Structured, Systematic Threat Based Approach to Evaluate and Improve Data Quality to Facilitate Digital Transformation
Bibliographic record
Abstract
Abstract Pipeline operators are rapidly and increasingly moving towards digital transformation in order to harvest efficiencies and achieve higher levels of reliability and safety. Fueled by advances in technology such as cloud computing and machine learning, data is considered a key asset, and pipeline operations are increasingly driven by information and analytics. However, successfully achieving a digital transformation toward reliable and high-quality data requires mature processes for obtaining, managing, evaluating, and continuously improving data quality. During a review of pipeline risk assessment results, a pipeline operator (Operator) found that risk results for a particular pipeline were driven by the mainline coating type being listed as “un-coated.” However, further review of the records showed that the pipeline, in fact, was coated. One of the Operator’s foundational principles is ‘data as an asset’. Thus, the Operator understands the critical impact of such data inconsistencies across many potential receptors, from financial impacts to public safety. Additionally, mature processes enhance confidence in prioritizing the “right work.” Data quality is essential for the use of historical data, interoperability across various data systems, and generation of useful analytics. The data quality process maturity (Process maturity) evaluation aims to assess all processes, capabilities, and governance required for ensuring high data quality. As a result, the Operator decided to rigorously evaluate their data quality and the maturity of data quality processes. The data quality assessment involved creating a comprehensive list of data elements required to assess a particular threat, prioritizing data elements, and documenting data storage by the source system. The data quality was then evaluated using Key Performance Indicators (KPIs), establishing a baseline. An organization’s Process maturity varies from level one (Initial) to level five (Optimized). The Process maturity of the Operator was assessed on five evaluation areas: Governance, Organization & People, Data Standards, Requirements & Metrics, Process Efficiency, Technology & Tools. Results of the evaluation led to the identification of actionable gaps. The process, as developed, leverages guidance provided in ISO (8000-8) [3] for data quality assessment and DNVGL-RP-0497 [4] for Process maturity evaluation. This paper presents a step-by-step approach developed for and successfully employed by the Operator as applied to pipeline integrity threats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.197 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.011 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".