Problem Statement

Product Opportunity

Saudi Telecom used a method called random sampling, where they checked a fixed percentage of their data to find problems. This method often failed to capture critical data quality issues, as it did not adapt to the dataset’s variability or prioritize high-impact areas, leading to persistent gaps and errors.

According to industry research, the global data quality tools market is expected to reach more than $5.5 billion by 2025. Wipro recognized a substantial market opportunity to leverage AI/ML-driven automated data quality solutions.

Manual quality checks were inadequate for the telecom’s vast and complex data, resulting in operational inefficiencies.

The rule-based remediation engines in use were rigid and struggled to address diverse data issues effectively, driving the need for an AI-based solution.

Solution

• Using AI/ML to profile data by focusing on unusual patterns instead of random samples, adjusting the sample size based on data quality to find all important issues.

• Repairing data problems automatically with smart workflows, making checks faster and more effective for Saudi Telecom’s large and complex data.

My Role

As Product Manager, I owned the entire product lifecycle:

• Led user research—planning, executing, and analyzing client interviews and card sorting exercises with stakeholders from sales, engineering, and data operations teams.

• Authored all user epics, job stories, and acceptance criteria.

• Facilitated feature prioritization workshops and translated user needs into a must-have roadmap.

• Drove design sprints, QA, engineering stand-ups, and direct demos to the client.

• Owned tracking and communication for KPIs and release milestones.

Product Development Approach

Discovery

Definition

Ideation

Prioritization

Wireframing and Prototyping

Delivery