As a MarTech platform grows, managing large amounts of marketing data becomes more challenging. More customers and campaigns lead to more API calls, increased traffic, and greater demands on data pipelines.
For one rapidly growing MarTech company, repeated third-party API failures were creating missing data, delayed reports, and inaccurate dashboard results.
The company needed a dependable way to collect, check, process, and monitor marketing data as its platform expanded.
By leveraging a modern DataOps framework, data engineering services, and enterprise data platforms, the company improved API monitoring, enhanced data quality, automated testing, and scaled its platform to support more than 10,000 business locations.
About the Company
The company provides an enterprise marketing intelligence and automation platform for businesses that operate across multiple locations.
Its platform enables brands to create, manage, and track local marketing campaigns across search engines, social media networks, and other digital channels.
The company uses a software-plus-support business model that combines MarTech technology with professional consulting services.
The Challenge: API Problems Were Affecting Data Quality
When the platform had fewer customers and locations, collecting campaign information through APIs from platforms such as Google Ads, Meta, and Instagram was relatively simple.
As the customer base and number of campaigns grew, the platform had to handle a much larger number of API requests.
This resulted in several issues, including:
- API timeouts
- Rate-limit errors
- Missing records
- Slow data synchronization
- Incomplete API responses
- Incorrect dashboard figures
These problems created two important data quality questions.
1. Was the Data Collected Correctly?
The engineering team needed to confirm that data received from external marketing platforms was:
- Complete
- Delivered on time
- Properly structured
- Free from missing records
- Consistent with the original source
2. Was the Final Data Accurate?
Collecting the data successfully was only the first step. Errors could also occur while the data was being transformed, processed, or displayed in dashboards.
The team needed to make sure that the information customers saw matched the original source data.
As a result, data ingestion monitoring and data observability became important parts of the platform.
Incorrect marketing metrics can influence campaign decisions, advertising spend, and customer trust. For a growing SaaS company, unreliable reporting can therefore become a serious business concern.
The Solution: A Two-Layer DataOps Framework
The engineering team introduced a two-layer DataOps monitoring framework to monitor data quality from the initial API connection all the way to the customer dashboard.
Automated checks were added at two important stages:
Third-Party Marketing APIs
Google Ads, Meta, Instagram
↓
Layer 1: Ingestion Monitoring
API health, payload checks, record counts, and data freshness
↓
Enterprise Data Platform
Data transformation and aggregation
↓
Layer 2: Data Observability
End-to-end testing and validation after transformation
↓
Customer Dashboards
This approach allowed the team to find data problems before incorrect information reached customers.
Layer 1: Data Ingestion Monitoring
The first layer focused on checking the quality of data entering the platform.
The system automatically monitored:
- API availability
- API response times
- HTTP status codes
- Payload completeness
- Record counts
- Data freshness
- Source schema compliance
- Synchronization schedules
These checks helped the team detect API and data issues as soon as information entered the platform.
Instead of allowing incomplete or outdated information to continue through the pipeline, the system could identify and flag problems early.
Layer 2: End-to-End Data Observability
The second layer monitored the data as it moved through the rest of the pipeline.
The process covered three main stages.
Raw Data
The system checked whether the expected information had been successfully received from external APIs.
Transformed Data
The team checked calculations, business rules, aggregations, and transformed records to make sure the processing was correct.
Customer-Facing Data
Automated tests verified that the values displayed in customer dashboards matched the expected results.
This changed the team's approach from reactive problem-solving to proactive data monitoring.
Instead of waiting for customers to report incorrect numbers, engineers could identify problems through automated alerts and validation checks.
Technical Implementation
The implementation concentrated on five major areas: API reliability, data quality, automated testing, scalability, and security.
Phase 1: Improving Data Ingestion
Automated API Testing
The team developed automated API tests using Postman to validate:
- API responses
- Response codes
- Data structures
- Schema compliance
- Payload completeness
These tests helped identify API problems before they affected other parts of the platform.
Real-Time Data Monitoring
The platform added monitoring for record counts and data timestamps.
This made it easier to detect missing records, delayed synchronization, and unexpected changes in incoming data.
CI/CD Integration
Data validation scripts were integrated into Azure DevOps pipelines.
The automated checks could run at scheduled times as well as during the software delivery process.
This reduced the need for repeated manual testing.
Load and Performance Testing
The team created separate testing environments to simulate large numbers of API requests.
- The tests measured:
- API response time
- Number of simultaneous connections
- System latency
- Platform stability
- Performance during traffic increases
These tests helped engineers identify possible performance bottlenecks before they affected production users.
Security Testing
Security measures were applied to API endpoints using HTTPS/TLS encryption.
The platform also used Vulnerability Assessment and Penetration Testing (VAPT) and Dynamic Application Security Testing (DAST), following security practices aligned with OWASP guidelines.
Phase 2: Adding End-to-End Data Observability
The second phase focused on checking the complete flow of data across the platform.
Automated User Workflow Testing
The team created around 200 test cases and automated 90 key user workflows using Cypress.
These workflows included activities such as:
- Using dashboard filters
- Creating reports
- Viewing campaign information
- Exporting reports
- Checking displayed metrics
Post-Transformation Data Validation
A custom data quality solution was created to validate records after transformation and before they appeared on customer dashboards.
This helped the team detect problems introduced during data processing.
Concurrency Testing
Stress tests were conducted with different numbers of users and API requests running at the same time.
The team measured platform response times, API latency, and connection stability under different levels of workload.
Centralized Monitoring
System logs, data ingestion results, uptime information, and error details were brought together in one centralized monitoring environment.
This gave engineers a single location to investigate both system and data-related problems.
Standardized DataOps Practices
Testing procedures and pipeline workflows were documented and standardized through a DevOps Center of Excellence (CoE).
This created a repeatable framework that could also be used for new integrations, product features, and future platform improvements.
Business Results
The DataOps approach helped the MarTech platform improve reliability while continuing to expand.
Faster Detection of Data Issues
The team could identify API failures and incomplete data before they appeared in customer-facing dashboards.
More Reliable Marketing Information
Automated validation helped keep dashboard metrics aligned with the original source data.
Support for More Than 10,000 Locations
The platform successfully expanded to support more than 10,000 business locations while maintaining data quality.
A Reusable DataOps Framework
The company created a repeatable process for monitoring APIs, checking data quality, testing workflows, and supporting future integrations
Key Takeaway
Scaling a MarTech platform requires more than simply handling a higher number of API requests. Companies also need effective processes for data quality, API monitoring, observability, automation, and security.
A two-layer DataOps framework can help organizations detect problems earlier, verify data throughout the pipeline, and maintain accurate reporting as their customer base grows.
For SaaS and MarTech companies managing large numbers of API integrations, combining data engineering, automated testing, and data observability can create a stronger and more scalable foundation for growth.
Frequently Asked Questions
What does DataOps mean in MarTech?
DataOps in MarTech is an organized way to manage, monitor, and maintain marketing data throughout its lifecycle. It helps ensure that data collected from platforms such as Google Ads and Meta is complete, accurate, timely, and dependable.
How can APIs used in marketing data pipelines be monitored?
API monitoring can use automated tests to check response times, error codes, payload completeness, data freshness, and schema compliance. These tests can also be connected to CI/CD platforms such as Azure DevOps.
What is the difference between data ingestion monitoring and data observability?
Data ingestion monitoring focuses on checking whether data enters the platform correctly.
Data observability looks at the health of data across the entire process, including ingestion, transformation, processing, and presentation.
In simple terms, ingestion monitoring checks the entry point, while observability provides a wider view of data quality across the platform.
How can a SaaS platform handle a growing number of API integrations?
A SaaS platform can improve API scalability by using API monitoring, automated testing, load testing, data freshness checks, concurrency testing, and security validation.
These practices help teams find performance issues and bottlenecks before they affect production systems.
Which tools can support DataOps and data quality testing?
Different tools can be used for different parts of a DataOps process. For example:
- Postman can be used to test APIs.
- Cypress can automate end-to-end user workflows.
- Azure DevOps can support CI/CD pipelines and scheduled tests.
- Centralized logging tools can help monitor errors, system health, and pipeline activity.
Why do marketing dashboards sometimes display incorrect data?
Incorrect dashboard information can come from several sources, such as failed API requests, incomplete data, delayed synchronization, transformation problems, calculation errors, or issues with how information is displayed on the front end.
Data observability helps teams locate the stage where an error occurred so it can be resolved more quickly.
How long does it take to implement a DataOps monitoring framework?
The implementation time depends on factors such as platform size, system complexity, number of API integrations, and the existing data infrastructure.
Companies can begin with basic API health checks and data validation and gradually add automated testing, observability, load testing, and dashboard validation.
How does DataOps improve MarTech data quality?
DataOps introduces automated checks throughout the data pipeline. These checks help confirm that marketing data is collected correctly, processed properly, and displayed accurately.
This reduces manual troubleshooting and helps prevent unreliable information from reaching customers.
What are the advantages of data observability for SaaS platforms?
Data observability can help SaaS companies find data problems earlier, reduce troubleshooting efforts, improve reporting accuracy, and provide a more reliable customer experience as data volumes continue to grow.
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