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Global real estate leader

Global real estate leader delivers trustworthy data across a complex application landscape and hybrid data environment

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increase in testing efficiency

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test coverage across structured and semi-structured datasets

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Company overview

Industrial real estate has grown increasingly complex in recent years, requiring businesses to navigate market instability and rapidly changing consumer and business behavior. Successfully adapting to these fluctuations hinges on the ability to pivot quickly in response to the market. A leading industrial real estate company realized that by harnessing the power of big data, they and their customers could make business and operational decisions faster and more confidently. The company developed a data-driven methodology that connects logistics real estate with the flow of goods through supply chains and implemented automated data integrity strategy to ensure the data remained trustworthy and decision-grade.

With a complex set of data, sourced from thousands of properties worldwide, the QA team began outlining a strategy to automate data integrity testing across the entire data journey, from ingestion across dozens of sources through the data warehouse and to the company’s BI tools and business processes. According to the manager of quality assurance, “Manual comparison of our entire records library in the database is virtually impossible.”

Alleviating the hundred-hour headache

With a variety of mission-critical enterprise applications that collectively create millions of data points within the org’s vast and growing portfolio, ensuring that data’s accuracy as it travels between source systems, data warehouses, and reports spells the difference between successful daily operations and chaos.

The QA team is charged with maintaining data integrity across these critical apps, including Snowflake, Salesforce, and a variety of proprietary and industry-specific software. Manually checking millions of data points as they travel across a complex and distributed IT environment, required hundreds of hours each release cycle. Despite this effort, the team still struggled with data loss, errors, and miscommunications across data teams and other business units that eroded confidence in data quality.

To both alleviate the testing burden and minimize the risk of data errors and losses, the team implemented Tricentis Data Integrity, which enables completely automated end-to-end data testing across all layers of the data ecosystem.

Challenges

  • Validating all ETL (extract, transfer, and load) & ELT (extract, load and transform) data movements across heterogenous data sources was time-consuming and error-prone
  • Manual data integrity testing cost the team hundreds of hours that could be spent on more strategic tasks
  • The team lacked a consistent method for validating against disparate data sources to ensure the accuracy of critical business reports
  • Data errors had eroded business leaders’ confidence in data quality and ability to make data-driven decisions

Continuous data integrity checks drive better business outcomes

The QA team now ensures data quality on daily data movements using their ETL tool throughout their continuous integration/continuous development (CI/CD) pipeline, so they used Tricentis Data Integrity to automatically trigger Azure pipeline jobs once the data gets moved by their ETL job.

This, in turn, calls the Tricentis Data Integrity scripts to validate the entire data set against two criteria: source and target schema. Tricentis Data Integrity then sends a consolidated report against the entire data load and provides an overall health status report to the various business stakeholders involved.

Now we can compare semi-structured data against data sources like fetching JSON/XML data from Rest API against data sources, which helps our quality team to validate the entire record set instead of returning signoff based on sample entity.

–  Manager of Quality Assurance

By utilizing the continuous automation capabilities of Tricentis Data Integrity, the team is now able to ensure continuous quality across a heterogeneous data landscape. Their data quality assurance strategy has evolved from a time-consuming “stare-and-compare” methodology to a comprehensive, automated approach that supports the company’s broader IT transformation vision.

Tricentis Data Integrity’s robust reporting capabilities has increased visibility into data quality and enabled the team to gain control of their massive data environment. Instead of scrambling to manually verify that their data remained reliable through its many transformations, they can now automatically detect and prevent potential data quality issues before they move further downstream and report on data quality with boosted confidence and clarity. With this peace of mind, the team can maintain day-to-day operations of their global business soundly with the assurance that the billions of rows of data moving through their infrastructure is validated as goods move through the org’s global logistics supply chain.

Snowflake is the core of the org’s data warehouse infrastructure, and the QA team relies on Tricentis Data Integrity to ensure ongoing system modifications do not compromise Snowflake data or processes.

Our confidence factor was near zero before, and now we can say it’s near one hundred (percent).

–  Manager of Quality Assurance

Results

  • Automated testing now checks billons of records against structured and unstructured data sources, offering data quality at scale
  • Regular validation check against multiple building blocks is now integrated into the CI/CD pipeline, increasing data integrity & consistency
  • Removing the need for manual validation has saved hundreds of hours of effort, resulting in an 90% increase in efficiency
  • Improved confidence in both the data validation process and in the quality of business-critical reports

Implementing a platform approach to quality

After reaping immediate gains from Tricentis Data Integrity, the team invested in Tricentis Tosca to scale functional test automation.

Implementing Tricentis qTest was the natural next step to improve visibility across Tosca and the other test automation tools used across the business. qTest increases test automation efficiency by centralizing management and reporting across test automation tools.

To simplify and scale performance testing, the team implemented Tricentis NeoLoad.

With a unified approach, the team has made “a lot of progress” towards a mature testing practice that can support large-scale IT transformation. She says they’ve made particularly significant strides “with regression testing and performance testing through NeoLoad’s integrations with Tosca.”

Before using the Tricentis platform, we did not have a peaceful signoff to production. Now, our teams – as well as the business project teams – are very confident when they receive the automated report results and there are no issues identified and we’re ready to move to production. That kind of confidence we can now bring to all our processes. That is our achievement by introducing Tricentis.

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