ClearCore automatically cleans and validates data before matching and offers many facilities for data cleansing and improving data quality of records within databases. The successful cleaning of data is the key to successful and effective matching. If we do not ensure the quality of the data we are matching then we will get unsafe and missed matches. We will also be unable to measure the quality of any matches made.

Data standardisation and validation: - All data is automatically standardised before validation and matching. E.g. fields are split and reformatted to ensure consistency and meet industry and local standards e.g.BS7666 and BS8766. This process includes mapping and matching diverse but equivalent codes from different applications

Data Cleaning and data enrichment: - Data can be automatically cleaned and enriched using reference datasets e.g. Address Reference datasets such as LLPG or CAG or an extract of DNAs or NLPG. Existing data at field or record level can be enriched with data from other sources. Other reference data includes “gone aways” lists, registers of births & death lists and commercial data, such as Dun & Bradstreet data.

Data de-duplication and data Matching: - Data is matched by identifying, linking and grouping related records and fields. Identification is both within and across data sets. Linking and matching uses historical as well as current data – taking account of people changing their identities. ClearCore is delivered with 1000’s of built in business rules for matching. The rules can be switched on or off by the user, using ClearCore simple point and click GUI, designed for use by non-technical user. This fine tuning achieves the perfect balance between overmatching (false links) and under-matching (missed links) giving the best results with the minimum of manual effort. An easy to interpret audit trail clearly explains the basis for each match.

Data Quality Profiling: - Field level data quality anomalies and issues are automatically measured and profiled.

Creation and maintenance of an Index: - An index with full cross references to source systems is created from the data from multiple sources e.g. a single view of adults and children. The index can be kept synchronised with the source systems via any middleware (such as WebSphere, BizTalk, Quovadx Cloverleaf, JBos) and/or via .csv batch updates. This index will be used to populate a Corporate Citizen or Children or Property Database, where other attributes can also be added. This data hub will be kept synchronised with source systems.”