing risk for auto insurance, planning retail operations, determining road safety projects, and more.
- For the most actionable insight, look for data that captures customer journeys rather than isolated location points; journeys can reveal how, when, and where people move.
- Data selection is as much about trust and transparency as it is about coverage and accuracy. Privacy, consent, and governance should be part of every evaluation process.
Introduction
Modern organizations have access to more movement data than ever before. Auto insurers use it to assess risk and inform pricing. Retailers use foot traffic data to understand customer behavior. Analysts for retail or financial services use it to identify emerging trends, evaluate markets, and measure real-world outcomes.
But not all mobility data is created equal.
Two providers may both offer data about how people move yet produce very different results depending on:
- How data is collected
- How frequently it is captured
- Whether it can accurately reflect real-world behavior
Before selecting a provider, organizations should understand the factors that determine a mobility dataset and how those factors align with their business objectives.
How do you assess mobility data?
There is no single metric that determines the best mobility dataset for you. Instead, the right dataset for you will fulfill multiple criteria to create a reliable picture of how people move through the physical world.
Some of the most important dimensions include:
- Scale and coverage
- Frequency
- Journey completeness
- Behavioral context
- Geographic representation
- Privacy and consent practices
- Ease of integration and use
- Accuracy
A dataset may perform well in one area but poorly in another. The most suitable mobility datasets provide a balanced combination of these attributes to support confident decision-making and business outcomes such as improved auto insurance pricing, retail operations, fleet safety, and overall road safety.
Start with the decision you’re trying to make
The first step in evaluating any dataset is defining the business question you need to answer:
- Auto insurance: How can I improve risk assessment and reduce adverse selection?
- Auto insurance performance marketing: How can I target drivers with our ideal risk profile?
- Retail operations: How can I drive efficiency based on recent shifts in consumer movement and demand?
- Public sector: How can I prioritize road infrastructure projects in order to have the highest impact on road safety and traffic congestion?
Each use case requires different data characteristics. Rather than seeking one dataset to rule them all, ask which dataset is best suited to the decision you are making.
Evaluate scale and coverage
Scale is one of the foundational components of mobility data assessment.
When evaluating a mobility data provider, consider how much data contributes to the insights being generated and whether coverage aligns with your target markets. Questions to ask include:
- How many trips, devices, or observations contribute to the dataset?
- How broad is geographic coverage?
- Is the data consistent across regions?
- Can the provider support analysis at the level of detail you require?
Adequate scale helps improve statistical confidence and allows organizations to uncover meaningful patterns that smaller datasets may miss. At Arity, we’ve collected nearly three trillion miles of driving data since inception as of July 2026, and we analyze nearly two billion miles daily from tens of millions of drivers to make sense of how people move.
Assess signal frequency and continuity
Scale alone isn’t enough. Large datasets must also be representative and reliably collected.
Many location datasets rely on intermittent pings that provide occasional snapshots of activity. Mobility data can provide a more complete understanding of movement when signals are captured frequently enough to reconstruct trips and identify patterns over time.
Questions to ask include:
- How frequently are signals collected?
- Can short trips be detected?
- How much signal loss occurs during a journey?
- Can complete trips be reconstructed?
Frequent, continuous observations can help organizations understand movement patterns, commuting behavior, customer journeys, and shifting trends with greater confidence.
At Arity, the frequency of our data collection is high; we collect data approximately every 15 seconds from drivers who chose to share their data. This near-continuous connection facilitates capturing customer journeys and the routes within them.
Determine whether the data captures customer journeys
One of the most important distinctions between basic location data and richer mobility data is journey visibility.
Location data may reveal where a device appeared at a specific moment. Mobility data can connect individual observations into a meaningful sequence of movement. This additional context helps organizations understand how locations relate to one another and how visits fit into broader behavioral patterns.
When assessing journey quality, ask:
- Can origins and destinations be identified?
- Are routes visible?
- Can multi-stop trips be reconstructed?
- Does the dataset reveal recurring routines?
Understanding movement between locations often provides more actionable insight than understanding visits alone. At Arity, we collect customer journeys as well as distinct routes within those journeys – insights that are designed to illuminate driving behavior risk, consumer movement and demand, and opportunities to support road safety efforts.
Look for behavioral context, not just locations
Knowing where people are is valuable. Understanding how they move and what patterns influence their decisions is often more valuable.
Behavioral context can help organizations such as retailers distinguish between intentional visits and pass-by activity, identify recurring movement patterns, and better understand how people engage with places over time.
Consider whether the dataset can help answer questions such as:
- Are visitors repeat customers or occasional visitors?
- Do people follow consistent routes and routines?
- Can movement patterns help anticipate trends in behavior?
- Does the data provide context around real-world consumer behavior?
The most useful datasets help explain behavior, not simply document it. At Arity, our mobility data products and services are designed to capture driving behavior data in context, revealing routes and routines. This intelligence can help organizations support risk assessment and pricing, operations, and safety.
Validate geographic thoroughness and accuracy
Coverage alone does not mean that a mobility dataset is the right fit for your company. A dataset should adequately represent the populations, points of interest, and behaviors relevant to your use case. Gaps in coverage or representation can introduce bias and lead to unreliable conclusions.
Organizations should also understand how providers validate and maintain data quality.
Ask questions such as:
- How is accuracy measured?
- How are data anomalies identified and corrected?
- Are quality assurance processes documented?
- How often is data reviewed and refreshed?
Transparency around methodology is often a strong indicator of dataset maturity and reliability. Read more about how Arity refines its algorithms and identifies anomalies.
Evaluate privacy, consent, and data governance
Collecting and using data responsibly and transparently will create better experiences for customers and businesses. Organizations should understand providers’ privacy practices to clarify how compliance is maintained.
Visit our Privacy Center to find out how Arity gathers consent, what data we collect, how we collect data, what we use it for, and our approach to data privacy.
Choose data that supports high impact decisions
Ultimately, the purpose of mobility data is to improve decision-making to drive better results.
The most effective datasets provide a combination of scale, frequency, journey visibility, behavioral context, coverage, accuracy, and transparency. Together, these qualities help organizations move beyond isolated observations and develop a more complete understanding of how people interact with the world around them.
When evaluating providers, focus on whether the dataset can reliably support the decisions your organization needs to make. Mobility data should provide insight, reduce uncertainty, align with privacy requirements, and ultimately provide a clearer view of real-world behavior to enable stronger outcomes.