Download 10-07-Garg-900_ppt.ppt

Land Records to Land “Big Data”:
The Way Forward
Sachin Garg
Philip E. Auerswald
Schar School of Policy & Government
George Mason University
[email protected]
Motivation: Better Data Helping Land Administration
• Data is the linchpin of Land Administration
• Attempt to understand how land administration
• Creates data,
• Uses data, and
• Manages data
• Mixed Methods
• Interviews with key stakeholders, public and private
• Archival Research
• Context
• Emerging Economy (India)
• 1 State (Madhya Pradesh)
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
2
Madhya Pradesh, India
• Heart of India
• Created in 1956 by merging 5
regions
• 2nd largest by area
• 5th largest by population
• 51 districts
• Economy – largely agricultural
• 62% of Workers
• 34% of GDP
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
3
Madhya Pradesh Land Administration
• Historical
• Different revenue systems within and
across regions
• Differing survey chain lengths and
measurement unit
• Survey undertaken at different times
in the different regions
• Post-Merger
• 1959: Single Revenue Code
• 1976: Started fresh survey (metric
system, hectare, 1:4000 scale maps)
• Fresh survey started in 26 districts –
but suspended
• Commissioner of Land Records (CLR)
23-March-2017
District Level Revenue Administration
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
4
Madhya Pradesh Experience
• Data Characteristics
•
•
•
•
•
Large number of records
Record of Rights digitized “as-is”
Multiple data sources
Differing map scales between rural and urban areas
Hybrid survey method
• Challenges
• Missing maps
• Lack of physical inspection
• Uses of Data
• “Gwalior Fraud Case”
• Multiple sanctions
• “Crop Cutting Experiment”
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
5
Applying a “Big Data” Perspective
• Evidence can be mapped to the “building blocks” of “Big Data”
Large number of records
VOLUME, VARIETY
Missing maps
VERACITY
“Gwalior Fraud Case”
VERACITY
Multiple sanctions
VARIETY, VERACITY
Record of Rights digitized “as-is”
VERACITY
Lack of physical inspection
Multiple data sources
NEED VERACITY
VARIETY, VERACITY
Policy for “BigVOLUME,
Data”
Differing map scales between rural and urban areas
VARIETY
Hybrid survey method
VARIETY
“Crop Cutting Experiment”
VARIETY, VELOCITY, VERACITY
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
6
Land Records to “Big Data”: Framework Elements
Stakeholder Consultation
Incremental System Design
Fit for Purpose
Data Governance
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
7
Stakeholder Consultation
Current Practice/Challenges
“Big Data”
• Larger number of
• Records management
stakeholders
largely government driven
• Data Providers
• Stakeholders consulted as
• Data Aggregators
needed
• Data Users
• Multiple touchpoints – but
data updates single sourced • Capabilities of “big data”
not known a priori
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
8
Incremental System Design
Current Practice/Challenges
• Large monolithic systems
• System specifications
frozen
• Static System
• Resistance to change
23-March-2017
“Big Data”
• Continuously evolving
system
• Data is central
• New insights/gaps identified
→ New ways of working →
Systemic Changes
• Need to manage both data
and algorithms
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
9
Fit for Purpose
Current Practice/Challenges
• Mission Creep
• Market and Technology
forces lead to overengineering
23-March-2017
“Big Data”
• Accepts “messy data”
• If needed, fusion with
available higher fidelity
data possible
• Ability to be Adaptive
and Evolutionary
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
10
Data Governance
Current Practice/Challenges
“Big Data”
• Current Practice/Challenges
• Largely considered to be
an Information Systems
problem
• Data lifecycle not
acknowledged
• “Big Data”
• Governance of data is central
• Data Lifecycle
• Data Quality
• Data Access
• Data Provenance
• Ongoing process
• Capabilities of “big data” not known a
priori
• Regular re-assessment is needed
• Need to ensure privacy aspects
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
11
Conclusions
Data is core to Land Administration
•Land Data is quintessential “Big Data”
•Need a “Big Data” perspective for Land Data
•We provide the essential elements needed
to develop policies for land “Big Data”
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
12
Thank You
Questions?
[email protected]
23-March-2017
2017 WORLD BANK CONFERENCE ON LAND AND POVERTY
13