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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