January 2004Simon Musgrave RSS/ASC New Approaches to Structuring Data and Metadata in Statistical Systems Implications for Usability and Functionality.

January 2004 Simon Musgrave RSS/ASC

New Approaches to Structuring Data and Metadata in Statistical SystemsImplications for Usability and

Functionality

Simon Musgrave, University of EssexRSS/ASC January 2004


User Scenarios

• We begin by painting three potential user scenarios– Information Analyst Workspace – Policy Maker Workpage– Market Research Client Page


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



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.



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


example


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


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)


Are the statistical systems?

Usefulness

Usability


Entry Points

• Finding– Browsing (tree, registry, file system)– Searching (google, keywords, metadata,

thesaurus)

• Linking– Shallow– Deep


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)


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 …….


Definitions

• 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)


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)


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 1. Conceptual Category View (Conceptual model)2. Statistical View (Role of the category within the statistical ontology)3. Data Management View (Access and Manipulation of Category Instance

Data)4. Administration View (Management and bookkeeping of the structures)


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


Simplified microdata modelproduction

method

dataset

Descriptive and technical info

structuralrelationships

variables

statistical population

numericinformation

statistical unit

carries

obtained through

refers to

Def

ined

by

contains

Based on

Grossman 2003


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)


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)


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?

January 2004Simon Musgrave RSS/ASC New Approaches to Structuring Data and Metadata in Statistical Systems Implications for Usability and Functionality.

Documents

simon musgrave rssasc

simon musgrave rssasc

functionality simon

types of data

theoretical slide

real data

structuring data

understanding of data