5. Specific issues

5.1 Address structures

The address structures used by the various Acacia partners differ. This has led to problems matching addresses, as found in the Acacia Pilot Project trials. Essentially there are two different address structures:

• postal addresses - as defined by Royal Mail for locations receiving mail deliveries, and described in 2.3,
• geographic addresses - for a wider set of geographic objects, as defined in BS 7666 Part 3.

In practice, most of the Acacia partners use a hybrid to enable both types of address to be catered for.

The periodic review of BS 7666 is taking place this year. It is important that the Acacia partners are involved in this. This may be at the level of the Strategy Group (which is made up of the major stakeholders, who are being asked to provide sponsorship), or at the User Review Group, who will be asked to review all proposed changes.

5.2 Problem addresses

Although the rules for creating postal addresses, are well established, and guidelines have been produced for address creation for the NLPG, there are particular areas that regularly generate problems in all address datasets. These include the following:

• unnamed streets and unnumbered properties
• multi-occupancy premises
• occupant names used to identify premises
• objects without postal addresses

Unnamed streets and unnumbered properties are a particular problem in rural areas, where some properties may be identified by a name and a locality. Whilst these may be well-known locally, they are often insufficient to exactly locate the property in wider applications, and in particular the delivery of services.

Multiple-occupancy premises include flats, hostels and shared business premises. They are often only addressed to the higher level unit. Even where they are individually addressed, this is usually done in an inconsistent manner, particularly in the way that the individual unit is referenced, for example "first floor flat". This is the subject of a separate investigation [Ref 6].

Occupant names are often used to identify properties, particularly for commercial premises, for example "Dillons". This creates problems in two ways. Firstly, the name is often not the exact name, and therefore can appear in different forms in different places (e.g. "Dillons Newsagent"). Secondly, the name can be changed without any of the addressing organisations being informed. Thirdly, the business may move to a new site, sometimes in the same street, taking the name with it. In this case, it will not be possible to differentiate between the two properties from the address.

Objects without postal addresses are a general source of addressing problems. There is inconsistency in the way of identifying them and describing them (the object name). Typical examples are the classification (e.g. hall/village hall/community hall) and the description of the location (e.g. "behind the Kings Arms").

For all of these types of problem addresses, definitive guidelines are required, covering the classification, description and location, including template examples.

5.3 Data management good practice

5.3.1 The need for data management

There is a range of activities concerned with the handling of data in any organisation. These will be implemented to varying degrees in different organisations. In general there is little awareness and commitment at senior levels of organisations of the importance of implementing good data management practises. The benefits from good data management include the following:

• increased confidence and trust in the data;
• increased use of data;
• improved quality and provision of timely information, fit for purpose within the business context;
• increased understanding of data and its use;
• improved business processes and resource allocation;
• better control over data access;
• more realistic charging becomes possible;
• there is scope to rationalise certain datasets and develop a one-stop shop approach to improve value for money and enhance stakeholder reputation.

5.3.2 Data management activities

Fundamental to the establishment of good data management within an organisation is the creation of a Data Policy or Data Strategy (Corporate Information Strategy). This provides a high level set of guiding principles and a plan for implementing and maintaining data management within the organisation. It establishes the framework within which data management will be carried out. As a minimum it should define the following:

• Data documentation for all datasets –metadata with a full description of the datasets, including definitions and source;
• Data lifecycle - covering development of a business case for data acquisition, data acquisition/creation, data maintenance (stewardship), data access/dissemination, data audit;
• Roles and responsibilities

The key roles for data management and their respective responsibilities are those of:

- Data Owner - with managerial and financial control of data, and legal rights over data, i.e. the Intellectual Property Rights and the Copyright.

- Data Champion - the senior member of an organisation who supports and presents the data issues to the managing board.

- Data Manager - with managerial responsibility for data management function within an organisation and implementation of the Data Policy.

- Data Steward/Custodian - responsible for the day-to-day management of each dataset within an organisation.

- Data Users - who have access to the data.

- Data Auditors – who independently check the quality of the data against agreed thresholds & targets.

The adoption of these roles will vary between organisations depending on the extent and level of data usage within an organisation.

5.4 Data quality assessment

Many of the address users have concerns about the quality of the current address datasets. In many cases, little is known about the data quality. For any such dataset, a formal quality assessment programme is required to establish:

• completeness - with respect to the specification of the dataset (which is often not formally defined). This includes both omissions (missing entries) and commission (duplicates) and is often difficult to establish as the totality of occurrences is not known. It requires comparing samples of the data with the source;
• currency - how up-to-date is the datsaset, compared to the real world. This is different from the frequency of update of the dataset;
• logical consistency - are adjacent addresses recorded in the same way. Basic consistency checks can be made to find inconsistencies which will then require investigation in the field;
• attribute accuracy - how accurate are the alphanumeric details of the data. Simple checks can be made of spelling and valid values;
• positional accuracy - how accurate are the coordinates (compared with the true position in the real world). This can now be established on a sample basis using GPS.