NCC BULGARIA

Technical/scientific Challenge:

It was discovered that managing the growing complexity of datasets in logistics operations presented a significant challenge for maintaining high data quality. The organization generates and processes large-scale data from diverse sources, including vehicle tracking, operational schedules, customer orders, and financial records. These data streams are integral to ensuring timely deliveries, optimizing resource allocation, and supporting real-time decision-making. However, inconsistencies, incomplete records, and delayed updates often disrupted workflows, leading to inefficiencies and potential operational risks.

The challenge extended beyond handling large data volumes. It required a systematic approach to assess and enhance data quality across critical dimensions, such as accuracy, completeness, timeliness, uniqueness, validity, and consistency. Disparate data formats, incomplete datasets, and legacy system integrations further complicated the task, making it difficult to ensure reliable and consistent data for decision-making.

Key technical challanges:

  • The integration of datasets from multiple systems revealed significant inconsistencies, making it difficult to maintain trust in the data. Vehicle tracking systems provided real-time updates, while manual or semi-automated systems, such as checkpoint logs, often introduced conflicting or incomplete information. Harmonizing these datasets required identifying discrepancies at their source and ensuring synchronization across all systems without introducing delays. The organization faced the ongoing challenge of ensuring that data from diverse origins adhered to uniform quality standards, which was critical for enabling accurate analytics and efficient operations.
  • The absence of critical data fields or delays in updating records presented a bottleneck for decision-making processes. Incomplete datasets—such as missing delivery details, customer contact information, or priority indicators—undermined operational efficiency and increased the risk of errors. Furthermore, delayed updates from real-time systems hindered the ability to adjust plans dynamically, particularly in time-sensitive scenarios. Addressing this issue required solutions capable of detecting and compensating for gaps in data while maintaining the integrity and reliability of information flowing through the system.
  • The reliance on a mix of legacy systems and modern analytics tools introduced complexity in ensuring seamless data integration. Legacy systems often produced unstructured or inconsistently formatted data, which clashed with the structured outputs required by modern analytics platforms. This misalignment led to inefficiencies in data validation and transformation. The organization required a robust strategy to bridge these technological gaps, ensuring that data from legacy systems could be reliably ingested and integrated with newer systems without compromising quality or introducing additional complexity.
  • As operations expanded, the volume and complexity of data increased exponentially. Managing this growth demanded a scalable infrastructure capable of processing and analyzing larger datasets efficiently. Without proper scalability, the organization risked system bottlenecks, latency in data processing, and decreased operational efficiency. The challenge involved not only increasing the capacity of existing systems but also designing workflows and algorithms that could adapt fluidly to higher loads and more intricate processing requirements. Achieving scalability required careful optimization to balance computational demands with resource availability, ensuring smooth and efficient operations.
  • Adhering to stringent regulatory standards, such as GDPR, required a robust data management framework to ensure compliance with data protection and privacy rules. Ensuring that personal data was stored, processed, and shared in line with these regulations was critical for avoiding legal and reputational risks. Additionally, the organization’s operational requirements demanded precise, high-quality data to meet service-level agreements (SLAs) and maintain customer trust. The dual need for regulatory compliance and operational precision added layers of complexity to the data quality management challenge, requiring automated validation mechanisms to enforce adherence to both external and internal standards.
  • Timely decision-making in logistics operations hinges on the ability to process and analyze data in real-time. The dynamic nature of logistics—where routes, schedules, and resource allocation often change rapidly—demanded that data pipelines could process and validate incoming information with minimal latency. The challenge lay in ensuring that this real-time processing capability did not compromise data quality or overwhelm system resources, particularly as the scale and complexity of operations grew.

Solution: 

To address the outlined technical challenges, the proposed solution focuses on developing a robust framework for data quality enhancement using Python Deequ. This approach emphasizes improving dimensions of data quality — AccuracyCompletenessTimelinessUniquenessValidity, and Consistency — by leveraging programmatic components tailored to the unique requirements of logistics data management. Each function is designed to accept external test datasets, apply targeted quality-improvement measures, and produce enhanced datasets, demonstrating measurable improvements in data quality. The framework not only addresses existing gaps but also ensures ongoing adaptability to dynamic data requirements through systematic evaluation and validation processes.

The solution operates in three phases for each function:

  1. Initial Quality Assessment: Each dataset undergoes a comprehensive evaluation using Python Deequ’s built-in and customized methods. This baseline assessment identifies data quality issues across predefined dimensions, serving as a benchmark for subsequent improvements.
  2. Application of Quality Enhancement Functions: A programmatic function specifically designed for the targeted dimension (e.g., Accuracy, Completeness) is applied to the dataset. These functions leverage Python Deequ’s capabilities alongside custom Python methods to resolve detected issues, such as filling missing values, harmonizing formats, or identifying duplicates.
  3. Post-Improvement Quality Assessment: After the function has been executed, the dataset is re-evaluated using the same quality assessment methods. This final assessment quantifies the improvements achieved and ensures that the enhancements meet quality expectations.

Programmatic components for each data quality dimension

  • The accuracy component ensures that data values conform to real-world expectations and predefined standards. It begins by evaluating deviations from expected benchmarks using statistical methods such as mean and variance analysis. Identified inaccuracies, such as outliers or values outside acceptable ranges, are addressed using data correction techniques like interpolation or predictive modeling. Post-enhancement validation confirms that the corrected values meet accuracy requirements, making the dataset reliable for analytics and operational decision-making.
  • The completeness component focuses on addressing missing or incomplete data, ensuring datasets are comprehensive and ready for processing. An initial assessment identifies gaps by detecting null or missing entries. To enhance completeness, the function employs data imputation techniques, such as replacing missing values with mean, median, or other contextually relevant placeholders. Finally, a post-enhancement evaluation verifies that the dataset is now complete, eliminating gaps that could hinder analysis or operations.
  • The timeliness component ensures data is updated within defined timeframes to support real-time decision-making. Initially, the data is assessed for delayed updates or missing timestamps, identifying records that fall outside acceptable time windows. Enhancement is achieved by annotating or correcting delayed records to align with operational requirements. A final evaluation ensures that all records meet timeliness standards, enabling efficient and timely data-driven decisions.
  • The uniqueness component eliminates redundancies and ensures that key identifiers, such as order IDs or vehicle numbers, are distinct across the dataset. The initial assessment identifies duplicates using metrics for distinctness and uniqueness. Enhancements are applied by removing duplicate entries and enforcing unique constraints. Post-assessment checks validate that all records now have distinct values, safeguarding the dataset’s integrity and ensuring a single source of truth.
  • The validity component ensures data conforms to predefined rules or patterns, such as ensuring product codes or date formats align with expected structures. During the initial assessment, compliance checks identify deviations from these rules. Enhancements involve applying algorithms to standardize and correct fields, ensuring conformity to expected patterns. A post-enhancement evaluation confirms that all records meet validity requirements, enhancing the reliability of the dataset.
  • The consistency component harmonizes data across related fields or systems, ensuring alignment and eliminating discrepancies. The initial assessment detects mismatches or conflicting values between interdependent fields. Enhancements involve applying reconciliation rules to resolve conflicts and standardize values. A post-assessment validation ensures that the dataset is now consistent, enabling seamless integration and reliable insights.

The solution is designed to handle the growing scale and complexity of logistics data operations. By modularizing each quality dimension into standalone programmatic functions, the framework ensures scalability, allowing seamless integration into existing systems. The use of Python Deequ provides a foundation for automated, real-time quality management while the customized components ensure adaptability to industry-specific needs. This hybrid approach balances computational efficiency with precision, ensuring that high data quality can be maintained even as operations scale.

Scientific impact:  

  • The solution leverages Python Deequ to automate quality checks across critical dimensions—Accuracy, Completeness, Timeliness, Uniqueness, Validity, and Consistency—while integrating custom algorithms to address domain-specific challenges. This hybrid approach not only demonstrates the practical utility of advanced data validation tools but also contributes to the development of customizable frameworks that can adapt to varied industry requirements. This modular design serves as a model for future research and applications in data quality management.
  • By emphasizing real-time data processing and quality assurance, the framework addresses one of the most pressing needs in logistics and operational analytics. The integration of real-time evaluation and enhancement functions ensures that decision-making processes remain dynamic and informed, even as data volumes and complexity grow. This aligns with contemporary demands for real-time analytics in other industries, making the framework highly relevant across multiple fields.
  • The framework tackles the long-standing challenge of integrating legacy systems with modern analytics tools. By harmonizing data across heterogeneous systems and ensuring uniform quality standards, the solution provides a pathway for organizations to modernize their data infrastructures without disrupting ongoing operations. This approach contributes to the broader scientific discourse on data integration, paving the way for smoother transitions in digital transformation initiatives.
  • The scalability of the solution, achieved through distributed computing and modularization, represents an essential scientific contribution. As datasets grow in size and complexity, the framework’s ability to scale both horizontally and vertically offers a replicable model for other domains facing similar challenges. Its adaptability ensures that the methodology can be tailored to different organizational or domain-specific needs, expanding its scientific and practical relevance.
  • The use of a structured evaluation cycle—initial assessment, enhancement, and post-assessment—provides clear, measurable evidence of data quality improvements. By documenting the quantitative impact of each programmatic component, the solution highlights the importance of iterative, evidence-based approaches to addressing data quality issues. This emphasis on measurable outcomes supports the adoption of similar frameworks in academic and industrial settings.
  • The solution addresses critical regulatory requirements, such as GDPR compliance, by integrating automated validation mechanisms. This aspect not only supports legal adherence but also reduces risks associated with data breaches or inaccuracies. By bridging data quality management with regulatory compliance, the work establishes a precedent for embedding legal considerations into technical frameworks, enriching the broader scientific discourse on data governance.
  • While developed for the logistics domain, the framework’s methodology can be extended to other fields requiring robust data quality management, such as healthcare, finance, and telecommunications. By emphasizing modularity, automation, and adaptability, this work contributes to cross-disciplinary knowledge transfer, ensuring its broader applicability in solving data-related challenges across sectors.

Benefits: 

  • By ensuring accurate, timely, and complete datasets, the solution supports better-informed and more reliable decision-making across logistics operations. This minimizes risks associated with delays, resource misallocation, or incorrect forecasting.
  • The automated data quality checks reduce manual intervention, streamlining workflows and saving time. Operations benefit from reduced bottlenecks, improved scheduling, and optimized resource utilization, leading to cost savings.
  • The modular and distributed design of the framework ensures that the system can seamlessly scale to accommodate increasing data volumes and complexity, enabling organizations to grow without disruptions.
  • While tailored to logistics, the framework’s modularity and flexibility make it transferable to other industries, such as healthcare, finance, or manufacturing, broadening its potential impact.

Success story # Highlights:

  • The integration of Python Deequ and custom Python methods has made improving data quality in large-scale distributed environments more accessible and efficient. The solution automates complex quality checks, reducing reliance on manual interventions.
  • The modular and distributed nature of the solution ensures scalability to meet growing data demands while maintaining high standards for accuracy, completeness, timeliness, uniqueness, validity, and consistency. Its flexible design supports seamless adaptation to diverse operational requirements.
  • While developed for logistics, the solution’s modular approach and adaptability make it transferable to other domains such as healthcare, finance, and manufacturing, addressing similar data quality challenges.

Figure 1. Data Quality Improvement Across Dimensions. The bar chart illustrates the improvement in data quality across six critical dimensions: Accuracy, Completeness, Timeliness, Uniqueness, Validity, and Consistency. The “Before Improvement” bars represent the baseline quality percentages of the dataset before applying the proposed solution, while the “After Improvement” bars show the enhanced quality achieved after implementation. For example, the accuracy dimension saw an improvement from 70% to 95%, highlighting a significant increase in data reliability. Similarly, timeliness improved from 60% to 88%, indicating enhanced real-time data availability. The chart provides a clear visual comparison, emphasizing the measurable impact of the solution on data quality.

Contact:

  • Prof. Kamelia Stefanova, kstefanova at unwe.bg,
  • Prof. Valentin Kisimov, vkisimov at unwe.bg,
  • Assist. Dr. Ivona Velkova, ivonavelkova at unwe.bg

University of National and World Economy, Bulgaria