Debbie Wilson: Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information
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Debbie Wilson: Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information

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Debbie Wilson: Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information Debbie Wilson: Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information Presentation Transcript

  • Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information
    “Deliver more, for less”
    Debbie Wilson
    Business Consultant
    debbie.wilson@snowflakesoftware.com
  • Need for efficient, joined-up information services
    Increased pressure both on Government, businesses and research communities to “deliver more, for less”
    2009 Budget announced that Government has to deliver an additional £5 billion on top of the £30 billion efficiency saving in 2010/2011 CSR
    • How can data providers and managers and service providers support their organisations to deliver efficiency savings?
    Improve access to existing data by making it more widely available
    Make it accessible in open, self-describing formats
    Develop harmonised data specifications that can be re-used across the business/domain community
    Enable your data to be joined-up with other data sources
  • Power of Information
    Living in an information/knowledge based economy where timely access to location-based information (i.e. “on-demand”) – via wide variety of channels is essential
    Government data is a key component
    of the knowledge economy:
    Understanding impacts on environment, health
    and welfare, security, transport, leisure & recreation
    Effective evidence-based decision making
    Share information with citizen to ensure engaged
    in policy-making process and can make more
    informed decisions
    Provide base information which when integrated with
    other sources can provide new “value-add” information and services
  • Billions being spent collecting data to meet specific legislative and business requirements
    Additional costs are being incurred further downstream:
    Inefficiencies in existing data exchange processes
  • Data Providers
    Current State
    Third Party Users
    Common Steps involved in Accessing Data
    Online search to find out what data already exists (e.g. Google, FOI/EIR Registers, organisation websites, thematic portals)
    If cannot find data – create it
    If data is available contact each data provider to:
    Get some test data to see if its fit for purpose
    Negotiate access to data (i.e. agree licensing T&Cs, & costs)
    If data online, register and download data
    If offline wait for data provider to supply data
    On receipt of data, transform, clean andintegrate data (~25-50% project budget!)
    Finally use it!
    Applications access data from local datastores
    Data (mainly held offline)
  • Data Providers
    Future State
    Third Party Users
    SDI
    Discovery, Access and View Services
    Mobile, Online, Desktop Applications
    User Authentication and Access Control (SSO) & Digital Rights Management
    Discovery, Access &View Applications
    Future Steps involved in Accessing Data
    Online search to find out what data already exists (e.g. INSPIRE or Member States GeoPortal (or Google)
    If cannot find data – create it (as probably doesn’t exist)
    If data is available log-in to:
    Evaluate data using view services
    Download data for local use or gain access to a service to directly access the data in an application
    Use it!
    Harmonised Data Specifications
    Data accessible online
    Applications access data from remote datastores
    Multi-Org. Data & Service Sharing Agreements
  • Efforts to Improve Data Management and Sharing
    SISE
    i2010
    Transformational Government
    Lisbon Strategy
    eGovernment
    Information Matters Strategy
    OGC
    Power of Information
    UK Location Strategy
    W3C
    SEIS
    Joined-up
    INSPIRE Directive
    ISO 19100
    Harmonised Data Specifications
    Open Standards
    Interoperability
    Platform Independent Models
    Semantic Web
    Spatial Data Infrastructure
    UML
    Linked Data
    Ontologies
    Implementation Models
    XML/XLink
    RDF/SPARQL
    KML
    GML Application Schemas
    Theasuri
    Registers
    Web Services
    Vocabularies
    Transformational WFS
    SOAP/REST
    WSDL
  • Efforts to Improve Data Management and Sharing
    SISE
    i2010
    Transformational Government
    Lisbon Strategy
    eGovernment
    Information Matters Strategy
    OGC
    Power of Information
    UK Location Strategy
    Aim to improve access to data and better integrate/ join-up data
    W3C
    SEIS
    Joined-up
    INSPIRE Directive
    ISO 19100
    Harmonised Data Specifications
    Open Standards
    Interoperability
    Platform Independent Models
    Semantic Web
    Spatial Data Infrastructure
    UML
    Linked Data
    Ontologies
    Implementation Models
    XML/XLink
    RDF/SPARQL
    KML
    GML Application Schemas
    Theasuri
    Registers
    Web Services
    Vocabularies
    Transformational WFS
    SOAP/REST
    WSDL
  • Role of Harmonised Data Specifications
    Many communities are developing common data specifications and adopting open web service standards for sharing location-based data
    Environment: INSPIRE Annex Themes Data Specifications
    Aviation: Single European Sky Initiative (SESAR) – AIM and WXXM
    Earth Systems Science: Observations and Measurements, SensorML, TransducerML
    Meteorology and Oceanography: CSML, NCML
    Hydrography: WaterML
    Geotechnical and Geoenvironmental: GeoSciML, DIGGS
    Topographic and Cadastral Mapping: ExM (Eurogeographics), OS MasterMap (GB), NAS-AAA (Germany), NEN 3610, IMRO, IMKICH, TOP10NL
    Building and Urban Modelling:CityGML
  • INSPIRE Harmonised Data Specifications
    The overarching aim of INSPIRE is to improve the interoperability of a set of core spatial objects that underpin wide range of environmental policy
    Article 3(7):
    ‘interoperability’ means the possibility for spatial data sets to be combined, and for services to interact, without repetitive manual intervention, in such a way that the result is coherent and the added value of the data sets and services is enhanced.’
    To achieve this requires common agreement of the core concepts that need to be modelled and rules for achieving interoperability
    INSPIRE shall define harmonised conceptual data specifications for 34 themes across three Annexes
  • Scope of INSPIRE Data Specifications
    INSPIRE Data Specifications only define the conceptual models for core spatial (and temporal) object types
    Additional non-spatial information related to the spatial-object type has been deemed out of scope
    These object types must be defined elsewhere (e.g. Member States, domain communities or by Commission when developing new legislation – e.g. CAFE Directive)
    INSPIRE is only the starting point for providing interoperable, joined-up data
  • INSPIRE Harmonised Data Specifications
    Harmonised Data Specifications will also define the rules for capturing and encoding the various types of data to be exchanged and used
    Rules for assigning object identifiers to objects
    Rules for managing object lifecycles
    Rules for cross-referencing related objects
    Rules for types of spatial and temporal objects to be supported
    Rules for encoding formats to be used to exchange information (i.e. XML/GML)
    Rules for portrayal
    Best practice for managing multi-representations
    Best practice for data transformation and multi-lingual support
  • But...how will this lead to more efficient, joined-up services
    Developing harmonised conceptual schemas for modelling different data components and using open data exchange formats means:
    Different information communities can be responsible for managing different object types for specific requirements
    Where common concepts traverse several domains they can adopt the same modelling patterns
    Data providers can exchange their data in a format that better preserves its structure and relationships
    Allow data providers to express relationships to other data components through references to join data together
    Conceptual model can be automatically transformed into different encoding schemas (e.g. database models, GML schemas)
    Rapidly develop web services to exchange data with different communities and can develop new, innovative applications for end users
    Data is self-describing enabling users (machines and humans) to immediately understand and use it
  • Defined by ISO 19107 – temporal schema
    Defined by OGC Observations and Measurements
    Defined by OGC SensorML
  • Provides a link to a resource that describes location to which the weather observation applies
  • Case Study: Met Office
    Met Office currently provides ~650 meteorological products and services for public, Government, business and research customers
    Move away from simply delivering data to end-users to providing direct access services and decision-support applications
    OpenRoads
    OpenRunway
    SafeSee
    Their legacy systems were also struggling to meet current customer and internal business demands
    As part of their web services infrastructure refresh they were looking for flexible solutions for quickly and efficiently developing and deploying data services
  • Case Study: Met Office
    Their legacy approach to product/service development was to develop a new data model and transformation scripts and processes for each new product/service
    They are moving towards a model driven approach to product development based on a core set of conceptual models for different components of a forecast, nowcastor time-series observation dataset
    Application specific schema for different services can rapidly developed by combining or extending generic model components together which can then be deployed as web services
  • Case Study: Met Office
    Using GO Publisher WFS the Met Office were capable of integrating and translating their meteorological data on-the-fly to develop new web services which was deployed within a week of defining requirements for a new service and application
    GO Publisher saved Met Office hundreds
    of developer hours which
    were used to develop
    high-quality decision support
    applications
    Adopting model driven approach
    Met Office can now develop and
    deploy new customer-focused
    decision-support applications within
    months
    Publisher
    Desktop
  • Case Study: INSPIRE Annex I testing – Land Registry
    For more information about how we transformed and published Land Registry data to comply with INSPIRE Implementing Rules go to http://www.snowflakesoftware.com/tv/gpinspire/index.htm
  • Conclusion
    Moving towards using modular, conceptual data specifications and using open data exchange formats will enable organisations to move from simply moving data around to providing on-demand, real-time services which can be consumed simultaneously through multiple channels
    INSPIRE provides the starting point for having more interoperable, joined up data
    More needs to be done within information communities to ensure that we model the related “business” information so that we can integrate all our data
    If we do achieve this we will end up in a situation where users will be able to discover and access and use a wide range of information more efficiently – but it does require us to change how we have been managing our data