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THE 6 COMMON DATA COLLECTION
MISTAKES YOU MAY BE MAKING
NATHAN KEBEDE, P.E.
Pavement Engineer, Cartegraph
THE 6 COMMON DATA COLLECTION MISTAKES
YOU MAY BE MAKING
Outline
• Introduction to asset data collection
• The 6 common mistakes
• Summary
• Q&A
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Infrastructure Assets: The Basics
• Government manages many assets used by the public
• Design, build, maintain, rehab, replace
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Asset Data Collection: The Basics
• Good asset data is needed to effectively and efficiently
manage all these assets
• The most important data types:
• Type of asset, location, condition
• Without this data, effective and efficient management of
these assets is almost impossible
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Asset Data Collection: The Benefits
• Improves operational effectiveness
• Boosts efficiency
• Facilitates forecasting
• Sharpens decision making
• Results in a High-Performance
team/department/organization
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Common Methods of Asset Data Collection
• Manual method—boots on the ground
• Automated
• Aerial imagery
• Right-of-way imagery
• LiDAR point-cloud
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Common Methods of Asset Data Collection
Manual
• Boots on the ground
• Could be done in-house
• Relatively slow
• Good for hard-to-reach
areas
Automated
• Use of vehicles or planes
(drones)
• Aerial imagery, right-of-way
imagery, LiDAR point-cloud
• Relatively fast
• Outsourced
• Post-processing needed
THE 6 COMMON
DATA COLLECTION MISTAKES
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#1: Failing to Set Goals
• Define specifically:
• What type of data should be collected?
• What format should the data be in?
• How will the data be used?
• Set goals before starting data collection
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#2: Choosing Quantity over Quality
• Examples of quantity over quality:
• Collecting signs but not identifying sign types
• Collecting asset with poor geospatial data
• Never compromise on quality for the sake of quantity
• Implement QA/QC measures
• Identify the quality level you desire for each data type collected
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#3: Creating Data Silos
• Examples that lead to data silos:
• Using proprietary systems that do not easily integrate with other
systems
• Lack of communication between departments that leads to the same
data being collected multiple times
• Make data easily accessible and shared
• Communicate with other departments using the same data
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#4: Relying on Human Memory
• Examples of relying on memory:
• A person with 20+ years in the department has all the information
• Human memory may forget details, or the person may leave
• Having data updated regularly and readily available across
the team is more sustainable
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#5: Overlooking Data You Already Have
• Examples of overlooking data:
• Old paper records stored in filing cabinets
• Data other teams may have about the asset
• Do a thorough records review to avoid duplicating efforts
• Identifying your data gaps leads to a more effective data
collection
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
#6: Trying to Do It Alone
• Most agencies underestimate the effort it takes
• There are firms that do data collection full time
• Have more domain expertise
• Are more efficient and effective
THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING
Summary
1. Always set goals before data collection
2. Choose quality over quantity
3. Make data accessible
4. Don’t rely on human memory
5. Determine data gaps prior to data collection
6. Don’t go at it alone
QUESTIONS?
THANK YOU!
Cartegraph empowers cities and counties to
become high-performance organizations.
UNCOVER MORE ABOUT HIGH PERFORMANCE AT
CARTEGRAPH.COM

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The 6 Common Data Collection Mistakes You May Be Making

  • 1. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING NATHAN KEBEDE, P.E. Pavement Engineer, Cartegraph
  • 2. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Outline • Introduction to asset data collection • The 6 common mistakes • Summary • Q&A
  • 3. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Infrastructure Assets: The Basics • Government manages many assets used by the public • Design, build, maintain, rehab, replace
  • 4. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Asset Data Collection: The Basics • Good asset data is needed to effectively and efficiently manage all these assets • The most important data types: • Type of asset, location, condition • Without this data, effective and efficient management of these assets is almost impossible
  • 5. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Asset Data Collection: The Benefits • Improves operational effectiveness • Boosts efficiency • Facilitates forecasting • Sharpens decision making • Results in a High-Performance team/department/organization
  • 6. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Common Methods of Asset Data Collection • Manual method—boots on the ground • Automated • Aerial imagery • Right-of-way imagery • LiDAR point-cloud
  • 7. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Common Methods of Asset Data Collection Manual • Boots on the ground • Could be done in-house • Relatively slow • Good for hard-to-reach areas Automated • Use of vehicles or planes (drones) • Aerial imagery, right-of-way imagery, LiDAR point-cloud • Relatively fast • Outsourced • Post-processing needed
  • 8. THE 6 COMMON DATA COLLECTION MISTAKES
  • 9. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #1: Failing to Set Goals • Define specifically: • What type of data should be collected? • What format should the data be in? • How will the data be used? • Set goals before starting data collection
  • 10. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #2: Choosing Quantity over Quality • Examples of quantity over quality: • Collecting signs but not identifying sign types • Collecting asset with poor geospatial data • Never compromise on quality for the sake of quantity • Implement QA/QC measures • Identify the quality level you desire for each data type collected
  • 11. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #3: Creating Data Silos • Examples that lead to data silos: • Using proprietary systems that do not easily integrate with other systems • Lack of communication between departments that leads to the same data being collected multiple times • Make data easily accessible and shared • Communicate with other departments using the same data
  • 12. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #4: Relying on Human Memory • Examples of relying on memory: • A person with 20+ years in the department has all the information • Human memory may forget details, or the person may leave • Having data updated regularly and readily available across the team is more sustainable
  • 13. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #5: Overlooking Data You Already Have • Examples of overlooking data: • Old paper records stored in filing cabinets • Data other teams may have about the asset • Do a thorough records review to avoid duplicating efforts • Identifying your data gaps leads to a more effective data collection
  • 14. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING #6: Trying to Do It Alone • Most agencies underestimate the effort it takes • There are firms that do data collection full time • Have more domain expertise • Are more efficient and effective
  • 15. THE 6 COMMON DATA COLLECTION MISTAKES YOU MAY BE MAKING Summary 1. Always set goals before data collection 2. Choose quality over quantity 3. Make data accessible 4. Don’t rely on human memory 5. Determine data gaps prior to data collection 6. Don’t go at it alone
  • 17. Cartegraph empowers cities and counties to become high-performance organizations. UNCOVER MORE ABOUT HIGH PERFORMANCE AT CARTEGRAPH.COM