Program Name or Ancillary Text eere.energy.gov

You Can’t Manage What You Don’t Measure

FAST Data Quality Issues: Your VMIS Can Help!

FedFleet 2012

Ron Stewart – INL FAST Web Team

Fleet Data Management Best Practices

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• DOE, GSA, FAST team– always talking about it…– adding tools to find and fix issues– looking for good ideas on how solve this challenge

• What we see in FAST:– systems + tools + processes + policies + procedures + people– Your VMIS may be part of the problem…– … it definitely can be a big part of the solution!

FAST data quality: a real problem

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• 2 capabilities your VMIS should provide• 2 strategies for minimizing FAST data quality problems

Today’s take-aways…

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• Reviewed 45 agency FY2011 submissions– Agency “Data Quality & Consistency Report”– Prior-year OMB A-11 AMVFR– FAST’s “Data Call Summary” report– Basic aggregate fleet metrics:

• Average gross vehicle cost/mile

• Average gross vehicle GGE/mile

– Agency use of DQR late in the data call

Real examples…

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• Every agency reviewed had at least one issue identified• 33 of 45 agencies flagged on AMVFR• 33 of 45 agencies flagged on DQR• 15 agencies flagged on Data Call Summary• 14 agencies had no/minimal use of DQR

Real examples…

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• OMB A-11 AMVFR:– Monthly operating costs out of normal range– Acquisition costs out of range– Planned acquisitions w/o costs– Armored or LE vehicles w/o costs– Negative out-year inventory projections– Average age of owned vehicles out of range/negative– Missing one or more narrative sections

Real examples…

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• Data Quality & Consistency Report– Significant swings in one metric w/o corresponding supporting

increases– Mismatches in data

• Inventory w/o costs

• Inventory w/o fuel

• Inventory w/o mileage

• Operational data w/o inventory

Real examples…

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• Data Call Summary report– Significant agency swings relative to prior year

• Fuel cost & consumption

• Mileage

• Overall costs

• Inventory

– EISA Section 141 designation of acquisitions

Real examples…

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• “Gross” … as in visible in agency aggregates

• … but traceable back to– specific groups of vehicles or– specific slices of agency-submitted data

Gross errors

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• Attack the problems where it makes the most sense…

How do we solve this?

VMIS(s) Reporting

FAST

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• VMIS should characterize data similar to FAST– Vehicle types & classes– Fleet costs

• Difficult to compare VMIS to FAST w/o similar groupings

VMIS Capability #1

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• Compare VMIS to FAST– along every major data axis

• inventory, acquisitions, …

• fuel cost & consumption

• mileage

• fleet costs

– variety of reports and tools on the FAST side• DQR, AMVFR, query tool

• Investigate, understand, resolve major discrepancies

Strategy #1: Compare, compare, …

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• Robust report generation & data querying– Ability to group data similar to FAST– Ability to identify vehicles with anomalies

• stale, incomplete, or inconsistent data?

• compare vehicles to their own history?

• compare vehicles to their peers?

VMIS Capability #2

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• … early in time:– Use VMIS through the year to make sure vehicle-level data

quality issues are identified and resolved as early as possible• Dashboards

• Data quality & consistency threshold

• Monthly or quarterly data reviews

• … early in the process:– Fix the problems in the reporting process– Fix the problems in the VMIS(s)– FAST reporting

• … complete as early as possible

• … review as early as possible

Strategy #2: Attack the problem early

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• Key VMIS Capabilities:– Characterize data the same way FAST does– Robust report generation and querying

• Strategies to improve data quality:– Compare, compare, compare– Attack the problem early

Summary