Data Quality Frameworks for Retail Operations Excellence

Data Engineering | Factspan

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    Executive Summary:

    Struggling with operational challenges and unreliable data, our global retail client sought transformation. Issues like missing data, duplicates, and outdated records hindered strategic decisions, customer satisfaction, and resource efficiency.

    Factspan’s advanced data engineering solution was achieved through the Python-based Data Quality Framework, incorporating tools like Soda, DBT, and GCP BQ, which emerged as the transformative solution. Beyond technical intricacies, the intervention revitalized the company culture, instilling confidence in decision-making. It marked a pivotal shift, ensuring accuracy in decision-making, boosting customer satisfaction, and optimizing resources. The result? A revitalized operational landscape marked by improved data reliability, faster decision-making, and a notable reduction in operational costs. Customer satisfaction soared with a significant decrease in churn. Factspan’s comprehensive solution propelled our client to operational excellence, fostering resilience and innovation in the face of dynamic challenges.

    Project Highlights:
    • 20% increased operational efficiency through advanced data engineering
    • Achieved a smooth transformation using Soda, DBT, and GCP BQ
    • Clear data inputs led to a 25% surge in confidence and decision-making
    • Revitalized operations with a 30% improvement in data reliability
    • Notable 25% decrease in churn, boosting customer satisfaction
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