Data Quality Metric Library

The percentage of pavement layer records with a layer date in the reported financial year and a known material and source, recorded width when not full width, recorded thickness between 20mm and 500mm, and recorded Work Origin.

Metric Purpose

Confirm pavement records attribute data added in the reported financial year is likely to be accurate and thus is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Without populated attribute data for pavement records, analysis and investment decision-making could be impacted due to data exclusions or assumptions for missing information.

Potential reason(s) for not being at the expected standard unique to this metric

Untrained staff collecting as-built inventory data. Lack of data validation checks as part of adding inventory record to RAMM. The system allows for invalid records to be entered. Lack of data standard on how geotextile/fabric layers are to be recorded.

The percentage of the achieved chipseal resurfacing renewals reported in TIO as-builted in RAMM (in m²) with a work category of 212 for reported financial year.

Metric Purpose

Confirm the financial years chipseal renewals achievement reported in TIO have been recorded, and are identifiable in RAMM.

If reseals that have short lengths or narrow widths are not loaded into RAMM, this could effect the result of this metric.

Consequence of Poor Quality Data

Poor capture of chipseal resurfacing renewals in RAMM can have an impact on modelling outputs and forward works programming including analysis, annual achievement reporting and investment decision making.

Poor data will impact the results of the ONRC and ONF Cost Efficiency Performance Measures.

Potential reason(s) for not being at the expected standard unique to this metric

Lack of, or poor process and trained personnel for recording correct work category in the carriageway surface table for sealed resurfacing work claimed under work category 212.

Reported achieved quantity from different source than as-builted records in RAMM.

Source data used for reporting achievement is not complete or up to date when needed.

Uncertainty around the work category to assign the surfacing component of the pavement rehabilitation to.

Specific comments on the metric

The metric compares RAMM totals to what is recorded in TIO. An exact match will give a metric result of 100%.

The threshold for minor issues is more than +/- 5% of an exact match i.e. RAMM is less than 95% of TIO or more than 105%.

The threshold for major issues is more than +/- 15% of an exact match i.e. RAMM is less than 85% of TIO or more than 115%.

The percentage of the achieved asphaltic concrete resurfacing renewals reported in TIO as-builted in RAMM (in m²) with a work category of 212 for reported financial year.

Metric Purpose

Confirm the financial years asphalt renewals achievement reported in TIO have been recorded, and are identifiable in RAMM.

If reseals that have short lengths or narrow widths are not loaded into RAMM, this could effect the result of this metric.

Consequence of Poor Quality Data

Poor capture of asphaltic concrete resurfacing renewals in RAMM can have an impact on modelling outputs and forward works programming including analysis, annual achievement reporting and investment decision making.

Poor data will impact the results of the ONRC and ONF Cost Efficiency Performance Measures.

Potential reason(s) for not being at the expected standard unique to this metric

Lack of, or poor process and trained personnel for recording correct work category in the carriageway surface table for sealed resurfacing work claimed under work category 212.

Reported achieved quantity from different source than as-builted records in RAMM.

Source data used for reporting achievement is not complete or up to date when needed.

Uncertainty around the work category to assign the surfacing component of the pavement rehabilitation to.

Specific comments on the metric

The metric compares RAMM totals to what is recorded in TIO. An exact match will give a metric result of 100%.

The threshold for minor issues is more than +/- 5% of an exact match i.e. RAMM is less than 95% of TIO or more than 105%.

The threshold for major issues is more than +/- 15% of an exact match i.e. RAMM is less than 85% of TIO or more than 115%.

The percentage of current top surface records, based on the surface structure, with a valid chip size (AM>=7, CS<=6, 2nd chip recorded for 2CHIP, RACK, B/S, 3CHIP, CAPE, and COMB), surface function (AC <> "M"), a recorded binder type, and a recorded top surface life.

Metric Purpose

Confirm surface records attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Without populated attribute data, surface records could have no network-level expected surface life, which could impact life cycle analysis and investment decision-making.

Potential reason(s) for not being at the expected standard unique to this metric

Untrained staff collecting as-built inventory data. Lack of data validation checks as part of adding inventory record to RAMM. The system allows for invalid records to be entered.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, there are surface records that will legitimately fail this validation test (i.e. Slurry surfaces). These typically constitute a small proportion of each network and have been considered in setting the grade ranges for this metric.

For AMDS, AWM currently populates a default chip size of 1 to the treatment length for AC rather than recording the mix designation number as the chip size, so the AC "chip size" test is not possible for AMDS using treatment length data alone. The metric joins to the surface structure table for that check.

The proportion of the number of surface records entered to RAMM with a surface date in the reported financial year that have a recorded start and end displacement within the limits of the road as defined in the carriageway table, and have a recorded surface width no greater than 2m wider than the carriageway width.

Metric Purpose

Confirm that the recorded surface inventory records in the financial year align with the network as defined in the carriageway table.

Consequence of Poor Quality Data

Potentially inaccurate data could be used to support decision-making processes, and the data summarised into the treatment length table will not reflect the network dimensions.

This could also result in incorrect reporting of renewal quantity and achieved life which feed into the ONRC AND ONF Cost Efficiency Performance Measures.

Potential reason(s) for not being at the expected standard unique to this metric

Poor QA process around as-built data collection.

Carriageway sectioning not actively managed/maintained.

Terratrip used for measuring start and end displacements not calibrated.

Specific comments on the metric

Not greater than 2m wider than the carriageway width was selected based on the guidance in SHDOM that a change in width of >2m should become a new carriageway section.

The proportion of surface records entered into RAMM with a surface date in the financial year and a Work Category assigned, and an original cost recorded where the recorded Work Category is 212 or 214.

Metric Purpose

Confirm surface records attribute data added in the reported financial year is likely to be accurate and thus is adequate for planning and decision-making purposes

Consequence of Poor Quality Data

Without a recorded original cost and work origin, there will be a reduced understanding of investment and performance, along with incorrect reporting of renewal quantity and achieved life which feed into the ONF and ONRC Cost Efficiency Performance Measures.

Potential reason(s) for not being at the expected standard unique to this metric

Poor or lack of process for the population of this attribute field. Lack of understanding of what costs are to be recorded.

Specific comments on the metric

30 June 2016 was selected as population of the original cost and work origin fields were requested of the sector from the 2016/17 financial year onwards.

The percentage of the length, based on treatment lengths, where the major top surface is newer than the underlying major pavement layer, the recorded pavement layer date is less than 3.5 years old at the end of the reported financial year, and the major pavement layer coverage is >=50% of the treatment length.

Metric Purpose

Confirm that a surface record exists for all pavement layer records added to the database in the last 3.5 years.

Consequence of Poor Quality Data

Where there is a newer pavement date than the surface date, this indicates a data error which could impact future renewal need analysis, forecasting and programming.

This could also mean incorrect reporting of renewal quantity and achieved life which feed into the ONF and ONRC Cost Efficiency Performance Measures.

Potential reason(s) for not being at the expected standard unique to this metric

No pavement layer data covering the majority of the treatment length section (i.e. major pavement layer and major surface records are for different portions of the treatment length).

Lack of use of pavement builder for completing inventory record updates in RAMM.

Poor, or lack of, process for managing and maintaining pavement and surfacing asset data.

Specific comments on the metric

The grade ranges make an allowance for treatment lengths which have little pavement layer data and the major pavement layer is associated with a shoulder widening, or similar.

The metric only includes treatment lengths where the major pavement layer coverage is >=50% of the treatment length.

The percentage of the total "in use" footpath (pathway) length entered to RAMM during the three year period to the end of the reported financial year.

Metric Purpose

Confirm that footpath (pathway) asset data is being added as new footpaths (pathways) are constructed/vested, and others are renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

The percentage of "in use" footpath (pathway) records with a valid recorded length (not null or zero), and width (>0.7m and <20m, and not null), an offset less than 40m (when recorded), surface date >= construction date and condition date (when recorded) >= surface date.

Metric Purpose

Confirm footpath (pathway) assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that footpath (pathway) assets are likely to be assigned to the correct road.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for 'historical' assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, there will be instances where a footpath (pathway) width falls outside the allowed range of this test.

Less than three years old has been considered a recent condition date.

The percentage of the total number of "in use" footpath (pathway) records with a known purpose, asset owner, location (start and end displacements and side), surface material and construction/surface date.

Metric Purpose

Confirm completeness of footpath (pathway) assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data may not be known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments about the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, there will be instances where a footpath (pathway) width falls outside the allowed range of this test.

Less than three years old has been considered a recent condition date.

The percentage of the total "in use" culvert (pipe) length entered into RAMM during the three year period to the end of the reported financial year.

Metric Purpose

Confirm that culvert (pipe) asset data is being added as new culverts (pipes) are constructed/vested, and others are renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

The percentage of "in use" culvert (pipe) records with a valid recorded length (not null or zero), and diameter/height (>=100mm and <8,000mm, and not null), an offset less than 40m (when recorded), and condition date (when recorded) >= construction date.

Metric Purpose

Confirm culvert (pipe) assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that the culvert (pipe) assets are likely to be assigned to the correct road.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for 'historic' assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, unknown constructed dates of 'older' assets and the ease and value of estimating these has been considered.

Less than three years old has been considered a recent condition date.

The metric will treat a construction/installation date of 1 Jan 1900 as an unknown date. 1 Jan 1900 is a default loading date in AMDS when there is no construct date, as the install date in AMDS is a required field.

The percentage of the number of "in use" culvert (pipe) records with a known drainage type, asset owner, location (displacement and side), material, culvert type (i.e. not "unknown") and construction date.

Metric Purpose

Confirm completeness of culvert (pipe) assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for 'historic' assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for unknown constructed dates of 'older' assets and the ease and value of estimating these.

northing and easting definitions are not in the AMDS schemas so these attributes are no longer tested in the Data Quality metrics.  

The metric no longer tests for culverts positioned beyond the end of the road.

Only include In Use assets for RCAs on AMDS. 

The percentage of the total "in use" surface water channel (channel) length entered to RAMM during the three year period to the end of the reported financial year.

Metric Purpose

Confirm that surface water channel (channel) asset data is being added as new surface water channels (channels) are constructed/vested, and others are renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

The percentage of "in use" surface water channel (channel) records with a valid recorded length (not null or zero), an offset less than 40m (when recorded) and condition date (when recorded) >= construction date.

Metric Purpose

Confirm surface water channel (channel) assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that the surface water channel (channel) assets are likely to be assigned to the correct road.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for 'historic' assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

Excludes surface water channel types of:

• SWCD (Surface Water Channel (Deep, >200 Below Seal Edge)),
• SWCS (Surface Water Channel (Shallow, <200 Below Seal Edge)),
• ESWC (Earth Surface Water Channel).

The percentage of the number of "in use" surface water channel (channel) records with a known channel type, asset owner, location (start and end displacement and side) and construction date.

Metric Purpose

Confirm completeness of surface water channel (channel) assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for 'historic' assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

Excludes surface water channel types of:

• SWCD (Surface Water Channel (Deep, >200 Below Seal Edge)),
• SWCS (Surface Water Channel (Shallow, <200 Below Seal Edge)),
• ESWC (Earth Surface Water Channel).

The percentage of the total number of "in use" streetlight lights (luminaires) recorded as replaced in RAMM for the three year period to the end of the reported financial year. Excludes when only lamp and/or gear is replaced.

Metric Purpose

Confirm that streetlight (luminaire) asset data is being maintained as streetlight lights (luminaires) are replaced/renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

Specific comments on the metric

For RCAs that have migrated to AMDS, the metric will filter using the owner of the luminaire. Where a luminaire also has a sub organisation assigned, the luminaire will be included if the owner type of the sub organisation is 'LA'. The metric will also check luminaires that are not attached to poles. 

For RCAs that have not yet migrated to AMDS, the metric will continue to use the owner of the carriageway section that the pole is located on, as the owner of the light cannot be accurately identified. Classic RAMM also still requires that a light be attached to a pole.

LIGHTS3 is now an Information Only metric. 

The percentage of the number of "in use" streetlight (luminaire) records located within the extents of the road as defined in the carriageway table and with an offset less than 40m (when recorded), and with condition date >= installation date (when recorded).

Metric Purpose

Confirm that streetlight (luminaire) assets are likely to be correctly located in relation to how the network is defined by the carriageway table, and that condition data is not out of date.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Untrained staff collecting as-built inventory data. Lack of data validation checks as part of adding inventory record to RAMM. Displacements recorded using an uncalibrated trip meter.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, if streetlight (luminaire) assets have been added into the database before the newly constructed road that they should sit on, they may have an offset that falls outside the defined ranges as they are temporarily assigned to another road.

For RCAs that have migrated to AMDS, the metric will filter using the owner of the light (luminaire) as AMDS allows for lights (luminaires) to exist with no outreach or pole. Where a light (luminaire) also has a sub organisation assigned, the light (luminaire) will be included if the owner type of the sub organisation is 'LA'.

For RCAs that have not yet migrated to AMDS, the metric will continue to use the owner of the carriageway section that the pole is located on, as the owner of the light (luminaire) cannot be accurately identified.

The metric only reports on In Use assets for AMDS.

The percentage of the number of "in use" streetlight light (luminaire) records with a known pole and light owner, known pole make and mounting type, known light make and model, known lamp make and model and pole and light installation dates. (Pole make, light make and light model cannot be checked for AMDS RCAs.) 

Metric Purpose

Confirm completeness of streetlight light (luminaire) assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Untrained staff collecting as-built inventory data. Lack of data validation checks as part of adding inventory record to RAMM.

Comments specific to the metric

AMDS supports luminaires not being attached to an outreach (bracket) or pole. Luminaires will be checked by this metric even if not attached to an outreach or pole.

Also, pole make, light make and light model are not in the AMDS standard so will not be checked for RCAs that have migrated to AMDS.

The percentage of the total "in use" railing (barrier) length entered in RAMM for the three year period to the end of the reported financial year.

Metric Purpose

Confirm that railing (barrier) asset data is being added as new railings (barriers) are installed/vested, and others are renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

Potential reason(s) for not being at the expected standard unique to this metric

Poor, or lack of process for updating the asset inventory table following renewal activity Renewal activity is delivered in quantities less than that identified for capitalisation by internal processes.

Specific comments on the metric

For AMDS, this metric will check the barriers table.

The percentage of "in use" railing (barrier) records with a valid recorded length (not null or zero), an offset less than 40m (when recorded) and condition date (when recorded) >= installation date.

Metric Purpose

Confirm railing (barrier) assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that railing (barrier) assets are likely to be assigned to the correct road.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, unknown constructed dates of 'older' assets and the ease and value of estimating these has been considered.

Less than three years old has been considered a recent condition date.

For AMDS, this metric will check the barriers table.

The percentage of the number of "in use" railing (barrier) records with a known railing type, asset owner, location (start and end displacement and side), material and installation date.

Metric Purpose

Confirm completeness of railing (barrier) assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for unknown constructed dates of older assets and the ease and value of estimating these.

For AMDS, this metric will check the barriers table.

The percentage of the total number of "in use" signs recorded as replaced in RAMM for the three year period to the end of the reported financial year.

Metric Purpose

Confirm that sign asset data is being maintained as signs are replaced/renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

The percentage of the number of "in use" sign records with recorded owner, class, type, material (background and legend), colour (background and legend), and installation date or recent known condition.

Metric Purpose

Confirm completeness of sign assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact investment decisions and asset valuations, and also could result in a lack of maintenance response.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions.

Less than 3 years old has been considered a recent condition date.

For RCAs that have migrated to AMDS, the metric will check sign type and class for Traffic Control Device (TCD) signs and will just check type for non-TCD signs. Also none of legend material, legend colour, and background material are in AMDS.

The percentage of the number of "in use" sign records located within the extents of the road and with a location and an offset less than 40m (when recorded), a height >=50mm and <=5000mm (when recorded), a width >=100mm and <=5000mm (when recorded), and condition date >= installation date (when recorded).

Metric Purpose

Confirm sign assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that sign assets are correctly located in relation to how the network is defined by the carriageway table.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact investment decisions and result in a lack of maintenance response.

Potential reason(s) for not being at the expected standard unique to this metric

Untrained staff collecting as-built inventory data. Lack of data validation checks as part of adding inventory record to RAMM. Displacements recorded using an uncalibrated trip meter.

Specific comments on the metric

The sign height limit is set to 50mm to allow for shorter height signs such as No Exit signs.

The percentage of the total "in use" retaining wall length entered in RAMM during the five year period to the end of the reported financial year.

Metric Purpose

Confirm that retaining wall asset data is being added as new retaining walls are constructed/vested, and others are renewed.

Consequence of Poor Quality Data

Poor updating of data has the potential to impact our investment decision-making, as well as hinder our understanding of the effectiveness of investment.

Poor updating of data can also result in inaccurate asset counts in asset valuations.

The percentage of "in use" retaining wall records with a valid recorded length (not null or zero), average height >=0.3m and <40m, an offset less than 40m (when recorded) and condition date (when recorded) >= construction date.

Metric Purpose

Confirm retaining wall assets attribute data is likely to be accurate and thus is adequate for planning and decision-making purposes. Confirm that the retaining wall assets are likely to be assigned to the correct road.

Consequence of Poor Quality Data

Inaccurate data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Incorrect road assignment also could lead to confusion and inefficiencies with inspections and other site based work.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for potential exceptions. For example, unknown constructed dates of 'older' assets and the ease and value of estimating these has been considered.

The height limit is set to 40m to try to catch cases where the height has been entered incorrectly eg 4.5 has been entered as 45.

The percentage of the number of retaining wall records with a known type, asset owner, location (start and end displacement and side), material, and construction date.

Metric Purpose

Confirm completeness of retaining wall assets attribute data is adequate for planning and decision-making purposes.

Consequence of Poor Quality Data

Poor data has the potential to result in misinformed decision-making which can impact renewal programme development, financial decisions, asset valuations and LoS delivery.

Potential reason(s) for not being at the expected standard unique to this metric

Attribute data not known for historical assets. Poor, or lack of, clarity on what attribute fields are to be collected and maintained for this asset type. Poor, or lack of quality control undertaken on inventory data.

Specific comments on the metric

The grade ranges were chosen by the project team to allow for unknown constructed dates of older assets and the ease and value of estimating these.