MatchUp Hub:Data Considerations - Revision history

Admin at 14:44, 2 October 2018

2018-10-02T14:44:41Z

← Older revision		Revision as of 14:44, 2 October 2018
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	In the case of Table 2, checking your data and an identifying an extensive amount of NULL values can also be a source of clustering issues. You can check this by using one of our Profiler products to check for NULL/Empty values, as well incorrect data types in columns. Passing your data through an address verification service in order to correct empty field values can help fix bad zip/addresses. For other NULL data types we suggest an alternate matchcode before deduping or splitting the data into multiple threads.		In the case of Table 2, checking your data and an identifying an extensive amount of NULL values can also be a source of clustering issues. You can check this by using one of our [[Profiler Object\|Profiler products]] to check for NULL/Empty values, as well incorrect data types in columns. Passing your data through an address verification service in order to correct empty field values can help fix bad zip/addresses. For other NULL data types we suggest an alternate matchcode before deduping or splitting the data into multiple threads.

	===Table 2===		===Table 2===

Admin at 21:18, 27 September 2018

2018-09-27T21:18:47Z

← Older revision		Revision as of 21:18, 27 September 2018
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	==Data Considerations==		==Data Considerations==
	After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[~~MatchUp Hub:Environment~~\|~~Environment~~]]) Users can still experience problems with slow processing speeds due to bad data.		After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[Matchcode Optimization\|Matchcode Optimization]]) Users can still experience problems with slow processing speeds due to bad data.

Admin at 22:20, 25 September 2018

2018-09-25T22:20:38Z

← Older revision		Revision as of 22:20, 25 September 2018
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	==Data Considerations==		==Data Considerations==
	After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[MatchUp Hub:Environment\|Environment]]) Users can still experience problems with slow processing speeds due to bad data.		After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[MatchUp Hub:Environment\|Environment]]) Users can still experience problems with slow processing speeds due to bad data.


	[[File:MCO_DataConsiderations.png\|link=]]		[[File:MCO_DataConsiderations.png\|link=]]


	In MatchUp, clustering is made possible when we have at least one component common to all used matchcode combinations. Since ZIP5 is used in all matchcode combinations, built keys will be grouped into different clusters based on that datatype. Therefore, if you have a database which contains uneven distribution of ZIP codes, as in the table below, changing your matchcode to include LAST NAME, for example, would create better clustering.		In MatchUp, clustering is made possible when we have at least one component common to all used matchcode combinations. Since ZIP5 is used in all matchcode combinations, built keys will be grouped into different clusters based on that datatype. Therefore, if you have a database which contains uneven distribution of ZIP codes, as in the table below, changing your matchcode to include LAST NAME, for example, would create better clustering.

Admin at 22:20, 25 September 2018

2018-09-25T22:20:26Z

← Older revision		Revision as of 22:20, 25 September 2018
Line 3:		Line 3:
	==Data Considerations==		==Data Considerations==
	After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[MatchUp Hub:Environment\|Environment]]) Users can still experience problems with slow processing speeds due to bad data.		After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas\|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[MatchUp Hub:Environment\|Environment]]) Users can still experience problems with slow processing speeds due to bad data.

			[[File:MCO_DataConsiderations.png\|link=]]

	In MatchUp, clustering is made possible when we have at least one component common to all used matchcode combinations. Since ZIP5 is used in all matchcode combinations, built keys will be grouped into different clusters based on that datatype. Therefore, if you have a database which contains uneven distribution of ZIP codes, as in the table below, changing your matchcode to include LAST NAME, for example, would create better clustering.		In MatchUp, clustering is made possible when we have at least one component common to all used matchcode combinations. Since ZIP5 is used in all matchcode combinations, built keys will be grouped into different clusters based on that datatype. Therefore, if you have a database which contains uneven distribution of ZIP codes, as in the table below, changing your matchcode to include LAST NAME, for example, would create better clustering.

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==Data Considerations==
After making sure your environment is setup correctly to run MatchUp ([[MatchUp Hub:Environment#Evaluation Areas|Environment Evaluation Areas]]) and your matchcode has been evaluated and optimized ([[MatchUp Hub:Environment|Environment]]) Users can still experience problems with slow processing speeds due to bad data.

In MatchUp, clustering is made possible when we have at least one component common to all used matchcode combinations. Since ZIP5 is used in all matchcode combinations, built keys will be grouped into different clusters based on that datatype. Therefore, if you have a database which contains uneven distribution of ZIP codes, as in the table below, changing your matchcode to include LAST NAME, for example, would create better clustering.

===Table 1===
:{| class="alternate01"
!RECID!!LAST NAME!!ADDRESS!!CITY!!ZIP
|-
|1||Jones||12 Main Street||Boston||02125
|-
|2||Smith||57 Maple Lane||Boston||02125
|-
|3||Connor||34 Summer Street||Boston||02125
|-
|4||Williams||1 Oak Drive||Boston||02125
|-
|n||***||***||***||02125
|}

In the case of Table 2, checking your data and an identifying an extensive amount of NULL values can also be a source of clustering issues. You can check this by using one of our Profiler products to check for NULL/Empty values, as well incorrect data types in columns. Passing your data through an address verification service in order to correct empty field values can help fix bad zip/addresses. For other NULL data types we suggest an alternate matchcode before deduping or splitting the data into multiple threads.

===Table 2===
:{| class="alternate01"
!RECID!!LAST NAME!!ADDRESS!!CITY!!ZIP
|-
|1||Jones||12 Main Street||Boston||NULL
|-
|2||Smith||57 Maple Lane||Boston||NULL
|-
|3||Connor||34 Summer Street||Boston||NULL
|-
|4||Williams||1 Oak Drive||Boston||NULL
|-
|n||***||***||***||NULL
|}

Table 3 shows a good example of data that has first been standardized and verified before processing with matchup. Due to the clustering aspect of ZIP codes, the data below will be grouped into two sections which will provide much better processing speeds.

===Table 3===
:{| class="alternate01"
!RECID!!LAST NAME!!ADDRESS!!CITY!!ZIP
|-
|1||Jones||12 Main Street||Boston||02125
|-
|2||Smith||57 Maple Road||Boston||02125
|-
|3||Connors||3 Summer Circle||Boston||02121
|-
|4||Williams||17 Oak Drive||Boston||02121
|-
|n||***||***||***||***
|}

[[Category:MatchUp Hub]]