New Approaches to Structuring Data and Metadata in Statistical Systems Implications for Usability and Functionality

1. New Approaches to Structuring Data and Metadata in Statistical SystemsImplications for Usability and Functionality Simon Musgrave, University of EssexRSS/ASC January 2004 Simon Musgrave RSS/ASC

2. User Scenarios • We begin by painting three potential user scenarios • Information Analyst Workspace • Policy Maker Workpage • Market Research Client Page Simon Musgrave RSS/ASC

3. Information Analyst Workspace • We would like an active workspace that dynamically brings together all pertinent information for alerts and review • Workpage sorts, merges and describes multiple heterogeneous information sources • e.g. Monitoring the local public health issues links to • latest Hospital Episode Statistics data • Health Survey for England data • NHS Direct statistics • local surveys • key events • previous reports • contextual information Simon Musgrave RSS/ASC

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5. Policy Maker Workspace • Latest performance measures for hospital trusts released. • Policy maker wants to understand the variability, comparisons with previous years and other regions, breakdown of component parts etc. • Ideally system will treat the number as a signpost to these lower levels of data so that • Underlying tables can be shown? • Displayed with measures of uncertainty • Ranked next to comparative areas • Expanded (if permitted) to detailed administrative data • Link to content management system via metadata etc. Simon Musgrave RSS/ASC

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7. Market Research Client Page • Dedicated page for client • Typically links to reports, surveys, analyses • Ideally are pages that contain all active links to company performance and available competitor information • Easy new analyses • Background information • Real-time market information Simon Musgrave RSS/ASC

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9. User Levels • Regardless of usage, we also have to accommodate different user competencies and expectations • Expert – professional analysts • Clerical • Executive • Press • Customers’ customers • Ignorant • Workspace should be tailored to usability criteria of end user Simon Musgrave RSS/ASC

10. Usability • Learnability: How easy is it for users to accomplish basic tasks the first time they encounter the design? • Efficiency: Once users have learned the design, how quickly can they perform tasks? • Memorability: When users return to the design after a period of not using it, how easily can they reestablish proficiency? • Errors: How many errors do users make, how severe are these errors, and how easily can they recover from the errors? • Satisfaction: How pleasant is it to use the design? Nielsen (2003) Simon Musgrave RSS/ASC

11. Are the statistical systems? Usefulness   Usability   Simon Musgrave RSS/ASC 

12. Entry Points • Finding • Browsing (tree, registry, file system) • Searching (google, keywords, metadata, thesaurus) • Linking • Shallow • Deep Simon Musgrave RSS/ASC

13. Functionality • Given the growing demand for all types of data, • from advanced statistical systems • to easy access to performance measurements • from all types of users • How can we build systems that • Handle a variety of data types • Indicators • Tables • Counts • Surveys • avoid disclosure risks (real or theoretical) Simon Musgrave RSS/ASC

14. And link seamlessly with both e-GIF and a potential data spine • All of these broad use cases demand joined up data ‘We would all love to do data linkage’ How do we model and build systems that provide for interoperability and at what level? All of this demands statistical metadata, which is ……. Simon Musgrave RSS/ASC

15. Statistical Metadata is anything that you need to know to make proper and correct use of the real data in terms of: capturing, reading, processing, interpreting, analysing and presenting the information Thus, metadata includes (but is not limited to) population definitions, sample designs, file descriptions and database schemas, codebooks and classification structures, fieldwork reports and notes, processing details, checks, transformation, weighting conceptual motivations, table designs and layouts (Westlake 2003) Definitions Simon Musgrave RSS/ASC

16. Or statistical metadata “… are relevant in the areas · definition of statistical concepts; · modelling of data and processes; · storage structures and transfer protocols; · standards to ensure a uniform and co-ordinated approach; · information about availability, location, meaning, quality and use of data.” (Kent and Schuerhoff 1997) Simon Musgrave RSS/ASC

17. Alternative Views • Typically our understanding of data and metadata systems reflect our own priorities and goals, which may have a creation, storage or usage bias • Within the recent EC Metanet project Grossman has defined the United Metadata Architecture for Statistics (UMAS) which seeks to ‘Define a framework to understand communalities and differences of Data / Metadata Models from a statistical point of view, irrespective of the terminology and goals of the specific models’. • He suggest 4 views • Conceptual Category View (Conceptual model) • Statistical View (Role of the category within the statistical ontology) • Data Management View (Access and Manipulation of Category Instance Data) • Administration View (Management and bookkeeping of the structures) Simon Musgrave RSS/ASC

18. Model Elements • Concepts – what is is we are describing, and so a link to non-statistical systems, vital for our integrated workspace • Semantics – understanding the meaning of both concepts and elements within the data model • Methods – what we can do with the data • Structure – how the underlying data is organised Simon Musgrave RSS/ASC

19. Simplified microdata model production method obtained through structural relationships refers to carries statistical population dataset Descriptive and technical info Based on Defined by contains statistical unit numeric information variables Simon Musgrave RSS/ASC Grossman 2003

20. Levels of interoperability • Descriptive information (e-GMS) • File exchange (data dictionary) • Dataset exchange (archive standards) • Information exchange between systems (data warehouse) • Application accessibility (Web services) Simon Musgrave RSS/ASC

21. Some standards • The Common Warehouse Metamodel (CWM) from OMG – a model and syntax for the exchange of metadata for data warehousing and business intelligence • ISO 11179 – a universal standard for describing data elements in a metadata repository • SPSS MR Data Model – an interface layer • GESMES and SDMX – a metadata model for the exchange of multidimensional data and time-series. • IQML, AskXML and Triple-S - metadata for the exchange of questionnaire and survey data • The Data Documentation Initiative (DDI) – a general metadata standard for statistical data (micro as well as aggregated) Simon Musgrave RSS/ASC

22. Challenge • Understand the scope of our ambitions • Are we building a simple interoperable environment within one organisation? • Are we seeking to link our information into a wider ‘data web’? • The technology (e.g. web services) offers massive potential – which moves away from our ability to organise to exploit it • Can we make systems that work, that are useful and highly usable? Simon Musgrave RSS/ASC