Quantifying and Mitigating Risk with Digtix and DigSights (New UI)

Quantifying and Mitigating Risk with Digtix and DigSights (New UI)

INTRODUCTION 

Knowing the risk associated with excavation jobs is paramount for successful damage prevention. Digtix provides the ability to easily identify risk and allocate the appropriate resources to address risk. 

Digtix considers millions of data points to provide a single risk score for people and projects. The higher the risk score, the greater the risk associated to that individual or project. It is possible to configure custom risk scoring factors to further support your organization's damage prevention plan.

NOTE: In our upcoming releases, we will be introducing many new and improved capabilities focused on risk management. Check back regularly for the latest improvements and information!

WHAT CONTRIBUTES TO RISK?

Risk can be considered as a combination of two quantifiable factors, as shown in the risk matrix (Figure 1) below:

1) The probability of an adverse event occurring aka “likelihood”.

2) The consequence if an adverse event does occur aka “impact”.

Figure 1: Risk Matrix

CONSEQUENCE/IMPACT

The consequence of an adverse event is usually specific to your organization. Worst-case scenarios can be reasonably approximated based on things that are (or can be) known:

• Asset footprint
• Infrastructure types
  • Gas vs fiber
  • Material
  • Size
  • Service lines vs transmission mains
• Customer footprint
  • Urban vs rural
  • Hospitals, schools, 911 centers, other points of interest

Maintaining quality geospatial information is a key risk mitigation factor. Knowing where your assets, customers, and points of interest are located will allow your organization to accurately quantify consequences for any adverse event that may occur.

Figure 2: Identification of Customer Footprint and Points of Interest

QUANTIFYING CONSEQUENCE VIA GEOSPATIAL DATA

Underground assets and customer service areas may be designated as "Critical" through the "Infrastructure Types" section of the Administration page.

Figure 3: Expand Menu

Figure 4: Select Administration Menu

Figure 5: Select Infrastructure Types

Figure 6: Critical vs Non-Critical Assets and Service Areas

Loading geospatial information into Digtix and designating areas as critical allows Digtix to determine if critical assets or service areas are in the scope of a locate request. This allows Digtix users to accurately quantify consequence for excavations.

IMPROVING GEOSPATIAL DATA QUALITY

Utility locators have the opportunity to provide field feedback on the GIS data loaded into Digtix. This can be done by creating a map note at the location where data quality could be improved. This map note can be sent to an internal team for consideration. Upon review, the GIS data may be corrected to provide a higher level of data quality, thereby improving your organization's ability to quantify risk probability and consequence factors.

Map notes may be created by "long clicking" the map at the location you wish to add a note. For more information, see the "Map Notes" section of the Introduction to the Ticket Map knowledge base article.

Figure 7: Long click the map location

Figure 8: Click Add Note

Figure 9: Type Note and Save

Figure 10: Example of note

MANAGING HIGH CONSEQUENCE AREAS ("HCA")

Designating infrastructures as critical provides Digtix the ability to determine if critical assets are in the scope of a locate request. This allows Digtix users to efficiently identify and prioritize high-impact locate requests.

As with all locate request fields, the new "Critical Infrastructure Close" field can be filtered, sorted, hidden, saved within views, and exported to Excel directly from the locate request grid.

The "Critical Infrastructure Close" field is considered within the Digtix Risk Score model.

Figure 11: Click Filters

Figure 12: Select Critical Infrastructure Close

The new "Critical Infrastructure Close" field may also be used as a condition when defining assignment rules. In this way, all locate requests involving a critical asset can be assigned to a user (with an optional alert) for careful review before dispatching to the field.

Figure 13: Select Administration Menu

Figure 14: Select Ticket Assignment

Figure 15: Assigning Locate Requests Based on Critical Infrastructure

AUTOMATED FOLLOW-UP TRIGGERS FOR HCA

It is possible to automatically trigger follow-ups based on infrastructure criticality/proximity. Such triggers can be configured from the "Triggers" tab on the "Follow Up Types" section of the Digtix Administration page. Follow-ups that are automatically triggered can also be automatically assigned to the appropriate damage prevention specialist via the "Ticket Assignment" section of the Digtix Administration page.

Figure 16: Configuring Automatic Follow-Up Triggers

Figure 17: Configuring Automatic Follow-Up Triggers

EXCAVATOR RISK

The "Excavators" page within Digtix provides a searchable registry of all excavators who have submitted a locate request or been involved in an incident. Digtix automatically calculates and maintains a risk score for every excavator based on past performance. 

The model that Digtix uses to calculate the risk score for each excavator is proprietary. Excavator risk scores range from 0 (low) to 100 (critical). Risk scores may be further increased or decreased by implementing risk-scoring factors specific to your Digtix environment. If your organization would like to explore custom risk scoring factors, please get in touch with the Digtix support team.

Figure 18: Select Excavators

The excavator risk score may be manually overridden by providing a value in the "Risk Score Override" field for that excavator.

Figure 19: Edit Excavator 

Figure 20: Excavator Risk Score Override

Excavator risk scores is one of the many factors used in determining the risk for locate requests.

LOCATOR (USER) RISK

Digtix automatically calculates and maintains a risk score for every defined user based on past performance.

The model that Digtix uses to calculate the risk score for each user is proprietary. User risk scores range from 0 (low) to 100 (critical). Risk scores may be further increased or decreased by implementing risk-scoring factors specific to your Digtix environment. If your organization would like to explore custom risk scoring factors, please get in touch with the Digtix support team.

Figure 21: Select Administration Menu

 Figure 22: Select Users

The user's risk score may be manually overridden by providing a value in the "Risk Score Override" field for that user.

Figure 23: User Risk Score Override

User risk scores is one of the many factors used in determining the risk for locate requests.

CONTRACT LOCATOR RISK

Digtix automatically calculates and maintains a risk score for every third-party contract locator that submits a positive response via the Digtix positive response service.

The model that Digtix uses to calculate the risk score for each contract locator is proprietary. Risk scores range from 0 (low) to 100 (critical). Risk scores may be further increased or decreased by implementing risk-scoring factors specific to your DigTix environment. If your organization would like to explore custom risk scoring factors, please get in touch with the Digtix support team.

Figure 24: Select Administration Menu, then Select Contract Locator Registry

The contract locator's risk score may be manually overridden by providing a value in the "Risk Score Override" field for that contract locator.

Figure 25: Contract Locator Risk Score Override

Contract locator risk scores is one of the many factors used in determining the risk for locate requests.

LOCATE REQUEST RISK

Digtix automatically calculates and maintains a risk score for every locate request using intelligent predictive models. The models that Digtix uses to calculate the risk score for each locate request are proprietary, however, the factors detailed above (excavator, locator, etc) are among those considered. Locate risk scores range from 0 (low) to 100 (critical). Risk scores may be further increased or decreased by implementing risk-scoring factors specific to your Digtix environment. See the "Customized Risk Score Modifiers" section below for more information.

Locate request risk scores are displayed in the locate grid's "Risk Score" column. Similar to other columns, the "Risk Score" column can be filtered, sorted, hidden, or moved to create the desired data visualization. 

Figure 26: Viewing Risk on Locate Requests

Locate request risk scores may be thematically mapped by selecting "Risk" in the "Color By" drop-down. This will color each locate request in the grid and on the map by the risk associated to each job.

Figure 27: Select Setting to Change Color By

Figure 28: Coloring Tickets by Risk

Risk scores may be overridden on a case-by-case basis in the "Risk Score Override" field if extraordinary circumstances are identified.

Figure 29: Click the Risk Score Drop-down

Figure 30: Locate Request Risk Score Override

Organizations should flag high-risk locates for extra attention. For example, triggering a follow-up in the form of a utility locate audit, excavation inspection, watch & protect, etc. could prevent an incident from occurring during excavation.

MIGRATING HISTORICAL RISK DATA INTO DIGTIX

If your organization tracked at-fault damage rates in another system prior to using Digtix, you may import that data into Digtix quickly and easily. Excavators, in-house locators, and third-party locators may have their locate and at-fault incident counts configured into their Digtix profiles. Digtix will then consider their pre-Digtix performance when calculating and maintaining their risk scores.

Figure 31: Select Excavators

Figure 32: Select Edit

Figure 33: Importing Excavator Performance Data Before Using Digtix

DIGSIGHTS: AI FOR INCIDENT PREDICTION

DigSights is a set of machine learning ("ML") models built using artificial intelligence ("AI"). While Digtix has a very robust set of risk algorithms built within it, Digtix may query DigSights in order to provide an enhanced view of risk for locate requests.

The DigSights ML pipeline rigorously examines billions of data points to test millions of different predictive models. It then selects the model which has demonstrated the best performance for quantifying risk.

For more information about DigSights, please see the knowledge base article DigSights Overview.