3 Using Matching

This chapter describes how you can use the EDQ-CDS matching functionality to match your data.

This chapter includes the following sections:

Section 3.1, "Objectives of Matching"
Section 3.2, "Using Key Generation"
Section 3.3, "Using Individual Matching"
Section 3.4, "Using Entity Matching"
Section 3.5, "Using ID Matching"
Section 3.6, "Using Matching with Customer-Added Attributes"
Section 3.7, "Using Address Matching"

EDQ-CDS has been designed to match customer data that exhibits real-world variability. All relevant matches in the data set are presented back and appropriately scored according to the likelihood of a match between records. To do this, it uses a variety of different mechanisms, including the application of a wide range of matching algorithms on the data as it is presented, as well as matching techniques on derived forms of the data.

For example, names presented in one writing system are matched both using this writing system and also using a transformed version of the name, providing effective cross-script matching. Similarly, addresses are matched in near raw form (after standardization of international address words and phrases, and after removal of filler words), but also by extracting and matching key information from the address, such as the likely building number, sub-building number, and postal code.

3.1 Objectives of Matching

In general, the matching services provided by EDQ-CDS are designed for duplicate prevention, rather than searching. This means that the intention of the out-of-the-box services is to intervene when a record is added to a system if it appears that it may already exist. The implication of this is that the matching services are focused on much more than a single attribute (such as Name) and deliberately do not cast as wide a net as a typical search operation. There may be other records in the system that are not matched but which have similar details, perhaps even exactly the same name, but where the secondary identification information indicates that a match is unlikely. In these cases, EDQ-CDS aims to minimize the additional work for users or data stewards whose role it is to resolve possible matches. This makes the product ideally suited to operate as the data quality protection component of a Master Data Management system, such as Oracle Customer Hub, where the purpose of the services is to link as many records as possible together automatically with as little noise as possible. The same is true for a Customer Relationship Management system, such as Siebel.

Note :

It is possible to change the configuration of EDQ-CDS in order to perform more exhaustive matching. This is mainly designed for use with low volume, high value data sets that do not necessarily offer sufficient secondary information (beyond name fields).

3.1.1 Multiple Locales and Languages

EDQ-CDS has been designed as a multi-locale system, and uses international and culture-sensitive name transcription, transliteration and variant recognition techniques, as well as using international dictionaries when standardizing and matching addresses.

The system is designed to work with international data, and provides international dictionaries of name and address standardizations for this purpose. The international 'Latin script' dictionaries provide coverage of the following 'base' locales, amongst others:

United States and Canada
United Kingdom
France
Germany
Italy
Spain
Portugal
Brazil
Greece
Ireland
Austria
Turkey
South Africa
Australia and New Zealand
Scandinavia
Argentina
Mexico

In addition to these base locales, EDQ-CDS provides specific optional capabilities for advanced handling of data from the following locales:

Arab World (Arabic and Mixed Arabic/Latin)
Japan (Kanji, Katakana and Hiragana)
China (Simplified and Traditional Chinese)
Russia
Korea (Hangul)

The set of enabled languages is determined by the configuration of the EDQ-CDS - Initialize Reference Data project, so that the same reference data may be used by any number of EDQ-CDS matching servers. By default, reference data sets for the base locales are pre-initialized in the EDQ server landing area, but these can be easily overwritten either by unzipping cdslists-initialized-full.zip over these files (to provided coverage for all supported locales and languages) or by configuring and running the Initialization job.

3.1.2 Uses of Matching

The Matching processes included in EDQ-CDS are designed primarily for the following use cases:

Duplicate Prevention - uses the Key Generation and Matching web services to prevent duplicate records being entered into applications.
Regular Batch Matching for Duplicate Removal - uses the Batch Matching job, run on all, or a subset of, data in an application, and links records together for potential merge.

It is also possible to use the Batch Matching processes as a template for the deduplication of records before they are loaded into a system. This is likely to require additional configuration, and use of EDQ. In such circumstances the best practice is to understand the data before matching using data profiling and audit techniques, such as those available in the EDQ-CDS Data Quality Health Check. In most cases, the set of enabled match rules will need some tuning towards the specifics of the in-scope data in order to provide the optimum balance between performance and effectiveness. It may also be necessary to use EDQ's Match Review application to review possible matches, and construct rules for merging records together.

Note :

EDQ-CDS does not provide any out-of-the-box merging (or survivorship) configuration, because in the two main use cases, merging is performed by the calling application after matches have been identified.

3.1.2.1 Duplicate Prevention

EDQ-CDS uses stateless web services for duplicate prevention to avoid complex replication and synchronization of large volume customer data. This places the following requirements on the application integrating with EDQ:

Storage of Cluster Key tables for each type of record (for example, Contacts or Accounts). These are normally thin tables with two columns - the Primary Key of the record and the Cluster Key. The table must allow for multiple key values per record.
Functionality to select and construct candidate records to submit to the Matching service. This involves:
1. Querying the Cluster Key table for the relevant record, and finding all records that share a key value with the driving record.
2. Constructing the data that is required for matching for each of these records.
3. Submitting these Candidate records together with the driving record to the Matching service.

Optimum Duplicate Prevention Process Flow

In order to access the full capabilities of EDQ-CDS for duplicate prevention, the integration should work as follows:

To prepare the system for real-time duplicate prevention, key values are generated for each record in Batch using the Key Generation process. This can occur either when migrating the data into the application, or as a batch process to generate the key values into the application's Cluster Key tables.
When a record is added or updated in the application, the Key Generation service is called in real-time, and returns a number of key values for the record.
The application then selects candidate records (those records which share a common key with the driving record) using the existing stored keys and submits them along with the driving record to the Matching service.
The Matching service decides which of the candidates are a likely match to the driving record and returns the ids of these records, and a score indicating the strength of match.
The application then decides how to consume the matching results; for example, whether to 'auto-match' or present possible matches to the user so that a decision can be made whether or not to continue with inserting a record, or merge it with an existing record.
If the record is merged with another record to create a changed master record, an additional call should be made to the Key Generation service in order to re-generate the correct key values before committing the record.

In this model, complex multi-locale EDQ techniques are used to generate the key values and ensure that the right balance between performance and matching effectiveness is maintained, while ensuring that the calling application retains control of data integrity and transactional commits.

3.1.2.2 Batch Matching

When working with Siebel CRM, Siebel's Data Quality Manager is used to instigate batch jobs and a shared staging database is used to write records for matching and to consume match results. The EDQ-CDS batch matching processes automatically adjust to Siebel's 'Full Match' (match all records against each other) and 'Incremental Match' (match a subset of records against all of their selected candidates) modes.

3.1.3 Match Tuning

In EDQ-CDS matching, it is not necessary to be overly concerned with which identifiers will be populated in the data that is worked with. EDQ-CDS does not use an algorithm that will place unnecessary emphasis on unpopulated data, and so does not require adjustment for this.

Matching works by considering matches on related input attributes separately (such as those relating to name, address, email, etc) and attempting various ways on each of these to find a match. EDQ refers to the grouped matching rules on these logically related attributes as "compound comparisons". It then combines the matches on these compound comparisons to decide how well two records match as a whole. The matching design builds in the knowledge of how strong an identifier is likely to be based on real world principles. Match tuning is normally a matter of performing one of the following tasks:

Adjusting the weighting of a compound comparison
Enabling or disabling a compound comparison
Adjusting the key generation configuration.
Enabling or disabling a provided rule
Adjusting the score for a specific rule within a compound comparison
Inserting a new rule into a compound comparison (perhaps a stronger or weaker version of an existing rule)

Note:

Even when inserting new rule configurations, it may well be possible to use existing comparisons and comparison results rather than adding new comparisons, though both are possible.

3.1.4 Output of Match Metadata

The output of match metadata provides granular details about why two records matched, providing information about which compound comparisons contributed to a match. The following EDQ match metadata is output for each compound comparison (for example, Name, Address, Email, Phone, etc.):

[Compound Comparison] Result, for example, N040 Given name abbreviated
Score (out of 100)
Category (Exact, Fuzzy, No data or Conflict)

3.2 Using Key Generation

Key Generation is used to minimize the work that is performed during the final stage of matching. It works by splitting the records into tranches (clusters), based on similarities in significant data fields. Only subsets of the data which share similar characteristics (and will therefore be placed in the same cluster) will be compared on a record-by-record basis during matching.

If loose clusters are used, there will be a large number of records in each cluster. This means that there is a reduced risk that true matches will be missed, but also that a greater amount of processing will be required to compare all the key generated records. A tighter key generation strategy will result in smaller groups and hence a reduced processing time, but will increase the likelihood that some true matches will not be detected.

EDQ-CDS is supplied with a number of different key method algorithms for individual, entity, and address data that use different combinations of key data fields in their construction. Each key method algorithm has been assigned a unique prefix code for easy identification, and to ensure keys from different key methods are not identical.

3.2.1 Legacy Clustering

Prior to release 12.2.1 key generation was referred to as clustering and the functionality provided was a much more restricted version of current key generation, although the principles were the same. Only three methods of "clustering" were provided with no easy scope for customization.

These "legacy" methods can still be used by setting the following in the run profile:

phase.*.process.*.uselegacykeygen = Y

and the levels set using

phase.Individual\ Keygen.process.*.clusterlevel = [1/2/3]

3.2.2 Structure of Key Methods

For each party type, key methods are grouped into key groups and key types.

For example, the individual 'Name Phone' key group contains all the key methods constructed using a combination of the name and phone attributes. Within this group, there are two key types:

FNMGNMPNR: key methods based on family name metaphone, given name metaphone and phone number rightmost characters

FNMPNL: key methods based on family name metaphone and phone number leftmost characters

Each key type then consists of one or more actual key methods, each one using varying lengths of the metaphone or leftmost/rightmost characters.

For example, the FNMPNL key type contains the following key methods:

FNM4PNL6: family name metaphone first 4, phone number last 6

FNM4PNL7: family name metaphone first 4, phone number last 7

FNM4PNL8: family name metaphone first 4, phone number last 8

These are categorized as 'strict', 'typical' and 'loose' respectively, as the length of the phone number substring used becomes larger and therefore provides a tighter key.

Key values generated using the last of these methods would take the form:

FNM4PNL8^MN^65065421

An automatic or 'encoded' key profile consists of a pipe-delimited list of key methods with their associated key priorities, for example:

AD112FNL5GNL5^10|GNW1FNL0^11|AD17AD25CTL10^12|FNM4PNL8^13|PNR6^14

Note that the key priorities are only relative within a specific profile, they have no intrinsic meaning.

3.2.2.1 Keys for Custom Attributes

Keys for custom attributes can optionally be created during Key Generation (by default keys are not generated for custom attributes).

This is specified in the run profile as follows:

phase.*.process.*.customstringNkey = Y

phase.*.process.*.customdateNkey = Y

and can be overridden in real-time on a per-message basis as follows:

<dn:request customstringNkey="Y" customdateNkey="Y">

The actual keying method used depends on the key profile specified:

Strict profiles key custom strings on the full string, and custom dates on the full date
Loose profiles key custom strings on the metaphone of the string, and custom dates on the year only
Typical profiles key custom strings on the first 10 characters of the string, and custom dates on the year and month

Note that custom attributes are ignored if legacy cluster levels are used.

3.2.3 Key Method Analysis

Key method analysis introduces the capability within CDS to automatically analyze the customer's data and determine the best key profile for that particular data set. Key analysis consists of these main steps:

Generate key values for the data using all available key methods.
Profile, score and rank those key values using various statistical mechanisms such as high frequency key values and distribution/diversity of key values.
Construct and output a recommended key profile by selecting the best key method(s) within each key group.

Custom attributes will be taken account of during Key Analysis if they are enabled for Key Generation, as described in Keys for Custom Attributes.

All available custom attribute key methods are analyzed, in a similar fashion to the existing fixed attributes.

3.2.3.1 Running Batch Key Analysis

There are several new staging tables for Key Analysis which must be created prior to running the job. The SQL commands to create these tables are added to the existing default script, edq_staging_tables.sql, which is delivered with EDQ and installed under <middleware_home>/edq/oracle.edq/scripts/cds.

The batch jobs for running Key Analysis are:

Batch Individual Key Analysis
Batch Entity Key Analysis
Batch Address Key Analysis

These jobs are structured in a similar fashion to the existing batch jobs for key generation and matching, in that they expect to receive party data in the relevant Candidates tables in the staging schema, and output their results to tables in the same schema.

Note that due to the statistical nature of the way Key Analysis works, it expects to always receive the full set of customer data to analyze. Whilst the jobs will actually run with a sample of the data, the results will only apply to that sample and cannot be scaled up to the full dataset.

The following run profile parameter must be set to Y in order for Key Analysis to run successfully:

phase.Key\ Analysis.process.*.generateallkeys = Y

Note also that the run profile contains various new SQL statements for Key Analysis, in order to expose the SERVERID and JOBID parameters in a similar fashion to the existing staging tables. Therefore these parameters will also need updating in the run profile in-line with any changes for the other table parameters.

3.2.3.2 Key Method Analysis Outputs

Key Analysis outputs results in the following staging tables:

3.2.3.2.1 EDQCDS_KEY_ANALYSIS_PROFILE

This table contains a single row per job, which simply contains the recommended key profile, consisting of a pipe-delimited list of key methods with their associated key priorities, for example:

AD112FNL5GNL5^10|GNW1FNL0^11|AD17AD25CTL10^12|FNM4PNL8^13|PNR6^14

This is the profile that should be used for key generation and matching, if the user decides to accept the recommendation.

Note:

Key Analysis does not actually output the key values for the recommended profile; this must be done separately by running the relevant batch key generation job and passing in the recommended profile accordingly.

3.2.3.2.2 EDQCDS_KEY_ANALYSIS_REPORT

This table contains a single row per key method analyzed, detailing the statistics and score for each method together with an indication of whether it was selected for the profile and if so the assigned priority. Only those key methods generated are listed - that is, those for which the party data contained relevant non-blank attributes.

This report is provided mainly for support and diagnostics purposes.

3.2.3.2.3 EDQCDS_KEY_ANALYSIS_TOP_VALUES

This table contains the top 20 key values by count per key method analyzed. Only those key methods generated are listed - that is, those for which the party data contained relevant non-blank attributes.

This report can help users identify potential DQ issues with their data, i.e. very large key value counts may indicate spikes and generic data values such as '000000' phone numbers or 'sales@' email addresses.

3.2.4 Individual Key Types

Key methods for matching individual data are based on the following key types:

Table 3-1 Address Only

Key Type	Description
AD1AD2CTL	address1 distilled (No whitespace, leftmost chars), address2 distilled (No whitespace, leftmost chars), city standardized (No whitespace, leftmost chars)
ADACTLPRE	adminarea standardized (No whitespace, leftmost chars), city standardized (No whitespace, leftmost chars), premise derived (Denoised, no whitespace, leftmost chars)

Table 3-2 Name and Company

Key Type	Description
ANLGNLFNL	accountname (No whitespace, leftmost chars), givenname standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars)
ANWFNMGNL	accountname (Leftmost words), familyname (Double metaphone, leftmost chars), givenname standardized (No whitespace, leftmost chars)
ANWFNM	accountname (Leftmost words), familyname (Double metaphone, leftmost chars),
ANMGNLFNL	accountname (First word, double metaphone, leftmost chars), givenname standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars)

Table 3-3 Name and DOB

Key Type	Description
DBYGNLFNL	DOB standardized (Year), givenname standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars)
DBXGNLFNL	DOB standardized (Full date), givenname standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars)
DBNGNLFNL	DOB standardized (Year and month), givenname standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars)

Table 3-4 Name Only

Key Type	Description
FMP	fullname standardized (Array of tokens, metaphone pairs, leftmost chars)
GNWFNL	givenname standardized (Leftmost words), familynamenormalized (No whitespace, leftmost chars)

Table 3-5 Name and Phone

Key Type	Description
FNMGNMPNR	familyname (Double metaphone, leftmost chars), givenname standardized (First word, double metaphone, leftmost chars), phonenumbers standardized (Rightmost chars (array of))
FNMPNL	familyname (Double metaphone, leftmost chars), phonenumbers standardized (Leftmost chars (array of))

Table 3-6 Full Name and Address

Key Type	Description
AD1FNLGNL	address1 distilled (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars), givenname standardized (No whitespace, leftmost chars)
FNLGNLPCL	familyname (No whitespace, leftmost chars), givenname standardized (No whitespace, leftmost chars), postalcode standardized (No whitespace, leftmost chars)
CTLFNLGNL	city standardized (No whitespace, leftmost chars), familyname (No whitespace, leftmost chars), givenname standardized (No whitespace, leftmost chars)

Table 3-7 Household Address

Key Type	Description
AD1FNMPCL	address1 distilled (No whitespace, leftmost chars), familyname (Double metaphone, leftmost chars), postalcode standardized (No whitespace, leftmost chars)
AD1FNMCTL	address1 distilled (No whitespace, leftmost chars), familyname (Double metaphone, leftmost chars), city standardized (No whitespace, leftmost chars)

Table 3-8 National ID

Key Type	Description
NIL	nationalidnumber standardized (Leftmost chars (array of))
NIP	nationalidnumber standardized (Pairs of leftmost & rightmost chars (array of))

Table 3-9 Phone

Key Type	Description
PNR	phonenumbers standardized (Rightmost chars (array of))

Table 3-10 Script Name

Key Type	Description
OSLPCL	scriptfullname (No whitespace, leftmost chars), postalcode standardized (No whitespace, leftmost chars), ()

Table 3-11 Tax Number

Key Type	Description
TNL	taxnumber standardized (Leftmost chars (array of))
TNP	taxnumber standardized (Pairs of leftmost & rightmost chars (array of))

Table 3-12 UID

Key Type	Description
UID(1/2/3)	uid[1, 2, 3]standardized (Leftmost chars (array of))

Table 3-13 Custom Strings

Key Type	Description
CM[1-6]	customstring[1-6] standardized (Double metaphone, leftmost chars, if blank 8 leftmost chars (no metaphone))
CL[1-6]	customstring[1-6] standardized (No whitespace, leftmost chars)

Table 3-14 Custom Dates

Key Type	Description
CY[1-6]	customdate[1-6 ]standardized (Year)
CX[1-6]	customdate[1-6] standardized (Full date)
CN[1-6]	customdate[1-6] standardized (Year and month)

Note :

The key method algorithms use data attributes that have been normalized (for example, converted to upper case and symbols stripped) and have had whitespace removed. This allows key generation and matching to be performed in a case-insensitive manner and to be tolerant of the spacing within attributes.

3.2.4.1 Examples

The following record data is used to provide examples of the key values that are generated by the individual key method algorithms:

Attribute	Value
`firstname`	Jim
`middlename`	Frederick
`lastname`	Smith
`mobilephone`	077777 123456
`email`	[email protected]
`taxnumber`	888666444
`accountname`	Acme Ltd
`address1`	14 high St
`city`	Cambridge
`postalcode`	CB1 2AB
`uid1`	00021-53563
`eid1`	gbr0008873323
`nationalidnumber`	AB 12 34 56 C

The key values that are generated using the Typical key profile are as follows:

Key Type	Key Method	Priority	Cluster Values
`UI1`	UI10	1	UI10^0002153563
`AD1FNLGNL`	AD110FNL3GNL3	42	AD110FNL3GNL3^14HIGH^SMI^JAM
`AD1FNMPCL`	AD12FNM3PCL5	55	AD12FNM3PCL5^14^SM0^CB12A
`AD1AD2CTL`	AD17AD25CTL5	59	AD17AD25CTL5^14HIGH^^CAMBR
`ANWFNM`	ANW1FNM4	54	ANW1FNM4^ACME^SM0
`CTLFNLGNL`	CTL10FNL3GNL3	51	CTL10FNL3GNL3^CAMBRIDGE^SMI^JAM
ENP	ENP15	40	ENP15^JSMITHMYMAILCOM
FNLGNLPCL	FNL3GNL1PCL5	44	FNL3GNL1PCL5^SMI^J^CB12A
FNMPNL	FNM4PNL7	46	FNM4PNL7^SM0^0777771
NIL	NIL10	36	NIL10^AB123456C
PNR	PNR6	47	PNR6^123456
TNL	TNL1	37	TNL10^888666444

3.2.5 Entity Key Types

The following key types are provided for matching entity data:

Table 3-15 Name Address

Key Type	Description
AD1EMTPCL	address1 distilled (No whitespace, leftmost chars), entityname distilled (Array of tokens, double metaphone, leftmost chars), postalcode standardized (No whitespace, leftmost chars)
ENLPCL	entityname distilled/normalized (No whitespace, leftmost chars), postalcode standardized (No whitespace, leftmost chars),
FANENLCTL	fulladdress distilled (No whitespace, no numbers, denoised, leftmost chars), entityname distilled/normalized (No whitespace, leftmost chars), city standardized (No whitespace, leftmost chars)
AD1ENLPCL	address1 distilled (No whitespace, leftmost chars), entityname distilled/normalized (No whitespace, leftmost chars), postalcode standardized (No whitespace, leftmost chars)

Table 3-16 Name Metaphone Address

Key Type	Description
CTLFALNSM	city standardized (No whitespace, leftmost chars), fulladdress distilled (No whitespace, leftmost chars), fullname distilled/normalized (Double metaphone, leftmost chars)
FALNSM	fulladdress distilled (No whitespace, leftmost chars), fullnamedistilled/normalized (Double metaphone, leftmost chars),
CTLNSM	city standardized (No whitespace, leftmost chars), fullname distilled/normalized (Double metaphone, leftmost chars),

Table 3-17 Name only

Key Type	Description
NSL	fullname distilled (No whitespace, leftmost chars)
ENMSNM	entityname distilled (Double metaphone, leftmost chars), entitysubname distilled (Double metaphone, leftmost chars)
FMT	fullname distilled (Array of tokens, double metaphone, leftmost chars)

Table 3-18 Name City Phone

Key Type	Description
CTLENLPNR	city standardized (No whitespace, leftmost chars), entityname distilled/normalized (No whitespace, leftmost chars), phonenumbers standardized (Rightmost chars (array of))
CTLENLPNL	city standardized (No whitespace, leftmost chars), entityname distilled/normalized (No whitespace, leftmost chars), phonenumbers standardized (Leftmost chars (array of))

Table 3-19 Phone

Key Type	Description
PNR	phonenumbers standardized (Rightmost chars (array of))

Table 3-20 Website

Key Type	Description
WSL	websitestem (Leftmost chars (array of))

Table 3-21 Script Name

Key Type	Description
OSL	script fullname (Array of tokens, leftmost chars)

Table 3-22 VAT number

Key Type	Description
VNL	vatnumber standardized (Leftmost chars (array of))
VNP	vatnumber standardized (Pairs of leftmost & rightmost chars (array of))

Table 3-23 Tax Number

Key Type	Description
TNL	taxnumber standardized (Leftmost chars (array of))
TNP	taxnumber standardized (Pairs of leftmost & rightmost chars (array of))

Table 3-24 UID

Key Type	Description
UID[1,2,3]	uid[1, 2, 3]standardized (Leftmost chars (array of))

Table 3-25 Custom Strings

Key Type	Description
CM[1-6]	customstring[1-6] standardized (Double metaphone, leftmost chars, if blank 8 leftmost chars (no metaphone))
CL[1-6]	customstring[1-6] standardized (No whitespace, leftmost chars)

Table 3-26 Custom Dates

Key Type	Description
CY[1-6]	customdate[1-6 ]standardized (Year)
CX[1-6]	customdate[1-6] standardized (Full date)
CN[1-6]	customdate[1-6] standardized (Year and month)

Note :

The key method algorithms use data attributes that have been normalized (for example, converted to upper case and symbols stripped) and whitespace removed. This allows key generation and matching to be performed in a case-insensitive manner and be tolerant to the spacing within attributes.

3.2.5.1 Examples

The following record data is used to provide examples of the key values that are generated by the entity key method algorithms:

Attribute	Value
`name`	Oracle UK
`subname`	Cambridge
`phone`	+441223228400
`website`	http://www.oracle.com/uk
`taxnumber`	RGW432D243224
`vatnumber`	999111
`address1`	296 Cambridge Science Park
`city`	Cambridge
`postalcode`	CB4 0WD
`uid1`	00021-53563
`eid1`	gbr0008873323

The following key values are generated using a key profile of Typical:

Key Type	Key Method	Priority	Key values
`AD1PCL`	`AD13PCL4`	43	AD13PCL4^296^CB40
`AD1EMTPCL`	`AD14EMT4PCL3`	41	AD14EMT4PCL3^296C^ARKL^CB4
`CTLNSM`	`CTL0NSM6`	49	CTL0NSM6^CAMBRIDGE^ARKLKM
`CTLENLPNL`	`CTL1ENL1PNL7`	47	CTL1ENL1PNL7^C^O^4412232
`ENLPCL`	`ENL4PCL3`	42	ENL4PCL3^ORAC^CB4
`FALNSM`	`FAL10NSM4`	39	FAL10NSM4^296CAMBRID^ARKL NSL25^ORACLECAMBRIDGE
`NSL`	`NSL25`	40	NSL25^ORACLECAMBRIDGE
`PNR`	PNR6	58	PNR6^228400
`TNL`	`TNL10`	35	TNL10^RGW432D243
`UI1`	`UI10`	1	UI10^0002153563
`VNL`	`VNL10`	36	VNL10^999111
`WSL`	`WSL8`	57	WSL8^ORACLE

3.2.6 Address Key Types

The following key method types are provided for matching address data:

Table 3-27 Address Lines

Key Type	Description
AD1AD2	address 1 distilled (No whitespace, leftmost chars), address 2 distilled (No whitespace, leftmost chars)

Table 3-28 Address City

Key Type	Description
AD1CTL	address 1 distilled (No whitespace, leftmost chars), citystandardized (No whitespace, leftmost chars)
CTLPCLPRE	citystandardized (No whitespace, leftmost chars), postalcodestandardized (No whitespace, leftmost chars), premisederived (Denoised, no whitespace, leftmost chars)
PMSPCC	premisederived/address 1 distilled (First number word of premisederived/ premise leftmost chars/first number word of address1distilled/left most chars address1 distilled), postalcodestandardized/citystandardized (Leftmost chars of postalcode standardized/leftmost chars of city standardized),

Table 3-29 Full Address

Key Type	Description
FAL	fulladdress distilled (No whitespace, leftmost chars)
FAN	fulladdress distilled (No whitespace, no numbers, denoised, leftmost chars)

Table 3-30 Postal Code

Key Type	Description
PCL	postalcode standardized (No whitespace, leftmost chars)

Note:

A Number word is a word with one or more numbers within it. for example, 234 and 2A are both number words.
The key method algorithms use data attributes that have been normalized (for example, converted to upper case and symbols stripped) and whitespace removed. This allows key generation and matching to be performed in a case-insensitive manner and be tolerant to the spacing within attributes.

3.2.6.1 Examples

The following record data is used to provide examples of the key values that are generated by the address key method algorithms:

Attribute	Value
`address1`	2529 CINCINNATI ST
`address2`	APT 6
`city`	LOS ANGELES
`adminarea`	CA
`postalcode`	90033

Note :

During Key generation, ST is distilled out of the address1 field, and APT is distilled out of the address2 field. This is because they are common addressing components that are less important identifiers than the remainder of the address line, and removing them produces more accurate clusters.

The Key values that are generated using the Typical address key profile are:

Key Type	Key Method	Priority	Key Values
AD1AD2	AD110AD210	12	AD110AD210^2529CINCIN^6
AD1CTL	AD15CTL8	9	`AD15CTL8^2529C^LOSANGEL`
CTLPCLPRE	CTL8PCL5PRE0	10	CTL8PCL5PRE0^LOSANGEL^90033^2529
FAL	FAL10	11	`FAL10^2529CINCIN`
FAN	FAN10	13	FAN10^CINCINNATI
PCL	PCL0	15	PCL0^90033
PMSPCC	PMS6PCC5	8	PMS6PCC5^2529^90033

3.3 Using Individual Matching

The matching design for individuals in CDS is based on combining matches between several logical identifiers (compound comparisons). These compound comparisons are:

Name
Address
Account name
DOB
Phone number
Email
National ID number
Tax number

It is also possible to enable matching of the custom fields (however, these are not enabled by default)

EDQ-CDS uses preconfigured match rules on the compound comparisons to ascertain how well two records match (or don't match) on that particular logical identifier.

In order to determine whether two records as a whole match, EDQ-CDS uses the results for the matching on the logical identifiers and combines them to produce an overall score that gives a measure of how well the records match. Note that a conflict will negatively affect a score, as well as a match increasing it. For example, two records with an exact match on name and address, but a conflicting date of birth will score lower than a two records with an exact match on name and address, but no date of birth.

Each logical identifier has a default weighting, defining how likely two records with matches on the compound comparison related to this logical identifier.

3.3.1 Matching on the Individual Name logical identifier

The rules for matching on the individual name compound comparisons include the use of pre-matching transformations and various matching comparisons in order to handle the following types of variance between different representations of what may be the same individual name:

Names written in different writing systems/scripts, for example, 'Зоран' and 'Zoran'.
Variants of the same name, for example, 'Bill' and 'William'.
Different levels of name completeness, for example, 'Joseph Andrew Harris' and 'Joseph Harris'.
Name tokens in a different order, for example, 'Lacazette Jacques' and 'Jacques Lacazette'.
Abbreviated forms of names, for example, 'Chris' and 'Christian'.
Typographic differences, for example, 'Michael' and 'Micheal'.
The use of initials, for example, 'A M' and 'Alexander Martin'.
Changes of surname due to marriage, for example, 'Paula Jones' and 'Paula Lewis' at the same address.
Various combinations of the above types of variance.

Note :

In this table the pipe character is used to indicate a separator between the input given name and family name attributes (for example, Given Name= Martin, Family Name=Smith is written as 'Martin|Smith'). Where no pipe character is used, this means the Full Name is used in the match rule.

Note Also:

Near the top of this list are some conflict name rules, these are designed to negatively weight matches between two names that are obviously different genders, to avoid matches of this type.

Name Matching Rules	Example Name Match	Type
Script full name exact		Exact
Name exact	Martin\|Fox = Martin\|Fox	Exact
Standardized given name	Bill\|Lewis = William\|Lewis	Exact
Given name abbreviated	Chris\|Smith = Christina\|Smith	Fuzzy
Name conflict, supplied gender different	Paula\|Smith - Paul Smith (negatively weighted to eliminate matches such as this)	Conflict
Name conflict, derived gender different	Paula\|Smith - Paul Smith (negatively weighted to eliminate matches such as this)	Conflict
Standardized given name abbreviated	Abell\|Hernandez = Abelson\|Hernandez	Fuzzy
Script full name any order		Fuzzy
Given name similar and sounds like	Yngrid\|Martin = Ingrid\|Martin	Fuzzy
First name similar and sounds like	Yngrid Elisabeth\|Martin = Ingrid Martin	Fuzzy
Additional given names	Michael John\|Smith = John\|Smith	Fuzzy
Standardized full name	Mehmood Mahomed = Mahmoud Mohammed	Fuzzy
Script full name has additional names		Fuzzy
Additional names	Mary Jones Steward = Mary Jones	Fuzzy
Script full name typos		Fuzzy
Standardized given name abbreviated; family name typos	Abell\|Hernandez = Abelson\|Hernandes	Fuzzy
Full name typos, all words	Mary Cloire Jonez = Mary Claire Jones	Fuzzy
First name first three; family name typos	Ros Susan\|Jonez = Rose Susan\|Jones	Fuzzy
Full name initials in order; additional names	G A\|Smith = Gordon Alfred\|Smith	Fuzzy
Standardized first name only; female	Jacklin\|Jones = Jacqueline\|Smith	Fuzzy

3.3.2 Matching on the other logical identifiers

Addresses

The rules for matching on the address compound comparison within individual name matching include the use of pre-matching transformations and various matching comparisons in order to handle the following types of variance between different representations of what may be the same address:

Extracting the premise and subpremise
Standardizing commonly used words such as STREET, ROAD, etc.
Stripping commonly used words such as STREET, ROAD, etc.
Typographic differences

Note:

In this table the pipe character is used to indicate a separator between the inputs of address1, address2, address3, city, adminarea, postalcode. For example address1=296 Cambridge Science Park address2= Milton Road address3=<blank>, city=Cambridge adminarea = <blank> postalcode=CB4 0WD is represented as 296 Cambridge Science Park|Milton Road||Cambridge||CB4 0WD

Table 3-31 Matching on other logical identifiers

Address Rule Name	Example	Type
Address exact	296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD = 296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD	Exact
Premise, subpremise, address similar, postal code	Flat 1\|296 Cambridge Science Park\|\|Cambridge\|\|CB4 0WD = Flat 1\|296 Cambridge Sci Park\|\|Cambridge\|\|CB4 0WD	Fuzzy
Premise, no subpremise, address similar, postal code	296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD = 296 Cambridge Sci Park\|Milton Road\|\|Cambridge\|\|CB4 0WD	Fuzzy
Address1 and address2 distilled exact, postal code starts with	296 Milton Road\|\|\|Cambridge\|\|CB4 0WD = 296 Milton Road\|\|\|\|\|CB4 0WD	Fuzzy
Address1 distilled exact, address2 no conflict, postal code starts with	296 Milton Road\|Science Park\|\|Cambridge\|\|CB4 0WD = 296 Milton Road\|\|\|\|\|CB4 0WD	Fuzzy
Premise, subpremise, postal code starts with	Flat 1\|352 Milton Road\|\|Cambridge\|\|CB4 0WD = 352 Milton Road\|Flat 1\|\|\|\|CB4 0WD	Fuzzy
Premise, no subpremise, postal code starts with	296 Cambridge Science Park\|\|\|Cambridge\|\|CB4 0Wd = 296 The Science Park\|\|\|\|\|CB4 0WD	Fuzzy
Address1 distilled exact, postal code starts with	296 Cambridge Science Park\|Flat 1\|\|Cambridge\|\|CB4 0WD = 296 Cambridge Science Park\|Flat 6\|\|Cambridge\|\|CB4 0WD	Fuzzy
Address all words	296 Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD = Science Park\|Milton Road\|\|\|\|CB4 0WD	Fuzzy
Address all words typos	296 Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD = Sciense Park\|Milton Road\|\|\|\|CB4 0WD	Fuzzy
Address similar, postal code	296 Science Pk\|Milton Rd\|\|Cambridge\|\|CB4 0WD = Sceince Park\|Milton Road\|\|Cmbridge\|\|CB4 0WD	Fuzzy
Address similar; first address1 word	297 Cambridge Science Park\|\|Milton Road\|\|\|CB30WS = 296 Cambridge Science Park\|Milton Road\|\|\|\|CB4 0WD	Fuzzy
Postal code	296 Science Park\|\|\|\|\|CB4 0WD = \|Milton Road\|\|\|\|CB4 0WD	Fuzzy
Postal code starts with	296 Science Park\|\|\|\|\|CB4 0WD = \|\|\|\|\|CB4	Fuzzy
City exact	352 Mill Road\|\|\|Cambridge\|\|CB1 3NN = 296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD	Fuzzy
Address no data	\|\|\|\|\| = 296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD	No data
Address conflict	19 Teme Ave\|\|\|Malvern\|Worcs\|WR14 2XA = 296 Cambridge Science Park\|Milton Road\|\|Cambridge\|\|CB4 0WD	Conflict

Account name

Matching on account name allows for matches including

Exact match
Typographic differences
All words in common

Table 3-32 Account name

Account name rule	Example	Type
Account name exact	Widgets and Gadgets Ltd = Widgets and Gadgets Ltd	Exact
Account name typos	Widgets and Gadgets Ltd = Widgets and Gagets Ltd	Fuzzy
Account name all words	Federal Mogul Camshafts Castings Ltd = Federal Mogul Camshafts Ltd	Fuzzy
Account name all words out of order	Federal Mogul Camshafts Castings Ltd = Federal Mogul Castings Camshafts Ltd	Fuzzy
Account name all words typos	Federal Mogul Camshafts Castings Ltd = Federal Mogul Camshfts Ltd	Fuzzy
Account name all words output of order typos	Federal Mogul Camshafts Castings Ltd = Federal Mogul Castings Camshfts Ltd	Fuzzy
Account name no data	Oracle Ltd =	No data
Account name conflict	Federal Mogul Camshafts Castings Ltd = Wigets and Gadgets Ltd	Conflict

Phone numbers

Table 3-33 Phone numbers

Phone matching rule	Example	Type
Phone exact	01223456678 = 01223456678	Exact
Phone last N	+44223456678 = 01223456678	Fuzzy
Phone no data	01223456678 =	No data
Phone conflict	01223456678=01684345678	Conflict

Email matches allow for matches including:

Exact match
User name only exact
Typographic errors

Table 3-34 Email

Email match rule	Example	Type
Email exact	[email protected] = [email protected]	Exact
Email user exact	[email protected] = [email protected]	Fuzzy
Email typos	[email protected] = [email protected]	Fuzzy
Email no data	[email protected] =	No data
Email conflict	[email protected] = [email protected]	Conflict

Date of birth

Date of birth matches allow for matches including:

Exact match
Transposition of day/month match

Date of birth match rules also include a conflict rule where very different dates are penalized more severely

Table 3-35 Date of birth

Date of birth match rule	Example	Type
Date exact	11/01/1976 = 11/01/1976	Exact
Date similar	01/11/1976 = 11/01/1976	Fuzzy
Date no data	11/01/1976 =	No data
Date too different	11/12/2001 = 11/01/1976	Conflict
Date conflict	11/01/1976 = 20/01/1976	Conflict

National Id number

Table 3-36 National Id number

National Id number rule	Example	Type
National Id number exact	ABC112345 = ABC112345	Exact
National Id number typos	ABC12345 = ABC112345	Fuzzy
National Id number no data	ABC12345 =	No data
National Id number conflict	ABD2535 = BCD2145	Conflict

Tax number

Table 3-37 Tax number

Tax number rule	Example	Type
Tax number exact	ABC112345 = ABC112345	Exact
Tax number typos	ABC12345 = ABC112345	Fuzzy
Tax number no data	ABC12345 =	No data
Tax number conflict	ABD2535 = BCD2145	Conflict

The individual matching service outputs fields which give information on the matching of any of the logical identifiers described above, as well as an overall score and a overall rule name. This will allow the consuming application to have more granular information about how the records matched, to use as they wish.

Here is an example. The records in Table 3-38 were compared. Results are given in Table 3-39.

Table 3-38 Comparing Records

Record 1		Record 2
Firstname	John	Firstname	J
Lastname	Smith	Lastname	Smith
Phonenumber	01223456789	Phonenumber	+44223456789
address1	35 Mill Road	address1	35 Mill Road
city	Cambridge	city	Cambridge
postalcode	CB1 2JJ	postalcode	CB1 2JJ

Table 3-39 Results of Comparison

Value	Result
matchscore	95
rulename	N040 Given name abbreviated, A010 Address exact, C070 Account name no data, D030 DOB no data, P020 Phone Last N, E040 Email no data, I030 National ID number no data, T030 Tax number no data
ruleattributes	NAME,ADDRESS,PHONE
comparisonresults	Name Fuzzy, Address Exact, Phone Fuzzy
namescore	95
nameresult	N040 Given name abbreviated
namecategory	Fuzzy
addressscore	100
addressresult	A010 Address exact
phoneresult	P020 Phone Last N
phonescore	90
phonecategory	Fuzzyfamilyname
*Results that are no data are omitted for brevity

Note:

If a field is known never to be populated in the data, then it is possible to "turn off" the compound comparison relating to the logical identifier, so that it does not appear in the rule.

The comparisonresults output field gives a comma separated list of any logical identifiers that have contributed to the match and the category of the match (i.e. returned a category of Exact or Fuzzy).

The ruleattributes field returns a comma separated list of the logical idenfiers that contributed to the match.

In addition to the logical identifiers described above, it is possible to configure individual matching to use Custom fields for matching. Custom fields are not enabled by default for either matching or clustering, for further information, see Section 3.6, "Using Matching with Customer-Added Attributes"

It is also possible to perform matching or elimination of Individual records using custom unique identifiers, see Section 3.5, "Using ID Matching."

3.4 Using Entity Matching

As with individuals, the matching design for entities in CDS is based on combining matches between several logical identifiers, using compound comparisons. These compound comparisons are:

Entity name
Address
Phone number
Website address
Tax number
VAT number

It is also possible to enable matching on the custom fields (however matching on these is not enabled by default)

EDQ-CDS uses preconfigured rules on the compound comparisons relating to the logical identifiers to ascertain how well two records match (or don't match) for that particular logical identifier.

In order to determine whether two records as a whole match, EDQ-CDS uses the results for the matching on the logical identifiers and combines them to produce an overall score that gives a measure of how well the records match. Note that a conflict will negatively affect a score, as well as a match increasing it. For example, two records with an exact match on name and address, but a conflicting web address will score lower than two records with an exact match on name and address, but no web address.

Each logical identifier has a default weighting, defining how likely two records with matches on this particular identifier are to be the same individual.

Note :

It is significantly harder to match entities (as opposed to individuals) between different writing systems, as the process of transliteration — and even transcription — is much less likely to be successful. Very often, the only way to recognize that a company is the same when written in two different languages is to hold huge dictionaries of all possible company names and their appropriate translations (rather than transliterations or transcriptions). In most cases, such data is simply not available though if it is available it can be plugged into EDQ-CDS in order to improve results.

3.4.1 Entity Name Matching

The rules for matching entity names include the use of pre-matching transformations and various matching comparisons in order to handle the following types of variance between different representations of what may be the same entity name:

Entity names written in different writing systems.
Entity names with or without suffixes, for example, 'Oracle LTD' and 'Oracle'.
Entity names containing abbreviated terms or suffixes, for example, 'Oracle Limited' and 'Oracle LTD'.
Character order and spelling differences/errors in entity names, for example, 'Oracle' and 'Oralce'.
Entity names with different levels of name completeness, for example, 'ABC Technology Consultants LTD' and 'ABC Technology LTD'.
Entity name tokens appearing in a different order, for example, 'Cambridge Science Park LTD' and 'Science Park Cambridge'.
Entity Names where part or all of the name is reduced to an acronym, for example, 'Oracle Catering' and 'O.C.'.

Note :

In the following table, where a name matching rule uses the 'full name', this means it applies to the entity full name identifier, a concatenation of the entity name and sub-name attributes. The pipe (|) character is used to separate the entity name and sub-name were the sub-name attribute is required to provide an example match.

Entity Name Matching Rule	Example Entity Name Match	Type
Script full name exact
Full name exact	TCHIBO GMBH = TCHIBO GMBH
Standardized full name exact	ORACLE UK LTD \| READING = ORACLE UK LIMITED \| READING	Fuzzy
Script full name without suffixes exact		Fuzzy
Full name without suffixes exact	ORACLE = ORACLE CORPORATION	Fuzzy
Full name without suffixes similar and sounds like	ORACLE CAMBRIDGE SCIENCE PARK = ORACLE CAMBRIDGE PARK SCIENCE	Fuzzy
Script full name out of order		Fuzzy
Script full name without suffixes all words out of order		Fuzzy
Full name without suffixes all words out of order	CAMBRIDGE SCIENCE PARK LTD = SCIENCE PARK CAMBRIDGE	Fuzzy
Script full name has additional names		Fuzzy
Script entity name without suffixes exact		Fuzzy
Entity name without suffixes exact	ORACLE CORPORATION \| CAMBRIDGE = ORACLE \| READING	Fuzzy
Full name all words shorter with typos	Oracle Inc \| Cambridge =Oracl \| Cambridge	Fuzzy
Script entity name without suffixes starts with		Fuzzy
Entity name without suffixes starts with	ABC TECHNOLOGY CONSULTANTS LTD = ABC TECHNOLOGY LTD	Fuzzy
Script full name without suffixes all words shorter with typos		Fuzzy
Full name without suffixes all words shorter with typos	Federal Mogull \| Camshafts Inc = Federal Mogul Camshafts Castings Ltd	Fuzzy
Script full name typos		Fuzzy
Full name typos	ABD SERVICES LTD = ABC SERVICES LTD	Fuzzy
Script full name without suffixes typos		Fuzzy
Full name without suffixes typos	ABD ENGINEERING LTD = ABC ENGINEERING	Fuzzy
Script entity name without suffixes starts with		Fuzzy
Entity name without suffixes starts with	ABC LIMITED \| CAMBRIDGE = ABC PHARMACEUTICALS LIMITED \| READING	Fuzzy
Standardized full name acronym exact	CSC= Computer Science Corporation	Fuzzy
Entity name distilled longest common substring 12+	Colebrook & Burgess (North Shields) Ltd. = Colebrook & Burgess (Teesside) Ltd.	Fuzzy
Full name without suffixes acronym exact	CSC = Computer Science Collaborations Ltd	Fuzzy
Full name without suffixes acronym contains	Oracle CK = Oracle Collaborative Koopers	Fuzzy
Entity name without suffixes loose typos	Oracle Collaborative Coopers = Orracl Colabarativ Kupers	Fuzzy
Entity name without suffixes first token	DANVERS BANCORP INC = DANVERS MUNICIPAL FEDERAL CREDIT UNION	Fuzzy
Entity name distilled first 3 exact, longest common substring 6+	Lincoln Co-Operative Chemists Ltd. = Lincolnshire Co-Operative Ltd.	Fuzzy
Entity name distilled one or more tokens exact	Burgess Video Ltd. = Sue Burgess Ltd.	Fuzzy
Entity name no data	Oracle Corporation =	No data
Entity name conflict	Oracle Corporation = Sue Burgess Ltd.	Conflict

3.4.2 Matching on other logical identifiers for Entities

Addresses

The rules for matching addresses within entity name matching include the use of pre-matching transformations and various matching comparisons in order to handle the following types of variance between different representations of what may be the same address:

Extracting the premise and subpremise
Standardizing commonly used words such as STREET, ROAD, etc
Stripping commonly used words such as STREET, ROAD, etc
Typographic differences

Note:

In this table the pipe character is used to indicate a separator between the inputs of address1, address2, address3, city, adminarea, postalcode, country. For example address1=296 Cambridge Science Park address2= Milton Road address3=<blank>, city=Cambridge adminarea = <blank> postalcode=CB4 0WD country= United Kingdom is represented as 296 Cambridge Science Park|Milton Road||Cambridge||CB4 0WD|United Kingdom

Table 3-40 Address matching

Address matching rule	Example	Type
Address exact	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom = Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom	Exact
Subpremise, premise, postal code starts with, address similar	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4\|United Kingdom	Fuzzy
Premise, no subpremise, postal code starts with, address similar	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4\|United Kingdom	Fuzzy
Subpremise, premise, postal code starts with	Flat 1\|352 Milton Road\|\|Cambridge\|\|CB4 0WD\| = 352 Milton Road\|Flat 1\|\|\|\|CB4 0WD\|	Fuzzy
Premise, no subpremise, postal code starts with	296 Milton Road\|Science Park\|\|Cambridge\|\|CB4 0WD\| = 296 Milton Road\|\|\|\|\|CB4 0WD\|	Fuzzy
Address all words	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom	Fuzzy
Address all words typos	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|Millton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom	Fuzzy
Address 1 typos, city, country exact or no data	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Science\|Mil\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom	Fuzzy
Address similar, postal code	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Science\|Milton\|\|Cam\|Cambridgeshire\|CB4 1AB\|United Kingdom	Fuzzy
Postal code	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|Arbury Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\|United Kingdom	Fuzzy
City and country	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|Arbury Road\|\|Cambridge\|Cambridgeshire\|\|United Kingdom	Fuzzy
City	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|Arbury Road\|\|Cambridge\|Cambridgeshire\|\|	Fuzzy
Address similar, first address 1 word	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Datanomic Science Park\|Milton Road\|Cambridge\|Cambridgeshire\|\|United Kingdom\|	Fuzzy
Country	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Datanomic Science Park\|Arbury Road\|Cambridge\|Cambridgeshire\|\|\|United Kingdom	Fuzzy
Address no data	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = \|\|\|\|\|\|	No data
Address conflict	Flat 1, 296 The Science Park\|Milton Road\|\|Cambridge\|Cambridgeshire\|CB4 1AB\| = Datanomic\|\|\|Arbury\|\|\|	Conflict

Table 3-41 Website address

Website address matching rule	Example	Type
Website exact	www.tcnltd.com = www.tcnltd.com	Exact
Website stem exact	www.tcnltd.co.uk = www.tcnltd.com	Fuzzy
Website no data	www.tcnltd.com =	No data
Website conflict	www.abc.com = www.tcnltd.com	Conflict

Phone Number

Phone number matches allow for matches including:

Exact match
Last N characters matching

Table 3-42 Phone Number

Phone matching rule	Example	Type
Phone exact	01223456678 = 01223456678	Exact
Phone last N	+44223456678 = 01223456678	Fuzzy
Phone no data	01223456678 =	No data
Phone conflict	01223456678=01684345678	Conflict

Table 3-43 VAT number

VAT number rule	Example	Type
VAT number rule	ABC112345 = ABC112345	Exact
VAT number exact	ABC12345 = ABC112345	Fuzzy
VAT number no data	ABC12345 =	No data
VAT number conflict	ABD2535 = BCD2145	Conflict

Table 3-44 Tax number

Tax number rule	Example	Type
Tax number exact	ABC112345 = ABC112345	Exact
Tax number typos	ABC12345 = ABC112345	Fuzzy
Tax number no data	ABC12345 =	No data
Tax number conflict	ABD2535 = BCD2145	Conflict

The entity matching service outputs fields which give information on the matching of any of the logical identifiers described above, as well as an overall score and an overall rule name. This will allow the consuming application to have more granular information about how the records matched, to use as they wish. Here is an example:

Table 3-45 Comparing Records

Record 1		Record 2
Name	Widgets and Gadgets Ltd	Name	Gadgets and Widgets Ltd
Subname	Cambridge	Subname	Cambridge
Phone	012234567890	Phone	+4412234567890
Website	www.widgetsandgadgets.com	Website	www.widgetsandgadgets.org
Tax Number	ABC 1234 12	Tax Number	ABC 1234 12
Address1	29 Mill Road	Address1	Flat 3
Address2	Flat 3	Address2	29 Mill Road
City		City	Cambridge
Postal Code		Postal Code	CB1 3GH

Table 3-46 Results of Comparison

Value	Result
ruleattributes	NAME,ADDRESS,PHONE,WEBSITE,TAXNUMBER
matchscore	97
rulename	N090 Full name without suffixes all words out of order, A040 Subpremise, premise, postal code starts with, W020 Website stem exact, P020 Phone last N, T010 Tax number exact, V030
comparisonresults	Name Fuzzy, Address Fuzzy, Website Fuzzy, Phone Fuzzy, Tax Number Exact
nameresult	N090 Full name without suffixes all words out of order
namescore	20
namecategory	Fuzzy
addressresult	A040 Subpremise, premise, postal code starts with
addressscore	50
addresscategory	Fuzzy
phonenumberresult	P020 Phone last N
phonenumberscore	70
phonenumbercategory	Fuzzy
websiteresult	W020 Website stem exact
websitescore	70
websitecategory	Fuzzy
taxnumberresult	T010 Tax number exact
taxnumberscore	100
taxnumbercategory	Exact
*Results that are no data are omitted for brevity

Results that are no data are omitted for brevity.

The ruleattributes field returns a comma separated list of the logical idenfiers that contributed to the match.

Note:

If a field is known never to be populated in the data, then it is possible to "turn off" the compound comparison relating to the logical identifier, so that it does not appear in the rule.

It is also possible to perform matching or elimination of Entity records using custom unique key generation, see Section 3.5, "Using ID Matching."

3.5 Using ID Matching

The ID Matching rules in EDQ-CDS allow matching (or elimination) based solely on custom unique identifiers, without the need for a name match of some kind, irrespective of matching (or not) on other fields. They are performed before, and are completely separate from the rule which matches on the logical identifiers described in the previous sections.

Matching and elimination is provided for Entity and Individual Matching, but not Address Matching.

Note:

Unique ID (UID) matching is always performed before EID or IEID matching. Therefore, if two records are matched by unique identifiers, they cannot then be eliminated.
These identifiers are always compared in standardized form; for example, values that differ only in case or additional non-alphanumeric character are considered identical. for example, the following values are identical for the purposes of ID matching:
- AB123456789
- ab123-456-789
- ab12345 6789
- ab#123456789

3.5.1 Using Unique ID Matching

The UID Match rules are held in the [I005] UID and[E005] UID match group of the Individual and Entity Match processes respectively. For example, for the match groups for Individual matches are as follows:

[I005A] Match UID1
[I005B] Match UID2
[I005C] Match UID3

To use these rules, map the required data in the records to one or more of the uid attributes. The matching rules will always match two records sharing a common unique identifier, even if none of the other attributes match.

Note:

The uid attributes accept multiple values in the form of a pipe delimited list. A match will be returned between two records if any one of a multiple set of attribute values is matched.
Matching between uid attributes is not possible, for example, uid1 values cannot be matched with uid2 or uid3 values.

Example

The Passport Number field in a series of records is configured as the uid1 attribute. Therefore, the following records are returned as a match:

Record ID	First Name	Last Name	`uid1` (Passport Number)	Match?
1	Fred	Smith	12345678	Yes
2	John	Doe	12345678	Yes

The following records with multiple values in the uid1 field are also matched:

Record ID	First Name	Last Name	`uid1` (Passport Number)	Match?
1	Fred	Smith	12312312 \| 67867867	Yes
2	John	Doe	67867867 \| 23423423	Yes

The SSN field for the same set of records is configured as the uid2 attribute. The uid1 and uid2 fields are not cross matched; even though the uid1 value of Record 1 matches the uid2 value of Record 2:

Record ID	First Name	Last Name	`uid1` (Passport Number)	`uid2` (SSN)	Match?
1	Fred	Smith	12312312	67867867	No
2	John	Doe	67867867	12312312	No

3.5.2 Using Elimination ID Matching

The Elimination ID (EID) Match rules are held in the [ELIM015] EID ELIMINATIONS group of the Entity and Individual Match processes:

[ELIM015A] ELIMINATE EID1
[ELIM015B] ELIMINATE EID2
[ELIM015C] ELIMINATE EID3

To use these rules, map the required data in the records to one or more of the eid attributes. The EID matching rules will always return a "No Match" result for two records that do not share a common value in an eid attribute, even if all other attributes match. The exception to this is if the two records are matched using a uid attribute, as UID matching is performed before EID matching.

Note:

eid attributes accept multiple values in the form of a pipe delimited list. A "No Match" result will be returned between two records if none the values in an attribute are matched.
Eliminating possible matches by comparing values between different eid attributes is not possible, for example, eid1 values cannot be compared with eid2 or eid3 values.

Example

The SSN field in a series of records is configured as the eid1 attribute. Therefore, the following records are eliminated as a possible match:

Record ID	First Name	Last Name	`eid1` (SSN)	Eliminate?
1	John	Doe	12345678	Yes
2	John	Doe	87654321	Yes

The following records with multiple values in the eid1 field are also eliminated as a possible match, as none of the values match:

Record ID	First Name	Last Name	`eid1` (SSN)	Eliminate?
1	John	Doe	12312312 \| 23423423	Yes
2	John	Doe	45645645\| 67867867	Yes

The Passport field for the same set of records is configured as the eid2 attribute. The eid1 and eid2 fields are not compared, and therefore a "No Match" result is returned and the records are eliminated as a possible match:

Record ID	First Name	Last Name	`eid1` (SSN)	`eid2` (Passport Number)	Eliminate?
1	John	Doe	12312312	67867867	Yes
2	John	Doe	67867867	12312312	Yes

Finally, there are two identical values in the eid1 fields of the following records, and therefore they are not eliminated as a possible match:

Record ID	First Name	Last Name	`eid1` (SSN)	Eliminate?
1	John	Doe	12312312 \| 23423423	No
2	John	Doe	45645645\| 12312312	No

3.5.3 Using Inverted Elimination ID Matching

The Inverted Elimination ID (IEID) Match rules are held in the INVERTED EID ELIMINATIONS group of the Entity and Individual Match processes:

Inverted ID matching provides similar functionality to Elimination Ids (EIDs) but produces a "No match" result when the identifier values are the same. Inverted ID matching allows you to eliminate matches where records share a common value.

To use these rules, map the required data in the records to one or more of the ieid attributes. The IEID matching rules will always return a "No Match" result for records where the inverted EID (IEID) values are the same.

3.6 Using Matching with Customer-Added Attributes

Matching with customer-added string and date attributes improves how you can configure EDQ and reduces the need to customize the EDQ-CDS configuration for attributes not present on the standard interface.

The Individual Candidates and Entity Candidates interfaces each contain six custom string and three custom date attributes. The Matches interface contains custom result, category, and score attributes for each custom string and custom date.

3.6.1 Standardization

Custom strings can be specified as type identifier or text, which affects how they are standardized: identifier custom strings are stripped of non-alphanumeric characters and converted to upper case, while text custom strings are just normalized.

This behavior is specified in the run profile as follows:

phase.*.process.*.customstringNtype = text

and can be overridden in real-time on a per-message basis as follows:

<dn:request customstringNtype="identifier">

Custom dates are standardized the same way, as a conversion to the date data type.

3.6.2 Matching

Custom attributes can optionally be used during matching (by default no matching is performed on custom attributes) irrespective of whether or not they have been used for keying (see Keys for Custom Attributes).

There are two ways custom attributes can be matched:

Exact only
Exact and fuzzy

There are two compound comparisons for each custom attribute:

customstringNexact/customdateNexact
customstringNfuzzy/customdateNfuzzy

Therefore the enablement and type of matching performed for each custom attribute, and the corresponding weighting, is specified in the run profile by using the relevant 'exact' or 'fuzzy' parameters for each of these compound comparisons, for example:

phase.*.process.Match\ -\ Individual.overallscore.customstring1exact.enabled = Y
phase.*.process.Match\ -\ Individual.overallscore.customstring1exact.weighting = 1
phase.Individual\ Match.process.*.overallscore.customstring1fuzzy.enabled   = N
phase.Individual\ Match.process.*.overallscore.customstring1fuzzy.weighting = 1

That is, in order to match on any given custom attribute, either the corresponding 'exact' or 'fuzzy' compound comparison should be enabled, but not both.

These settings can also be overridden in real-time on a per-message basis as follows:

<dn:request 
   overallscore.customstring1exact.enabled="Y"
   overallscore.customstring1exact.weighting="1"
   overallscore.customstring1fuzzy.enabled="N"
   overallscore.customstring1fuzzy.weighting="1"
>

3.7 Using Address Matching

The rules for matching addresses include the use of pre-matching transformations and various matching comparisons in order to handle variance between different representations of what may be the same address, for example:

Addresses containing abbreviated terms or suffixes.
Character order and spelling differences/errors in addresses.
Addresses with different levels of completeness.
Addresses where extracted premise and sub-premise match, and other components of the address are in a different order or missing on one side.

The following table lists all of the rules provided:

Address Match Rule Code	Address Match Rule Description
[A010]	Address exact, postal code exact
[A020]	Address exact, no postal code
[A030]	Address lines 1 and 2 exact, city exact, postal code exact
[A040]	Address lines 1 and 2 exact, city exact, postal code starts with
[A050]	Address all words, subpremise exact, premise exact, postal code exact
[A060]	Address all words, subpremise exact, premise exact, postal code no conflict
[A070]	Address 1 exact, address 2 no conflict, subpremise exact, premise exact postal code exact
[A080]	Address 1 exact, address 2 no conflict, subpremise exact, premise exact, postal code starts with
[A090]	Address 1 exact, address 2 no conflict, subpremise exact, premise exact, postal code no conflict
[A100]	Address all words typos, subpremise exact, premise exact, postal code exact
[A110]	Address all words typos, subpremise exact, premise exact, postal code no conflict
[A120]	Address 1 exact, address 2 no conflict, postal code exact
[A130]	Address 1 exact, address 2 no conflict, postal code starts with
[A140]	Address 1 exact, subpremise exact, premise exact, postal code exact
[A150]	Address 1 exact, subpremise exact, premise exact, postal code starts with
[A160]	Address 1 exact, subpremise no conflict, premise no conflict, postal code exact
[A170]	Address 1 exact, subpremise no conflict, premise no conflict, postal code starts with
[A180]	Address all words, subpremise no conflict, premise no conflict, postal code exact
[A190]	Address all words, subpremise no conflict, premise no conflict, postal code no conflict
[A200]	Address 1 all words, subpremise exact, premise exact, postal code exact
[A210]	Address 1 all words, subpremise exact, premise exact, postal code starts with
[A220]	Address 1 all words, subpremise no conflict, premise no conflict, postal code exact
[A230]	Address 1 all words, subpremise no conflict, premise no conflict, postal code starts with
[A240]	Address1 common string 7+, subpremise exact, premise exact, postal code exact
[A250]	Address all words, postal code exact
[A260]	Address similar, subpremise exact, premise exact, postal code exact
[A270]	Address 1 all words, address 2 no conflict, postal code exact
[A280]	Address 1 all words, address 2 no conflict, postal code starts with
[A290]	Address all words typos, postal code exact
[A300]	Address 1 exact, subpremise exact, premise exact, postal code no conflict
[A310]	Address 1 all words, subpremise exact, premise exact, postal code no conflict
[A320]	Address 1 exact, postal code exact
[A330]	Address 1 exact, postal code starts with
[A340]	Subpremise exact, premise exact; postal code exact
[A350]	Subpremise exact, premise exact, postal code starts with
[A360]	Address all words
[A370]	Address all words typos
[A380]	Address similar; postal code
[A390]	Address similar; first address one word

The following table provides examples of matches by Match Rule Code only, with the key fields highlighted in bold text where required:

Address Match Rule Code	Address Component	Record	Matched Record
[A010]	address1	901 GOLF CLUB RD	901 GOLF CLUB RD
	city	WESTWOOD	WESTWOOD
	subadminarea	PLUMAS	PLUMAS
	adminarea	CA	CA
	postalcode	96137	96137
	country	US	US
[A020]	As for [A010], but the postalcode field in both records is blank.
[A030]	address1	1201 BEECH ST	1201 BEECH ST
	address2	APT 104F	APT 104F
	city	PALO ALTO	PALO ALTO
	subadminarea	SANTA CLARA	SAN MATEO
	adminarea	CA	CA
	postalcode	94303	94303
	country	US	US
[A040]	As [A030], except the v field in one address starts with the same characters as the other, but is not identical.
[A050]	address1	5 Hogskoleringen	Hogskoleringen 5
	city	Trondheim	Trondheim
	adminarea		SØR-TRØNDELAG
	postalcode	7491	7491
	country	Norway	Norway
[A060]	As [A050], except one or both of the postalcode fields are blank.
[A070]	address1	Heinrichboeckingstr 10-14	Heinrichboeckingstr 10-14
	address2	Service Zentrum Merzig
	city	Saarbrücken	Saarbrücken
	adminarea		SAARLAND
	postalcode	66121	66121
	country	Germany	Germany
[A080]	Same as [A070], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A090]	Same as [A070], except one or both of the `postalcode` fields are blank.
[A100]	address1	HOGSKOLERINGE 5	HOGSKOLERINGEN 5
	city	Trondheim	Trondheim
	postalcode	9491	9491
	country	Norway	Norway
[A110]	Same as [A100], except one or both of the postalcode fields are blank.
[A120]	address1	Marshfield Bank	Marshfield Bank
	address2	WOOLSTANWOOD
	city	Crewe	Crewe
	postalcode	CW28UY	CW28UY
	country	UK	UK
[A130]	Same as [A120], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A140]	address1	Apt Y302	APT Y302
	address2	1605 Sherringtowne Ave	1605 Sherington Ave
	city	NEWPORT BEACH	NEWPORT BEACH
	adminarea	Orange	Orange
	postalcode	92663-9087	92663-9087
	country	US	US
[A150]	Same as [A140], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A160]	address1	1728 Corporate Xing	1728 Corporate Xing
	address2	Suite1
	city	O Fallon	O Fallon
	adminarea	ILLINOIS	IL
	postalcode	62269-3734	62269-3734
	city	US	US
[A170]	Same as [A160], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A180]	address1	Block 16	16 Dunsinane Ave
	address2	Dunsinane Avenue
	address3	Dunsinane Industrial Estate
	city	Dunsinane	Dunsinane
	postalcode	DD23QT	DD23QT
	country	UK	UK
[A190]	As [A180], except one or both of the postalcode fields are blank.
[A200]	address1	26701 QUAIL CRK	26701 QUAIL CRK APT 107
	address2	APT 107
	city	ALISO VIEJO	LAGUNA HILLS
	postalcode	92656-1089	92656-1089
	country	US	US
[A210]	Same as [A200], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A220]	address1	Folkes Road	Unit 12 Folkes Road
	address2	Hayes Trading Estate	Lye
	address3	Lye
	city	Stourbridge	Stourbridge
	postalcode	DY98RN	DY98RN
	country	UK	UK
[A230]	Same as [A220], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A240]	address1	101/61 NAWANAKORN INDUSTRY	101/61 NAVANAKORN INDUSTRY
	address2	SELFLEMENT PHAHONYOTHIN	PAHOLYOTHIN KLONGNUENG
	city	KLONGLAUNG	KHLONG LUANG
	postalcode	12120	12120
	country	Thailand	Thailand
[A250]	address1	Blyth House	Blyth House
	address2	130 Hordern Road	Hordern Road
	city	Wolverhampton	Wolverhampton
	postalcode	WV60HS	WV60HS
	country	UK	UK
[A260]	address1	21001 State Route 739	21001 Sr Rt 739
	address2	7
	city	Raymond	Raymond
	postalcode	43067	43067
	country	United States	United States
[A270]	address1	Lancaster House Aviation Way	Aviation Way
	address2		Southend Airport
	city	SOUTHEND ON SEA	SOUTHEND ON SEA
	postalcode	SS26UN	SS26UN
	country	UK	UK
[A280]	Same as [A270], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A290]	address1	Blythe House	Blyth House
	address2	130 Hordern Road	Hordern Road
	city	Wolverhampton	Wolverhampton
	postalcode	WV60HS	WV60HS
	country	UK	UK
[A300]	Same as [A140], except one or both of the postalcode fields are blank.
[A310]	Same as [A200], except one of both of the postalcode fields are blank.
[A320]	address1	Network House	Network House
	address2	1 Ariel Way	Wood Lane
	city	London	London
	postalcode	W127SL	W127SL
	country	UK	UK
[A330]	Same as [A320], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A340]	address1	College Business Park	College Business Park
	address2	Park	Coldhams Lane
	city	Cambridge
	postalcode	CB13HD	CB13HD
	country	United Kingdom	United Kingdom
[A350]	Same as [A340], except the postalcode field in one address starts with the same characters as the postalcode field in the other, but is not identical.
[A360]	address1	938 Miller St	Medical Ctr Blvd
	address2	Medical Center Boulevard
	city	Winston Salem	Winston- Salem
	postalcode	27157	27157
	country	United States	United States
[A370]	address1	Humberstone Avenue	24 Humberston Avenue
	address2	Humberstone	Humberston
	city	GRIMSBY	GRIMSBY
	postalcode	DN364SX	DN364SP
	country	UK	UK
[A380]	address1	5 Sidings Court	Greyfriars House
	address2	White Rose Way	Sidings Court
	city	DONCASTER	DONCASTER
	postalcode	DN45NU	DN45NU
	country	UK	UK
[A390]	address1	120 Howard St	120 Howard St
	address2		STE 200
	city	San Fransisco	San Fransisco
	adminarea	CA	CA
	postalcode	94105-1622	94105-1615
	country	United States	United States

Note:

Unlike Individual and Entity matching, Address Matching does not make use of the compound comparison match functionality, since it does not lend itself to splitting the matching between separate logical identifiers for matching in the same way.