Insights Blog Multicultural Optimization Lifts Data Engine Quality, Speed and UX for Tech Leader

Case Study

Multicultural Optimization Lifts Data Engine Quality, Speed and UX for Tech Leader

To elevate overall data quality, speed and user experience for speakers of dozens of languages in multiple countries, the Client needed highly specialized engineers to create a scalable global program.


Client urgently needed to improve data quality and speed for speakers of dozens of languages in multiple countries.

Poor quality and slow reaction times were creating a negative user experience.


Qualitest deployed highly experienced engineering teams who were also skilled in local languages and dialects.

The teams developed automatic tools and mechanisms to improve quality and speed and enable scalability.


Qualitest teams achieved a significant improvement in data quality, resulting in increased users and ad revenues.

Projects were completed several months ahead of plan due to automation.

Client overview

The Client is a multinational technological company and a provider of data for news, audio, video and other categories.

With social turmoil, the COVID pandemic and the accelerating pace of world events, more people constantly seek information online in real time. Leading websites rely on the Client not only for general data topics, but also for breaking news, progression of events, market trends and other timely information. This requires the Client to be agile and quick, able to scale and deliver flawless performance across numerous devices, locations, scenarios and languages.

Bringing digital data up to speed across borders

When the Client approached Qualitest, filtering data maturity in English was considered high. However, in many other languages, as well as the dialects and variations of those languages in different countries (for example, French in France vs. French in Canada), the maturity was considered significantly lower.

To elevate the filtered data overall, the Client needed a scalable global program. Because data capabilities drive a large portion of the Client’s revenues, there was an urgent need to establish such a program quickly and be able to scale it significantly year over year.

To undertake this program, the Client wanted a Quality Engineering partner who:

  • Understood the Client’s industry and business rationale, with the eventual goal of a multi-year engagement.
  • Understood the current production usage and metrics.
  • Could support different languages and locales.
  • Was able to self-manage the program and provide an SLA to hundreds of concurrent projects.
  • Could provide global coverage via a 24/7 model.
  • Could maintain a model with the ability to be flexible based on changing needs.

A three-phased project built to scale

Qualitest implemented an iterative process model, with three clear, well-defined phases.

Phase 1: Proof of concept

To identify the right framework, Qualitest engineers/team leads undertook enterprise-level deep dives and fact-finding excursions into the Client’s existing production systems, documenting findings carefully to create a preliminary knowledge bank. The Qualitest teams gained a detailed understanding of the unique logic and tools required.  During this phase, our teams began delivering projects across different countries supporting multiple languages and establishing a mutually agreed-upon SLA for the program as a whole.

Our engineers also took it upon themselves to master the understanding of some of the integrations between the data engine and other Client systems.

Phase 2: Scaling to support the different countries, languages and technology stacks involved

With this firm foundation in place, Qualitest began the scale-up, creating an operational engine to:

  • Scale from a few teams to 20+ teams across different geographical locations (with hundreds of quality engineers and software developers in test).
  • Develop more and more automatic tools that would enable the teams to improve from project to project.
  • Define a set of quality metrics that would help in continuous improvement.

The outburst of COVID during this phase brought two unanticipated challenges: the “Great Resignation,” a global trend in which employees were leaving their jobs, and a significant shift in the way consumers used data, which necessitated rethinking many of the program’s basic assumptions. Through Qualitest’s internal rotation program, we were able to retain the vast majority of engineers and successfully scale the program up.

Phase 3: Steady state project support

Currently the projects are in steady state, while the teams are showing continuous improvement month after month. Today we focus on improving a Qualitest delivery model known as 3-I:

  • Intelligent Optimization
  • Integrated Coverage
  • In-Sprint Agility

Key benefits

The Qualitest team of engineers not only met all the Client’s stated goals within the allotted time frame, but also exceeded many of their targets.

  • Supported existing data operations with close to no new failures, while scaling many new programs.
  • Scaled the program successfully, allowing support for hundreds of projects a year.
  • Improved a variety of processes through smart automation, reducing cycle times with no compromise to ROI.
  • Deployed significantly more improvements than originally planned.


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