Enhancing Data Quality and Collaboration

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Enhancing Data Quality and Collaboration

CGA’s Data Reporting and Evaluation Committee is spearheading several initiatives to enhance data quality and consistency, enabling stakeholders to focus corrective actions where they can have the most substantial impact. These efforts are crucial for reaching our 50-in-5 goal. Without detailed information on root causes, work types and other key data, the industry's ability to target improvements effectively is limited. Conversely, when organizations have access to robust, high-quality data, they can more effectively allocate resources to improvement efforts and track progress over time.

Data Improvement Highlights

The impact of "unknown" categories in data analysis is significant and may be masking important trends.
North Carolina 811's success story demonstrates how focused improvement efforts can significantly enhance Data Quality Index (DQI) scores, reduce unknown root causes and drive actionable damage insights.
CGA’s Data Committee has developed a new root cause analysis flow chart to guide users in selecting more specific root causes, aiming to reduce reliance on catch-all categories and uncover deeper issues.
While work type represents the largest category of “unknown” data in DIRT, classification even of known work types is also a challenge, with efforts underway to standardize mapping of information to this critical data point.
CGA is reviewing and documenting the complexities of mandatory reporting across states through a comprehensive survey of 811 centers and state regulators, aiming to clarify requirements and improve data analysis.

A Model for Data Quality Index (DQI) Improvement

On a foundational level, it is critical for every organization to not only submit damage and near- miss data to DIRT, but also to regularly assess and improve the quality of data. DIRT provides a Data Quality Index (DQI) score that gives submitters actionable information about the completeness of their reports, and where it’s most important to improve quality.

DQI enhancements can drive our ability to test hypotheses and develop insights – for example, NC811’s intentional DQI improvement enabled the 811 center to analyze damages associated with locating practice errors as described in the preceding section of this Report. State regulations require excavators to report damages to NC811, which then uses an API to enter the information into DIRT.

In 2021, NC811 began a focused effort to improve its data quality. Initially, its average DQI was in the low 50s, largely due to the frequent reporting of "other" instead of specific root causes. By implementing a process change that included a dropdown menu of DIRT root causes and training customer service representatives to capture this information, NC811 dramatically improved its DQI to 79.5 in 2023, with unknown root causes dropping from nearly 100% to around 8%.

Refining Root Cause Analysis

To further improve root cause data and enable DQI improvements, the Data Committee developed a flow chart to guide users through selecting the most appropriate root cause. This tool aims to reduce reliance on catch-all categories like "Locator Error" and "Improper Excavation" by encouraging consideration of more specific root causes before defaulting to these broader categories. While these catch-all categories are preferable to "Unknown/Other," they can mask deeper issues that need to be addressed.

The development of this flow chart raised important questions about how to handle situations where an area was marked but the 811 ticket was invalid. While many organizations might draw from repair-claims or enforcement-based data to categorize these incidents as No Locate Request or one of the Invalid Use of Ticket by Excavator root causes, the Data Committee's approach encourages a more nuanced analysis. By focusing on the true root cause – defined as the point where a change in behavior could reasonably lead to a different outcome – this method can help facility operators identify internal issues they can control, even in cases of invalid tickets.

Improving Work Type Classification

One of the most challenging aspects of DIRT data collection is accurately capturing the type of work performed. This data point consistently has the highest percentage of unknown entries, despite its potential value in tailoring outreach and education efforts to specific stakeholder groups. For example, fencing and landscaping contractors have different issues than fiber installers.

How much does telecom work contribute to damages?

Telecom work is the leading individual known work type associated with damages, but nearly three times as many damages are reported as "unknown." Presenting data as percent known assumes that what's masked in the unknown data follows the same patterns as known data.

The Data Committee is collaborating with OCSI to develop a comprehensive list of common work types from 811 tickets, with the goal of creating a searchable tool that maps these to recommended DIRT selections. Currently, some 811 centers accept any free-text description for work type, and there can be numerous ways of describing the same work. There are 30 “known” work type options in DIRT, but some 811 centers have hundreds or thousands in their systems. The Data Committee’s initiative to map common work types will encourage 811 centers to map to standardized work type classifications, leading to more consistent data analysis and potentially new or revised work type options in DIRT.

Understanding Mandatory Reporting Requirements

To address common questions about mandatory reporting and its impact on data quality and damage rates, CGA conducted a survey of 811 centers and state regulators in early 2024. The survey aimed to clarify various state damage reporting requirements and their relationship to DIRT reporting. Results were categorized into five groups based on the level and nature of reporting requirements, ranging from basic notification of damages to comprehensive reporting systems.

This survey serves multiple purposes: it helps establish parameters for defining "mandatory reporting states," provides resources for stakeholders to understand and comply with state requirements and informs the evaluation of how mandatory reporting considerations affect DIRT data analysis. By clarifying these aspects, CGA aims to improve data quality and consistency across the industry, ultimately contributing to more effective damage prevention strategies.