DronaBlog

Monday, August 6, 2018

Informatica MDM - Match Rule Tuning

Would you like to know how to perform  Informatica MDM match rule tuning? Are you also interested in knowing what steps are involved in match rule tuning? If so, then this article, will assist you in understanding the steps involved in match rule tuning.

Introduction

The Informatica MDM match tuning is an iterative process. It involves the following steps: data profiling, data standardization, defining the fuzzy match key, tuning the fuzzy match process and database tuning.

Activities

The activities mentioned below are needed to perform the match rule tuning in the Informatica MDM.

Activity
Details
Data Profiling​
The right data in for the match, the data investigation, the data accuracy, the data completeness.
​Data Standardization
Cleaning and standardization
​Define the Fuzzy Match key
Fuzzy match keys  ( the columns that need to be matched ) with the key width.
Fuzzy Match Process​
How to use the following:
1) Key width
2) Match level
3) Search level
4) Cleanse server log
5) Dynamic Match Threshold (DMAT)
6) Filters
7) Subtype Matching
8) Match Only Previous Rowid Object option
9) Configure match threads
10) Enable Light Weight Matching (LWM)
​Database Tuning
​1) Analyze tables
2) Create indexes
3) Configure Match_Batch_Size
4) Analyze STRP table.


Data Profiling

  • You need to perform analysis of the data on which the match will be performed. You should also analyze the quality of data across all fields.
  • Share the result of the data analysis with business users and get inputs about what attributes need to be considered for the matching  process.
  • Identify fields which you think can provide better matches, e.g. SSN, TAXID etc.
  • The next step is to determine filter criteria which are normally used on exact columns such as COUNTRY=US. This will be helpful for achieving better performance.
  • You need to also determine the completeness of data. For example, if the country code is valued in only 50% of the records, it may not be a good candidate as an exact column.
  • You need to verify percentage of data accuracy.,.e.g. the gender field should only contain gender values. 
  • It is always a good idea to analyze data using the pattern mechanism.
  • Finally determine the type of match population to use. e.g. USA.

Data Standardization

  • Determine the cleansing rule to standardize data, for example, Street, St. to ST.
  • Use data standardizing tools such as address doctor, Trillium or any other third party tool.

Determine the Fuzzy Match Key

The basic rules mentioned below about defining the fuzzy match key include:
  • OrganizationName: If the data contains the organization names or both organization names and the person's name 
  • PersonName: If the data contains person names only
  • AddressPart1: If the data contains only the address

Tuning the Potential match candidates

a) Key Width:
  • For less SSA indexes, reduce the key width to ‘Preferred’  
  • For more match candidates, use the key width as ‘Extended’ 
b) Search Level: 
  • For less SSA ranges use the search level as ‘Narrow’
  • For more candidates to match use search level as ‘Exhaustive’
  • Use ‘Typical’ search level for business data
  • To match most candidates, use search level as ‘Extreme’. It has performance issues associated with it. 
c) Match Level: 
  • For records that are highly similar and which should be considered for a match, use match level as ‘Conservative’
  • Use match level as ‘Typical’ for most matches
  • Match level ‘Loose’ is better for manual matches to ensure that tighter rules have not missed any potential matches
d) Define the fuzzy match key columns that have more unique data, e.g. the Person Name or the Organization Name

e) Data in the fuzzy match key columns should not contain nulls. Nulls (SSA_KEY is K$$$$$$$) are potential candidates for each other. 

Use the range query as below and review the SSA_DATA column for all the qualifying candidates-

SELECT DISTINCT ROWID_OBJECT, DATA_COUNT,SSA_DATA, DATA_ROW
FROM C_PARTY_STRP
WHERE SSA_KEY BETWEEN ‘YBJ>$$$$’ AND ‘YBLVZZZZ’
AND INVALID_IND = 0
ORDER BY ROWID_OBJECT, DATA_ROW

Cleanse Server logs

Cleanse server logs help to determine long running ranges. These long running ranges normally have more candidates to match. Isolate such ranges by looking into the cleanse server logs. Normally, production is a multi-threaded environment, so determine the rangers for these threads. Analyze which thread is taking more time and take out those records from the matching process and re-run the match job.

The video below provides more details about the match rule tuning -




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