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UK Data Warehouse Work

This document outlines a strategic approach for implementing a data warehouse, focusing on background analysis, cost-benefit considerations, and prototyping. Key topics include data harmonization and integration from various operational sources, methodologies for data extraction, and validation techniques. Essential recommendations include the gradual implementation of the warehouse, emphasizing integration, deduplication, and the potential inclusion of statistical processing facilities. Insights from interviews suggest that pooling data can enhance analytical capabilities across related outputs.

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UK Data Warehouse Work

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  1. UK Data Warehouse Work 23rd May 2012 Paul Tutton, Sarah Ravenhill

  2. Outline • Background • Approach • Warehouse Concepts • Prototyping & Modelling • Data Harmonisation • Recommendations and Next Steps

  3. 1. Background Other Services Data Sources Staging Operational Data Store Data Repository Data Consumers

  4. 2. Approach What are the costs and benefits? What can we put in there? How would we implement one? Does that work? Build it and see What do we want? How do we want to work?

  5. 3. What and How Define Store Interrogate Data And Metadata Input & Update Extract Find Gaps Validate Aggregate Derive

  6. 4. Build It… Integrate data from multiple sources Define a method for describing extracts Automate choice between or combination of sources Make extracts to support current and new statistics Identify gaps in extracts

  7. FAKE

  8. Source Level Indicators

  9. Variable Level Indicators Rate my data – what are we consistently suspicious of?

  10. 4. …and See • Warehouses work • Statistical processes must change • Shared Information Models are important • Think about the minimum acceptable amount of data

  11. 5. Assess Potential Harmonisation Analysis Conceptual Overlap Meaning of the Data Dataset Shape Shape of the population Statistical Activity Process surrounding the data

  12. 5. Analysis Steps List your sources Describe variables Pool the list Find the concepts Classify variables Assess results

  13. 5. Overlap findings Small numbers found Exact Replication Conceptually Close General Feasibility Combinations Otherwise Derivable

  14. 5. Example Concepts • Acquisitions/ • expenditure • Business • Operation • Business • Structure • Comments/ • Narrative • Disposals/ • Income • Employee • Count • Employment • Foreign • Investment • Hours/ • Pay • Pension • Schemes • Profit/ Loss • Statistical • Units • Stock • Taxes/ • National • Insurance • Turnover

  15. 5. Interview Findings Pooling data: May assist imputation Enables consist stories across outputs Is of more benefit for some subjects than others (e.g. employment over finance) Allows congruence checking at unit level Is more useful if it exposes timelier sources to output managers

  16. 6. Recommendations and Next Steps • Continue development of CIM • Analyse extent of process change due to movement away from survey silos • Implement a warehouse in stages: • Integrate storage first • De-duplicate and harmonise once integration is complete • Consider the addition of statistical processing facilities to reap further benefits

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