improving the quality of psi. steven ramage epsiplus thematic meeting, london 15 th july 2007...

Improving the quality of PSI

Steven Ramage

ePSIplus Thematic Meeting, London

15th July [email protected]

Data Quality

• Introduction

• Focus on spatial data quality

• Aspects of spatial data integrity

• Ideas to consider

• Conclusion

AGENDA

“The first step is for government bodies themselves to know what information they have and to organise it in such a way that the information can be easily discovered and retrieved.”

The second step should be to determine the state of the information and its fitness for use, i.e. the data quality elements!

ePSIplus Update No 2, May 2007,

Enabling PSI re-use: the Need for Standards

“We have witnessed in the UK, restrictive data practices due to poor record keeping, data in no workable format and of exceptional poor quality.” http://www.epsiplus.net/epsiplus/forum__1/epsiplus_forum/is_germany_losing_out

Experiences similar with spatial data quality.

This highlights a major cultural issue.

ePSIplus ForumMonday 02 July 2007

Quality Data? Christian Lister

“Thank you for your email. The consultation was supportive of the Commission's proposestandards. We are making some adjustments in response to the comments received, but the overall tone and content will remain the same. We will not be focusing specifically on spatial data,but on data in a more generalsense.”

Senior Manager, Audit Policy & Practice, The Audit Commission, July 2007

Audit Commission Consultation on Data Quality, April 2007

Data Quality

• Define data quality

• Is it fit for purpose?

• Define stakeholders

• Why be concerned?

INTRODUCTION

'Re-use' is defined as the use of information

held by a public sector information holder 'for

a purpose other than the initial purpose within

that public sector body's public task for which

the document was produced' (The Re-use of

Public Sector Information Regulations 2005,

SI 2005 No.1515, 4(1), 'the Re-use

Regulations').

Data Quality

• Determine what already exists

• Very timely ITT/call for tender from European Commission

“The assessment of the reuse of public sector information (PSI) in the geographical (cartographic-mapping and cadastral) information, meteorological information and legal information sectors.”

Data Quality

• Awareness and definitions

• Input, verification, output

• Systems and presentation

INTRODUCTION

Data Quality Management Activities

• Data Augmentation – Enhance data using information from internal/external data sources

• Data Integration – Match, merge or link data from a variety of disparate sources

INTRODUCTION

Data Quality Management Activities

• Data Profiling – Inspect for errors, inconsistencies, redundancy and incomplete information

• Data Validation – Correct, standardise and verify data

• Data Monitoring – Check and control data integrity over time

INTRODUCTION

Data Quality - vision• Audit Commission Data Quality Consultation, Section 8

“Producing data that is fit for purpose should not be an end in itself, but an integral part of an organisation’s operational, performance management, and governance arrangements. Organisations that put data quality at the heart of their performance management systems are most likely to be actively managing data in all aspects of their day-to-day business, in a way that is proportionate to the cost of collection, and turning the data into reliable information.”Audit Commission consultation:

Improving Information to Support Decision Making: Standards for Better Quality Data, 15 Mar 2007

INTRODUCTION

Data Quality – risk management

• Audit Commission Data Quality Consultation, Section 10

“The risk in not identifying and addressing weaknesses in data quality, or the arrangements that underpin data collection and reporting activities, is that information may be misleading, decision making may be flawed, resources may be wasted, poor services may not be improved, and policy may be ill-founded. There is also a danger that good performance may not be recognised and rewarded.”Audit Commission consultation:


INTRODUCTION

Data Quality – accountability (1)


“Public bodies are accountable for the public money they spend: they must manage competing claims on resources to meet the needs of the communities they serve, and plan for the future. The financial and performance information they use to account for their activities, both internally and externally, to their users, partners, commissioners, government departments and regulators, must be accurate, reliable and timely.”Audit Commission consultation:


INTRODUCTION

Data Quality – accountability (2)• Audit Commission Data Quality Consultation, Section 9

“Ultimate responsibility for ensuring that data is fit for purpose can only rest with public bodies themselves. This responsibility should not be confused with the role of government departments in setting a policy framework, including defining national performance measures and issuing standards and guidelines, or the role of regulators in providing assurance and identifying improvements.”Improving Information to Support Decision Making: Standards for Better Quality Data, 15 Mar 2007

INTRODUCTION

Data Quality – fitness for purpose


“There are many audiences for the data collected by public services. This in itself can cause problems with the reliability of reported information, because the need to aggregate and analyse raw data in a variety of ways to suit a variety of purposes (Table 1) may not be understood by all those involved in the data collection and reporting processes. Data collected for a specific local purpose may ultimately be used or reported in ways not envisaged, intended or understood by its originators.”Audit Commission consultation:


INTRODUCTION

Spatial Data in Public Sector

• PIRA report 2000, Euros 36 billion

• 40% of public sector data is geographic

• Replacement cost today, Euros 100+ billion

• Everything happens somewhere

• Public Sector Information reuse essential

FOCUS AREA

Before After


Consider, as an illustration, a land management/property registration application used to record ownership rights. In such a system, the business rules concerning the spatial data are well understood: every piece of land (parcel) has ownersland parcels do not overlap land parcels do not have gaps between them.

Generic Concepts Once rules are adopted it becomes possible to monitor them and to quantify the impact of drift. Importantly, rules state the formal set of conditions that should be met before data can be said to be fit for purpose.

FOCUS AREA


• Spatial data used for decisions relating to education, health, land and property, roads and networks, etc.

• Issues associated with updating the real world, changes in technology and organisational change

• Accurate data for accurate decisions

SPATIAL DATA INTEGRITY


• There are 410 LAs in England and Wales – at the County, or District/Borough levels (Unitary Authorities are responsible for the duties of both). Together they employ over two million people and each authority undertakes an estimated 700 different functions.2 Examples of the types of raw information held by LAs include policy and strategy documents,details of services, annual reports, budget plans, statistics, public consultations, meeting minutes, performance data, and the

location of council buildings, land and other assets http://www.oft.gov.uk/shared_oft/reports/consumer_protection/oft861e.pdf



• Business issues –

• cannot share data or sharing poor data

• data must be recaptured frequently

• cleansed frequently

• high offshore and maintenance costs

• analysis of inaccurate information



• Technical issues –

• cannot load data into existing software or database systems

• once loaded errors can prevent electronic processing

• manual intervention takes much longer, diverts resources and is subject to visual inspection only


Data Quality

• Independent verification important

• use tools or services from 3rd parties

• Quantitative measures necessary

• have a baseline to review

• set the target level

• monitor progress

• build into planning


• Work according to organisational Vision

• Strategic Assessment up front

• Data Audits within Data Management Plan

• Use Information Strategy as guideline

• Decisions based on quantifiable and measurable results; iterative process

• Future proof or sustain data quality

CONSIDERATIONS


• Is it quality assessed or measured?

• Do you or can you share spatial data?

• Can you integrate standard business information with spatial data?

• How much has been invested in those spatial data? What is the required return on that investment?

CONSIDERATIONS


• INSPIRE (Infrastructure for Spatial Information in Europe)

• European Spatial Data Infrastructure

• Impacts public sector in Europe

• Limited budget allocated to GI projects in eContentplus programme

KEY LEGISLATION


• FOT ID, Denmark

• AAA, Germany

• IntesaGIS, Italy

• RGI, Netherlands

• SPIRE, England and Wales

INITIATIVES ACROSS EUROPE


• AdV, Germany

• City of Amsterdam, Netherlands

• City of Oslo, Norway

• East Sussex County Council, England

• Environment Agency, England&Wales

CASE STUDIES


• IGN Belgium

• IGN France

• KMS Denmark

• London Borough of Enfield, England

• Ordnance Survey of Northern Ireland

• Transport for London

CASE STUDIES


“It boils down to us being able to satisfy our customer's need for high quality data, and in the same time being able to deliver and link data conforming national standards (andtherefore being able to re-use public service information).”

CASE STUDIES – City of Amsterdam


A government that doesn’t ask the same thing twice:

• is customer oriented

• cannot be misled

• knows its facts

• doesn’t spend more than necessaryMust have access to reliable and high quality data


PERSONS

GEMEENTELIJKE BASISADMINISTRATIE

Businesses

BASIS BEDRIJVENREGISTER

Civil Registry Office

Tax department

ADDRESSES

BASISREGISTRATIEADRESSEN

RESIDENCE-UNITSBUILDINGS

BASIS GEBOUWENREGISTER

Large scale mapsSmall scale maps

Aerial photographs

GEOGRAFISCH KERNBESTAND

PARCELS

KADASTRALEREGISTRATIE

Geo en Vastgoedinformatie



“It boils down to us being able to satisfy our customer's need for high quality data, and in the same time being able to deliver and link data conforming national standards (andtherefore being able to re-use public service information).”



• Automated model generalisation for 7 German States

• Demonstrated significant savings through a fully automated workflow without manual edits

• Spatial data quality played a major role in the success of this large project

• Changing data models across Europe their current data against that model.

CASE STUDIES – AdV germany

Spatial Data in Public Sector• Departmental data is usually ‘fit-for-purpose’ within its own setting. Combining data from different sources is often the first indication of inconsistencies.•There was no mechanism for testing ‘fit-for-purpose’ of the combined data. Now using Radius Studio. •The overheads in correcting combined data (particularly when the original data is fit for its original intended purpose) discourage official data sharing.• Data can be created at different scales/resolutions but expected to work together. Often the end-user has little understanding of the ‘accuracy’ of the data – should we expect the end user to know this?

CASE STUDIES – OSNI GeoHub


• Improving spatial data quality to enable PSI re-use

• Automation key to success

• Applicable across local, regional/central and national government

• Rationalising the supply chain

CASE STUDY LESSONS


Points to address:

• Fragmentation of datasets and sources

• Gaps in spatial data availability

• Diverse collection and preservation practices

• And lack of harmonisation between datasets at different geographical scales

CASE STUDY LESSONS


Aggregate ‘core’ geographic information across borders to guarantee its interoperability for seamless data integration.

• Easily accessible and re-useable datasets

• Use open, non-proprietary standards (where appropriate)

• Assessments of pricing models and their effects on re-use

• Consider multilingual access

• Provide performance indicators (specific, realistic, measurable)

• Involve relevant stakeholders (including co-ordinating bodies)

CASE STUDY LESSONS

The open sharing of information and monitoring of performance are key to the success of Local Partnerships, LAAs and LSPs.

“Now for the very first time, we are going to be talking the same language but also talking about it at the same time. I believe that data and that accuracy of data will provide us with the power we never had but also provide us with much more confidence that the activities we are suggesting as the way forward are the right activities” Dr Angela Lennox, Chair, Leicester Partnership

TESTIMONIALS

STANDARDS

• Data Quality Working Group

• Approved December 2006

• Build on ISO 1911n

• Standard way of describing and communicating spatial data quality

• Chaired by 1Spatial

“Ascertain what organisations involved in the market place understand and mean when using the term spatial data quality. The WG will attempt to define a framework and a grammar for the certification and communication of spatial data quality. This method to describe and communicate data quality measures will reference, but not be limited by, a number of categories such as completeness, accuracy, scale, consistency and validity. Reference shall be made to the standards defined in ISO 19113, 19114, and 19138 when published. “ DQ WG Charter

STANDARDS

• "Prediction is very difficult, especially if it's about the future."

-- Nils Bohr, Nobel laureate in Physics

Steven Ramage

ePSIplus Thematic Meeting, London

15th July [email protected]

improving the quality of psi. steven ramage epsiplus thematic meeting, london 15 th july 2007...

Documents

link data

data data monitoring

data quality awareness

data quality elements

control data integrity

restrictive data practices

q uality data

quality of psi slide