What market filters are available in Pave's Market Data product?

As of January 2025, Pave's Market Data products provide customers with the following filters to fine-tune compensation benchmarks:

We are actively working on additional filtering options and plan to launch improved functionality throughout 2025.

Does Pave's Market Data product include industry-based market filters?

Does Pave's Market Data product include peer group reporting capabilities?

In the meantime, customers can use existing market filters to define relevant cohorts of companies.

Pave Job Architecture

What are the core elements of Pave's job architecture system?

Pave currently organizes employees into job levels and job families based on job-related data (e.g., job title, functional area, reporting lines, span of control, tenure, etc.) collected by Pave's real-time connections to HR platforms as follows:

• Career Track – Based on job-related data, is the employee in Pave's Executive, Management or Professional Individual Contributor career track?
• Job Level – Based on job-related data, how senior is the employee, how experienced is the employee, and what is the employee's scope of responsibility within their assigned career track?
• Job Family – Based on job-related data, what business department or function, and specific job role should the employee be assigned to?

For additional information on how Pave places employees into job families, see the Pave Job Matching section of this guide for more details.

What career tracks and job levels are in Pave's job architecture system?

Pave's career tracks and job levels define a hierarchy of employees spanning professional individual contributor, management, and executive roles. Starting at the senior-most level of an organization, Pave's career tracks and job levels are as follows:

What executive jobs can I benchmark using Pave?

As of January 2025, Pave's Market Data products deliver compensation benchmarks for the following executive jobs:

Additional executive jobs are added on a regular basis as data sufficiency standards are met. Please note, benchmarks for some executive jobs will not be available in all locations or market cuts due to data sufficiency standards.

Pave Job Matching

How does job matching work at Pave?

Pave uses a machine learning algorithm to streamline and accelerate the job matching process for customers. Our algorithm is regularly reviewed by internal and external compensation professionals to improve results over time.

To create strong training data for our job matching algorithm, we periodically ask select customers to manually match some of their employees to Pave's job architecture system. Patterns identified in this training data are then used by our job matching algorithm to assign job matches across our full database.

Our job matching algorithm uses many of the same signals that compensation professionals and consultants use for manual job matching, including:

• Job title
• Job function
• Reporting line
• Span of control
• Employee location
• Compensation history
• Company industry

Additionally, when customers provide us with their job matches to external survey providers, we can use this information to further enhance the accuracy of job matches.

Data Collection and Management Practices

Is connecting my HR platforms to Pave required to access Market Data?

Yes. Access to Pave's Market Data products is contingent upon customers agreeing to connect their HR platforms to Pave, including HRIS, ATS, and EMS systems.

What HRIS, ATS, and EMS systems connect to Pave?

Pave currently supports connections to the following HRIS, ATS, and EMS systems:

We regularly build connections to new HRIS, ATS, and EMS systems, so if you use a tool not listed above, Pave can still work for you. We encourage you to contact our team to learn more about your options.

What information does Pave collect on customer employees?

Pave's Market Data products deliver compensation benchmarks to customers using data collected directly from real-time connections to HR platforms. Data collected on customer employees and candidates will vary based on customer needs and controls, but generally includes:

• Employee or candidate name
• Employee or candidate email address
• Location
• Job title
• Job level
• Manager
• Base salary
• Data related to variable (or bonus) compensation
• Data related to equity grants and holdings

Some of this information is used to power Pave's machine learning-based job matching algorithm. For customers who only use our Market Data product, and depending on the HRIS and EMS system you use, we may have options that allow you to connect to Pave without sharing employee names or email addresses.

See the Data Privacy and Security section of this guide to learn more about how Pave protects customer data.

What information does Pave collect on customer organizations?

For Pave's Market Data products, we ask customers to provide the following business information:

Company demographic information is used to power filtering options in Pave's Market Data products. Some of the information above cannot be collected directly from HR platforms, so in these cases, account administrators will be asked to input and update information every six months.

See the Data Privacy and Security section of this guide to learn more about how Pave protects customer data.

Data Consistency Labels, Data Sufficiency and Safe Harbor

Why does Pave provide data consistency labels?

Pave's Market Data products include data consistency labels next to all compensation benchmarks. We provide this information because the way data is distributed within a compensation dataset has a significant impact on the reliability of compensation benchmarks. To help companies make well-informed compensation decisions, a benchmarking dataset must paint a complete picture of both sample size and data distribution patterns.

While most traditional compensation surveys only share sample size information, Pave's data consistency labels allow customers to assess both the size and statistical quality of a reported compensation benchmark.

How should customers use Pave's data consistency labels?

Data consistency labels should be used to guide decisions on when and how to utilize compensation benchmarks. A compensation benchmark labeled as "Very High Consistency" can be used at face value with a higher degree of confidence. This is because benchmarks with higher consistency levels indicate there is less variability in pay practices.

A compensation benchmark labeled as "Low Consistency" can still be used, but with an understanding that there is more variability in how the market compensates for this role. This means customers likely have more flexibility to pay slightly above or below the reported compensation benchmark depending on their specific compensation philosophy and existing pay ranges. However, it is important to note that data consistency labels are not intended to serve as a replacement for company-specific range spreads in compensation bands.

How are Pave's data consistency labels different from data confidence labels?

Pave's updated data consistency labels are very similar in concept and practice to the "data confidence labels" displayed in past versions of our Market Data products. However, when we launched updated data consistency labels, we expanded support to all compensation types (e.g., equity) and improved our methodology to provide more context to customers.

Why does my search have a large sample size but low consistency?

This may seem counterintuitive at first glance, but this is exactly why Pave introduced data consistency labels. Our goal is to help compensation professionals think differently about the data they use to make better decisions. In general, as sample sizes increase, data consistency decreases.

For example, if you benchmark compensation for a job using data across the entire United States, this will greatly increase your sample size (generally a good thing); however, there will be a lot of variation in your dataset because it is drawn from such a wide pool of employees (generally a bad thing). As we all know, pay varies widely by city, industry, company size, and company stage of development, etc.

In order to reduce variation in a dataset, compensation professionals typically apply market filters to hone in on more relevant information. However, as you add filters, your sample size will go down.

This is why we show both sample size and data consistency information; it helps compensation professionals fully understand the statistical quality of a reported compensation benchmark and the potential impact of selecting wider vs. narrower datasets.

Why does equity data tend to have lower consistency labels?

Generally speaking, there is a much higher degree of variation in how companies pay employees with equity compensation vs. cash compensation. Equity award sizes also vary widely across locations, job families, job levels, and company stages. Thus, our reported equity compensation benchmarks tend to have lower consistency labels.

Indeed, when comparing cash and equity compensation benchmarks, our data science team finds that it typically takes 10 times more equity data points than base salary data points to produce benchmarks with the same level of consistency.

How do data consistency labels apply to Pave's Calculated Benchmarks feature?

Pave's data consistency labels are applied to all compensation benchmarks in our Market Data products, including compensation benchmarks generated using raw data and our Calculated Benchmarks feature. In both cases, we take various factors, including sample size and data distribution, into account to give customers a view into how well a dataset represents market practices via a margin of error.

Why does a Calculated Benchmarks output have low consistency? Does this mean Pave isn't confident in its calculation?

Data consistency labels are intended to give customers context into how much variation there is in a dataset underlying a compensation benchmark. When there is a high degree of variation in how the market compensates employees for a given role, the consistency level of compensation benchmarks will be lower in order to provide customers with this context. This is true regardless of whether a compensation benchmark is calculated using raw data or other means.

In the case of Calculated Benchmarks outputs labeled as "Low Consistency," Pave is A) providing you with a compensation benchmark where one would otherwise not exist, and B) being transparent that there is a higher level of variation in how the market typically compensates employees for this role.

What are Pave's data sufficiency standards?

Pave's Market Data products require data from a minimum of three companies to generate a compensation benchmark. However, in cases where company and incumbent counts are very low, algorithms in our product often override the display of data. In some cases, our data consistency rules will block the display of data altogether, and in others, our Calculated Benchmarks feature will model results using more robust datasets.

What is Pave's approach to Safe Harbor?

For decades, guidance from the U.S. Department of Justice (DOJ) and U.S. Federal Trade Commission (FTC) trained HR professionals that aggregated compensation data must be at least three months old before being shared, among other considerations. However, in February 2023, the DOJ withdrew its 1996 Healthcare Safe Harbor statement, and in January 2025, the DOJ and FTC went further by withdrawing the 2016 Antitrust Guidance for Human Resource Professionals.

As a result, long-held beliefs about what is required to meet safe harbor guidelines are changing. Additionally, nothing about a real-time market data approach conflicts with the core intent of DOJ and FTC guidance, which is designed to prevent an agreement among competing employers to limit competition or the competitive process.

Pave's model, which aggregates and de-identifies data from thousands of companies, and adheres to all expected data sufficiency standards, meets this litmus test.

Data Privacy and Security

How does Pave protect privacy?

Pave aggregates and de-identifies compensation data from our customers, meaning the information we display in our Market Data products cannot be linked back to any specific customer or employee. In addition, we do not sell employee data. These protections are contractually guaranteed in Pave's agreements, including our:

• Master Subscription Agreement (MSA)
• Data Processing Addendum (DPA)

Is Pave CCPA and GDPR compliant?

Pave adheres to the California Consumer Privacy Act (CCPA) and the European Union's General Data Protection Regulation (GDPR), ensuring that personal data is processed lawfully, transparently, and securely. We also work closely with customers in meeting their compliance requirements under these regulations.

Is Pave SOC 1 Type 2 compliant?

Yes. This attestation verifies that Pave has effective controls in place for financial reporting, ensuring the accuracy and reliability of financial data processed through its platform.

Is Pave SOC 2 Type 2 compliant?

Yes. This attestation confirms that Pave's systems are designed to keep customer data secure, available, and confidential over time, reflecting the company's commitment to ongoing operational excellence.

Is Pave ISO/IEC 27001:2022 certified?

Yes. Pave has achieved ISO/IEC 27001:2022 certification, demonstrating our adherence to international standards for information security management systems. This certification underscores Pave's dedication to systematically managing sensitive information and ensuring data integrity. To view Pave's certificate, click here.

Does Pave have a bug bounty program?

Yes. Pave operates a private bug bounty program.

How often does Pave undergo penetration testing?

The Pave application undergoes biannual penetration testing.

Does Pave use encryption to protect customer data?

Yes. Data is encrypted at rest and in transit. Pave data is encrypted in transit with TLS 1.2 and at rest with AES 256-bit encryption.

How does Pave manage encryption keys?

Pave relies on Google Key Management Service.

Does Pave collect PII from customers?

Yes. The collection of some Personally Identifiable Information (PII) is required for Pave to deliver services to customers. The amount and nature of PII collected by Pave will vary based on the products a customer uses. Typically, our Market Data products consume less PII than our compensation management tools. Customers can work directly with our team to control and adjust data flows as needed.

Who has access to customer data at Pave?

Pave enforces the principle of "Least Privilege," ensuring that employees have access only to the data necessary for their roles. This approach minimizes the risk of unauthorized data exposure and maintains strict confidentiality.

Does Pave have a security training program for employees?

Yes. Pave has a security training program that covers topics ranging from general security awareness for all employees, to more specialized training in secure design principles and other advanced topics for software engineers.

Where does Pave store customer data?

Pave stores data in the United States, utilizing enterprise-grade cloud storage solutions provided by Google Data Centers. We follow data storage best practices that comply with relevant regulations and industry standards.

What else does Pave do to protect customer data?

Pave's Market Data products only display aggregated and de-identified data, ensuring that individual identifying information is not utilized. This practice protects individual privacy and maintains data confidentiality.