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Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information<br />“Deliver more,...
Need for efficient, joined-up information services<br />Increased pressure both on Government, businesses and research com...
Power of Information<br />Living in an information/knowledge based economy where timely access to location-based informati...
Billions being spent collecting data to meet specific legislative and business requirements<br />Additional costs are bein...
Data Providers<br />Current State<br />Third Party Users<br />Common Steps involved in Accessing Data<br />Online search t...
Data Providers<br />   Future State<br />Third Party Users<br />SDI<br />Discovery, Access and View Services<br />Mobile, ...
Efforts to Improve Data Management and Sharing <br />SISE<br />i2010<br />Transformational Government<br />Lisbon Strategy...
Efforts to Improve Data Management and Sharing <br />SISE<br />i2010<br />Transformational Government<br />Lisbon Strategy...
Role of Harmonised Data Specifications<br />Many communities are developing common data specifications and adopting open w...
INSPIRE Harmonised Data Specifications<br />The overarching aim of INSPIRE is to improve the interoperability of a set of ...
Scope of INSPIRE Data Specifications<br />INSPIRE Data Specifications only define the conceptual models for core spatial (...
INSPIRE Harmonised Data Specifications<br />Harmonised Data Specifications will also define the rules for capturing and en...
But...how will this lead to more efficient, joined-up services<br />Developing harmonised conceptual schemas for modelling...
Defined by ISO 19107 – temporal schema <br />Defined by OGC Observations and Measurements<br />Defined by OGC SensorML<br />
Provides a link to a resource that describes location to which the weather observation applies<br />
Case Study: Met Office<br />Met Office currently provides ~650 meteorological products and services for public, Government...
Case Study: Met Office<br />Their legacy approach to product/service development was to develop a new data model and trans...
Case Study: Met Office<br />Using GO Publisher WFS the Met Office were capable of integrating and translating their meteor...
Case Study: INSPIRE Annex I testing – Land Registry<br />For more information about how we transformed and  published Land...
Conclusion<br />Moving towards using modular, conceptual data specifications and using open data exchange formats will ena...
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Debbie Wilson: Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information

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

  1. 1. Deliver More Efficient, Joined-Up Services through Improved Management and Sharing of Data/Information<br />“Deliver more, for less”<br />Debbie Wilson<br />Business Consultant<br />debbie.wilson@snowflakesoftware.com<br />
  2. 2. Need for efficient, joined-up information services<br />Increased pressure both on Government, businesses and research communities to “deliver more, for less”<br />2009 Budget announced that Government has to deliver an additional £5 billion on top of the £30 billion efficiency saving in 2010/2011 CSR<br /><ul><li>How can data providers and managers and service providers support their organisations to deliver efficiency savings?</li></ul>Improve access to existing data by making it more widely available<br />Make it accessible in open, self-describing formats<br />Develop harmonised data specifications that can be re-used across the business/domain community<br />Enable your data to be joined-up with other data sources<br />
  3. 3. Power of Information<br />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<br />Government data is a key component<br /> of the knowledge economy:<br />Understanding impacts on environment, health<br /> and welfare, security, transport, leisure & recreation <br />Effective evidence-based decision making<br />Share information with citizen to ensure engaged<br /> in policy-making process and can make more<br /> informed decisions<br />Provide base information which when integrated with<br />other sources can provide new “value-add” information and services<br />
  4. 4. Billions being spent collecting data to meet specific legislative and business requirements<br />Additional costs are being incurred further downstream:<br />Inefficiencies in existing data exchange processes<br />
  5. 5. Data Providers<br />Current State<br />Third Party Users<br />Common Steps involved in Accessing Data<br />Online search to find out what data already exists (e.g. Google, FOI/EIR Registers, organisation websites, thematic portals)<br />If cannot find data – create it<br />If data is available contact each data provider to: <br />Get some test data to see if its fit for purpose <br />Negotiate access to data (i.e. agree licensing T&Cs, & costs)<br />If data online, register and download data <br />If offline wait for data provider to supply data<br />On receipt of data, transform, clean andintegrate data (~25-50% project budget!)<br />Finally use it!<br />Applications access data from local datastores<br />Data (mainly held offline)<br />
  6. 6. Data Providers<br /> Future State<br />Third Party Users<br />SDI<br />Discovery, Access and View Services<br />Mobile, Online, Desktop Applications<br />User Authentication and Access Control (SSO) & Digital Rights Management<br />Discovery, Access &View Applications<br />Future Steps involved in Accessing Data<br />Online search to find out what data already exists (e.g. INSPIRE or Member States GeoPortal (or Google)<br />If cannot find data – create it (as probably doesn’t exist)<br />If data is available log-in to: <br />Evaluate data using view services <br />Download data for local use or gain access to a service to directly access the data in an application<br />Use it!<br />Harmonised Data Specifications<br />Data accessible online<br />Applications access data from remote datastores<br />Multi-Org. Data & Service Sharing Agreements<br />
  7. 7. Efforts to Improve Data Management and Sharing <br />SISE<br />i2010<br />Transformational Government<br />Lisbon Strategy<br />eGovernment<br />Information Matters Strategy<br />OGC<br />Power of Information<br />UK Location Strategy<br />W3C<br />SEIS<br />Joined-up<br />INSPIRE Directive<br />ISO 19100<br />Harmonised Data Specifications<br />Open Standards<br />Interoperability<br />Platform Independent Models<br />Semantic Web<br />Spatial Data Infrastructure<br />UML<br />Linked Data<br />Ontologies<br />Implementation Models<br />XML/XLink<br />RDF/SPARQL<br />KML<br />GML Application Schemas<br />Theasuri<br />Registers<br />Web Services<br />Vocabularies<br />Transformational WFS<br />SOAP/REST<br />WSDL<br />
  8. 8. Efforts to Improve Data Management and Sharing <br />SISE<br />i2010<br />Transformational Government<br />Lisbon Strategy<br />eGovernment<br />Information Matters Strategy<br />OGC<br />Power of Information<br />UK Location Strategy<br />Aim to improve access to data and better integrate/ join-up data <br />W3C<br />SEIS<br />Joined-up<br />INSPIRE Directive<br />ISO 19100<br />Harmonised Data Specifications<br />Open Standards<br />Interoperability<br />Platform Independent Models<br />Semantic Web<br />Spatial Data Infrastructure<br />UML<br />Linked Data<br />Ontologies<br />Implementation Models<br />XML/XLink<br />RDF/SPARQL<br />KML<br />GML Application Schemas<br />Theasuri<br />Registers<br />Web Services<br />Vocabularies<br />Transformational WFS<br />SOAP/REST<br />WSDL<br />
  9. 9. Role of Harmonised Data Specifications<br />Many communities are developing common data specifications and adopting open web service standards for sharing location-based data<br />Environment: INSPIRE Annex Themes Data Specifications<br />Aviation: Single European Sky Initiative (SESAR) – AIM and WXXM<br />Earth Systems Science: Observations and Measurements, SensorML, TransducerML<br />Meteorology and Oceanography: CSML, NCML<br />Hydrography: WaterML<br />Geotechnical and Geoenvironmental: GeoSciML, DIGGS<br />Topographic and Cadastral Mapping: ExM (Eurogeographics), OS MasterMap (GB), NAS-AAA (Germany), NEN 3610, IMRO, IMKICH, TOP10NL<br />Building and Urban Modelling:CityGML<br />
  10. 10. INSPIRE Harmonised Data Specifications<br />The overarching aim of INSPIRE is to improve the interoperability of a set of core spatial objects that underpin wide range of environmental policy<br />Article 3(7): <br />‘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.’<br />To achieve this requires common agreement of the core concepts that need to be modelled and rules for achieving interoperability<br />INSPIRE shall define harmonised conceptual data specifications for 34 themes across three Annexes<br />
  11. 11. Scope of INSPIRE Data Specifications<br />INSPIRE Data Specifications only define the conceptual models for core spatial (and temporal) object types<br />Additional non-spatial information related to the spatial-object type has been deemed out of scope<br />These object types must be defined elsewhere (e.g. Member States, domain communities or by Commission when developing new legislation – e.g. CAFE Directive)<br />INSPIRE is only the starting point for providing interoperable, joined-up data<br />
  12. 12.
  13. 13. INSPIRE Harmonised Data Specifications<br />Harmonised Data Specifications will also define the rules for capturing and encoding the various types of data to be exchanged and used<br />Rules for assigning object identifiers to objects<br />Rules for managing object lifecycles<br />Rules for cross-referencing related objects<br />Rules for types of spatial and temporal objects to be supported<br />Rules for encoding formats to be used to exchange information (i.e. XML/GML)<br />Rules for portrayal<br />Best practice for managing multi-representations<br />Best practice for data transformation and multi-lingual support<br />
  14. 14. But...how will this lead to more efficient, joined-up services<br />Developing harmonised conceptual schemas for modelling different data components and using open data exchange formats means:<br />Different information communities can be responsible for managing different object types for specific requirements<br />Where common concepts traverse several domains they can adopt the same modelling patterns<br />Data providers can exchange their data in a format that better preserves its structure and relationships<br />Allow data providers to express relationships to other data components through references to join data together <br />Conceptual model can be automatically transformed into different encoding schemas (e.g. database models, GML schemas)<br />Rapidly develop web services to exchange data with different communities and can develop new, innovative applications for end users<br />Data is self-describing enabling users (machines and humans) to immediately understand and use it<br />
  15. 15. Defined by ISO 19107 – temporal schema <br />Defined by OGC Observations and Measurements<br />Defined by OGC SensorML<br />
  16. 16. Provides a link to a resource that describes location to which the weather observation applies<br />
  17. 17. Case Study: Met Office<br />Met Office currently provides ~650 meteorological products and services for public, Government, business and research customers<br />Move away from simply delivering data to end-users to providing direct access services and decision-support applications<br />OpenRoads<br />OpenRunway<br />SafeSee<br />Their legacy systems were also struggling to meet current customer and internal business demands<br />As part of their web services infrastructure refresh they were looking for flexible solutions for quickly and efficiently developing and deploying data services<br />
  18. 18. Case Study: Met Office<br />Their legacy approach to product/service development was to develop a new data model and transformation scripts and processes for each new product/service<br />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<br />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<br />
  19. 19. Case Study: Met Office<br />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<br />GO Publisher saved Met Office hundreds <br />of developer hours which<br />were used to develop<br /> high-quality decision support<br />applications<br />Adopting model driven approach<br /> Met Office can now develop and <br />deploy new customer-focused<br /> decision-support applications within <br />months<br />Publisher <br />Desktop<br />
  20. 20. Case Study: INSPIRE Annex I testing – Land Registry<br />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<br />
  21. 21. Conclusion<br />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<br />INSPIRE provides the starting point for having more interoperable, joined up data<br />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<br />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<br />
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