Improved data management and sharing through the use of harmonized data specifications and open standards can enable organizations to deliver services more efficiently with reduced costs. Specifications like INSPIRE define common modeling approaches for environmental data that allow data to be joined from different sources. Case studies show how the Met Office and Land Registry leveraged such standards to build new data services quickly and transform legacy systems. Adopting modular, model-driven approaches facilitates the rapid development and deployment of applications to meet new business and user needs.

1. 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

3. 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

4. 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

5. 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)

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

13. 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

14. 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

15. Defined by ISO 19107 – temporal schema Defined by OGC Observations and Measurements Defined by OGC SensorML

16. Provides a link to a resource that describes location to which the weather observation applies

17. 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

18. 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

19. 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

20. 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

21. 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