Client
Leading Foods Distributor
Industry
CPG
Solution
Data Quality AutomationA leading broadline foods distributor in the US grappled with the complexity of managing its extensive operations and diverse customer base. The organization faced significant hurdles in calculating the Total Cost to Serve (TCS), given the intricacies of their SKU-level analysis and multi-stage distribution network. Identifying cost drivers across customers, geographies, and seasons proved challenging, making it difficult to align operational strategies with business objectives. Additionally, the lack of robust Data Quality Management (DQM) processes hindered their ability to ensure data consistency and accurate decision-making.
Complex Network Operations
Inaccurate Cost Calculations
Inefficient Data Management
Traditional data management practices, such as manual data validation and data cleansing, were no longer scalable in the face of growing data volumes. The client needed a reliable solution to address inefficiencies, improve data integrity, and integrate advanced data governance frameworks into their workflows. Ensuring compliance with data quality compliance standards and enhancing customer data integrity were key priorities to drive better business intelligence and personalized customer experiences.
Quantzig introduced end-to-end data quality solutions tailored to the distributor's unique needs. By leveraging AI-driven DQM automation, Quantzig implemented an advanced Total Cost to Serve model that utilized dynamic data quality rules for cost calculations at SKU levels and across distribution stages. The model incorporated predictive analytics for data quality, enabling the identification of cost drivers and inefficiencies with precision.
To streamline operations, Quantzig deployed cloud-based data quality solutions integrated with automated data pipelines. These pipelines, coupled with AI-powered data remediation and real-time data validation tools, ensured accurate and actionable insights. A centralized data catalog and central rule library were established to maintain data governance frameworks, while customizable data quality dashboards provided clear visibility into data quality metrics and KPIs. Machine learning for data quality further enhanced the detection of data anomalies, supporting robust data integration tools for seamless workflows.
Quantzig also enabled self-service data quality platforms, empowering the client’s teams to address data issues independently. These platforms integrated enterprise data quality tools and metadata management to ensure data accuracy and alignment with regulatory compliance requirements.
The deployment of Quantzig’s solutions transformed the client’s data management landscape, delivering significant improvements in operational efficiency and cost savings. The AI-driven data monitoring tools facilitated real-time monitoring, allowing the client to identify and address inefficiencies across their network. The integration of data enrichment processes enhanced the quality and reliability of business insights, leading to more informed decision-making.
Key results included the identification of high-cost customers, SKUs, routes, and geographies through advanced data profiling and analysis. The solution also improved data consistency and integrity, enabling improved personalized customer experiences. Additionally, the client improved compliance with data quality standards, minimizing regulatory risk, and achieved cost savings through scalable, automated data quality solutions like AI-powered data remediation. Ultimately, the integration of data fabric systems and integrated data quality workflows enabled seamless data management, fostering trust in data governance frameworks and supporting measurable success.
