Welcome to this Explanatory Pack, designed to help local authority officers introduce the importance

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EXPLANATORY PACK OVERVIEW AND GUIDANCE

• Welcome to this Explanatory Pack, designed to
  help local authority officers introduce the
  importance of data quality to other members of
  the organisation, such as Heads of Service or
  Directors
• This is very much intended as a guidance document
  please feel free to amend it as necessary to
  suit your particular circumstances and focus
  note that you may wish to delete or hide this
  slide when using this presentation
• It is divided into three main parts
• Introduction
• The importance of having good data quality
• How you can improve your data quality
• Speaker notes have been in this presentation
  please read these for further guidance when
  presenting the relevant slides
• If you have any questions or queries, please
  contact Mohammed Naqvi at Tribal on
  mohammed.naqvi_at_tribalgroup.co.uk or 020 7323 7132

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Improving our data quality
Your authoritys / teams name

• June 2009

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Introduction
The aims of this presentation are threefold
To (re-)introduce the concept of data quality
To highlight the importance of having good data
quality
To provide you with prompts to improve your data
quality
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Definitions
What do we mean by data?
Data
Data quality

• Data quality management

• An item of record at the lowest level of
  abstraction used to represent facts about events
  or objects
• Within local authorities this includes records
  about People, Businesses, Properties and Council
  Operations, such as calls to a contact centre,
  bins collected, meals delivered etc

• The quality of an item or set of data as measured
  against six characteristics Completeness,
  Accuracy, Validity, Timeliness, Reliability and
  Relevance

• The way in which you manage your data quality to
  ensure that it is of the most appropriate
  standard, that staff are following correct
  processes and there is continuous improvement

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Cost of poor data quality
Add in specific examples to your authority if you
know them
Poor data quality costs money and adversely
affects performance
Examples

• HM Revenues Customs (HMRC) writing off 1
  billion of overpaid tax credits since introducing
  the system in 2003

• Department for Work and Pensions overpaying 73
  million in benefits to families of deceased
  individuals in 2008

Financial cost

• Disputes between Councils and ONS over local
  demographics impacting grant funding including
  Manchester City Council received over 100m of
  additional funding over 10 years following a
  correction to the ONS population statistics

• In 2008, Data Connects found that improving data
  quality through a Customer Data Integration
  solution could result in real benefits such as
  saving nearly 750,000 per year through
  collecting more parking fines in a London borough

• Cases such as Baby P where the state was unable
  to join up the information it had effectively
  enough to protect the child this is the same
  issue that came to light in the Soham, Victoria
  Climbie and Child B cases

Service cost

• Local incidents such as blue badges being
  delivered to incorrect addresses, wrong people
  being chased for council tax or staff not being
  paid the correct amount

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Local Context
What is our current approach to data quality?

• Example questions for you to answer for this
  slide
• How is data quality managed in your local
  authority at the moment, if at all? For example,
  is it managed at a corporate or service area
  level?
• Is data quality a priority within your authority?
  Are staff incentivised to improve it?
• How is data quality monitored in your authority?
• Are staff aware of its importance to the
  organisation?
• You may wish to use the framework (see subsequent
  slides) to consider the different aspects of how
  your authority/team manages data

Dummy content
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How can we improve?
What can be done to address poor data quality?
Ensuring good data quality should be a priority
for local authorities and their service areas to
reduce costs and improve performance
Improving data quality effectively involves
balancing costs, risks and benefits, and
prioritising areas for action - for example,
there is a greater need for higher data quality
in Childrens Services than in Libraries as the
impact of poor data quality will be higher
Data Connects, a group of authorities that
recognise the strategic importance of data
quality, has developed a Data Quality Management
(DQM) Framework to help local authorities develop
and define their approach (please see following
slides)
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How can we improve?
Data Quality Management Framework - Explanation

• The DQM Framework is intended as an best practice
  guidance for local authorities aiming to improve
  their data quality

Governance Objectives
This relates to planning our approach
This relates to hard improvements that can be
applied to data over the course of its use, from
input to disposal
Data Life Cycle Measures
Culture
This involves improving awareness of the
importance of data quality and changing attitudes
and behaviours
Monitoring
This involves measuring data quality and taking
action to ensure its continuous improvement

• Each theme contains components which detail
  activities that can be carried out to achieve
  improvement in that theme please see the next
  slides for
• An overview of the framework
• An example of a component
• An index of all the components
• The following 5 slides are example content from
  the Framework

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Introduction
Instructions for how to use the Framework
Data Quality Management Framework Overview
Data Quality Management Themes
Data Quality Management Components
Example slide
Clicking on a theme in the Framework takes you
to a detailed page on that Theme
Clicking on a Component in the Theme takes you
to a detailed page on that Component
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Framework overview
Data Life Cycle Measures
Data Verification Measures
Data Input Measures
Monitoring Procedures
Technology Services
Governance Objectives
Reporting against your approach
Ensuring recommendations are implemented
Understanding the current situation
Planning your approach to data quality
Data Disposal Measures
Use of Data Measures
Example slide
Improving Awareness
Building Skills
Defining Roles
Culture
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Framework overview
Governance Objectives

• Governance Objectives refers to the
  organisational structures, procedures and
  documentation that determine and steer the
  organisations efforts towards the right level of
  data quality for the business context
• The Governance Objectives process begins with
  understanding the current situation with regards
  to data quality this knowledge is then used to
  plan the authoritys approach towards improving
  the situation
• A key element of this strand is risk management
  data quality needs to be managed in proportion to
  the associated risk
• Please click on the links below to discover more
  about this process and the activities and
  documents that you can use to improve this theme
  of data quality management

Example slide
Understanding the current situation
Planning your approach
Reviewing national initiatives standards
Reviewing existing business objectives
Producing a Service area data quality policies
Producing a Corporate DQ policy
Producing Team DQ policies
Producing a DQ improvement plan
Conducting a corporate DQ risk assessment
Conducting a service area DQ risk assessment
Self-assessing your situation using a toolkit
Review data quality definitions and objectives
Producing a DQ organisational chart
Developing a communications plan
Conducting an annual data quality management
audit (see Monitoring Procedures)
Quantifying the cost of poor data quality
Monitoring Procedures
Culture
Data Life Cycle Measures
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Example component
Reviewing national initiatives and standards

• What are they?
• National initiatives and standards are documents
  and schemes set out by central government
  agencies relating to data quality
• They include the Tell Us Once Campaign,
  ContactPoint and the Audit Commission guidance
  and assessment
• Authorities must keep up to date with these
  initiatives to ensure that they understand and
  meet their requirements
• Why are they important?
• Authorities may be judged in terms of how well
  they comply with such initiatives (for example
  the Audit Commissions Data Quality Inspection
  influences an authoritys Use of Resources
  assessment)
• Understanding the reasons behind and the
  requirements for these initiatives can be a good
  way to build up an appreciation of the importance
  of Data Quality for the authority
• Who is responsible for this?
• The corporate data quality manager should
  understand everything that is applicable
• The service area data quality managers should
  understand those that are relevant to them.

Description
Example slide
Insert relevant examples or documents as objects
below

• Advice and tips
• We would recommend
• Talking to service areas about the major external
  returns they need to make
• Documenting your conversations in a short report
  this can be used to inform the corporate data
  quality policy
• This should be a regular process that you
  undertake annually, perhaps as part of monitoring
  your data quality improvement plan, so that you
  are on top of what is on the horizon

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Index of components
COMPONENTS MAP
Reviewing national initiatives standards
Reviewing existing business objectives
Producing service area data quality policies
Producing a corporate DQ policy
Producing a DQ improvement plan
Conducting a corporate DQ risk assessment
Producing team DQ policies
Conducting a service area DQ risk assessment
Self-assessing your situation using a toolkit
Defining data quality
Producing DQ organisational chart
Ensuring new starter DQ training happens
Ensuring system users receive DQ training
Raising awareness of senior managers
Developing a communications plan
Assigning a DQ Board
Assigning a senior DQ sponsor
Example slide
Assigning a corporate DQ manager
Assigning service area DQ managers
Assigning DQ Champions
Implementing sanctions incentives
Creating a DQ intranet page
Holding data quality best practice forums
Confirming officer responsibilities
Holding regular DQ roadshows
Distributing corporate DQ newsletters
Distributing DQ reference packs
Conducting a resource capacity assessment
Distributing service area DQ newsletters
Collecting data on a timely basis
Using autocomplete technology
Preventing duplicate entry
Using consistent field standards
Using standard templates
Using data validation where possible
Validating data against master data sources
Conducting sampling exercises
Merging duplicates
Assigning reviewing confidence estimates
Maintaining a third party data policy
Applying sourcing referencing standards
Applying a customer data integration solution
Enriching data
Maintaining a data security policy
Conducting regular data cleansing activity
Applying archiving standards
Maintaining an audit trail
Generating corporate data quality reports
Generating service area data quality reports
Generating team data quality reports
Conducting an annual data quality management audit
Conducting regular key systems integrity checks
Testing data quality
Implementing quality processes for data quality
Maintaining a corporate data quality risk
register
Maintaining service area risk registers
Ensuring partnership protocols are followed
Monitoring the DQ improvement plan
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How can we improve?
How can we use this Framework to improve our data
quality?

• Example content to include on this slide
• Improving data quality requires investment, for
  example in employing data quality managers or in
  buying data quality improvement technology like
  Customer Data Integration solutions
• This Framework can be used as a guide for
  directing this investment to the right places,
  and as a model for what good data quality
  management looks like
• It is a starting point for data quality
  management and we can adapt it as necessary to
  suit our situation, focusing on certain service
  areas or datasets as we see fit

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How can we improve?
What are we going to do next?

• Example content to include on this slide
• Possible actions that we could take
• Do nothing continue to lose money and risk poor
  service performance
• Use technology to increase the quality of our
  data now this could be expensive but may
  provide a positive return-on-investment over time
  
• Set up a corporate data quality team this team
  would have explicit responsibility for improving
  our data quality and could drive things forward
• Employ the measures outlined in the Framework to
  improve staff awareness these kind of actions
  encourage staff to get better at managing data
  over the long-term

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Contact Details
For further information on Data Quality please
contact

• Joe Bloggs
• London Borough of X
• Joe.bloggs_at_londonboroughofx.gov.uk
• 020 7XXX XXX
• If you would like to find out more about Data
  Connects, please contact
• Tony Ellis, Chair of Data Connects
• Tony.Ellis_at_brent.gov.uk
• 020 8937 1400