A Reproducible Framework for Detecting and Quantifying Join-Induced Metric Inflation

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@article{1716724,
      author = {Sai Lalitesh Pothukuchi},
      title = {A Reproducible Framework for Detecting and Quantifying Join-Induced Metric Inflation},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {3},
      pages = {867-881},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716724.pdf},
      abstract = {The modern analytics pipelines are crucial in the realisation of reliable decision-making, which is only possible with accurate key performance indicators (KPIs). There is, however, an inadvertent distortion of metrics by relational joins in data engineering processes, which results in Join-Induced Metric Inflation undermining analytical integrity. These distortions are usually compounded by the low data quality, such as the Duplicate and Null Key Impact, as well asComposite Key Integrity, that spreads the error by aggregating KPIs and distorts business intelligence. Addressing these issues, this paper suggests a Reproducible Data Engineering Framework that will be used to detect join-induced distortions systematically, quantify them, and mitigate them. The framework combines Automated Join Risk Flagging, which allows for the identification of high-risk joins before metrics are reported and an inflation estimation mechanism that forecasts the degree of possible KPI distortions. By ensuring that such a framework is incorporated into the routine ETL processes, organisations can guarantee that such workflows are reproducible in nature, uphold data integrity, and foster confidence in the results of the associated analytical activities. The framework is demonstrated through empirical examples and conceptual discussion of how each operationalises Data Quality Risk Assessment and KPI Distortion Detection, offering practical advice on how an analyst can operate the framework as well as governance of an enterprise-wide data setting. The research work has wider implications than its technical mitigation, such as in supporting better analytics governance and reproducible research practices, which are the keys to large-scale, data-driven decisions.},
      keywords = {Join-Induced Metric Inflation, Data Quality Risk Assessment, KPI Distortion Detection, Automated Join Risk Flagging, Duplicate and Null Key Impact, Composite Key Integrity, Reproducible Data Engineering Framework},
      month = {September},
      doi = {https://doi.org/10.64388/IREV7I3-1716724}
  }