Data Profiling with R - The R Project for Statistical Computing
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Data Profiling with R - The R Project for Statistical Computing
http://www.loyaltymatrix.com. Data Profiling with R. 2006. Discovering Data
Quality Issues as Early as Possible. Agenda. 2. • Background & Problem
Statement.
Agenda
2006
Data Profiling with R Discovering Data Quality Issues as Early as Possible
• • • • • •
Background & Problem Statement High Level Design R & SQL Integration Examples Grid Graphics for Summary Panels Some Real World Data Profiles Demo Profile Run (After Break)
June 2006 Loyalty Matrix, Inc. 580 Market Street Suite 600 San Francisco, CA 94104 (415) 296-1141 http://www.loyaltymatrix.com 2
Loyalty Matrix Background
MatrixOptimizer®: Architecture Overview
• 15-person San Francisco firm with an offshore team in Nepal • Provide customer data analytics to optimize direct marketing resources • OnDemand platform MatrixOptimizer® (version 3.1) • Over 20 engagements with Fortune 500 clients • Deliver actionable marketing actions based on real customer behavior
3
4
MatrixOptimizer®: Profile Point
Ideal Data Profiler Requirements •Require minimum input from analyst to run •Intuitive output for DB pros – share results with client DBA. •Column profiling (each column treated independently) •Simple statistics & plots •Patterns, exceptions & common domain detection
Profile Here
•Dependency profiling for intra-table dependencies •Redundancy profiling for between table keys, overlaps •Easy to use reports for analysts & clients •Save findings in accessible data structure for subsequent use •Low Cost or “Free” •See: Data Quality – The Accuracy Dimension by Jack E. Olson
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Profiling Data Flow
Profiler Output Panel Summary Counts & %’s
AMA_Stage . RECEIVABLE_TXN . X_ASOF_DATE # %
Rows 3,861,249 100.00
Nulls 2,908,244 75.32
Distinct 28,564 0.74
Empty 0 0.00
Numeric 0 0.00
19
Date 953,005 100.00
Min. 1st Qu. Median Mean 3rd Qu. Max. 2003-01-14 2003-08-12 2003-12-29 2004-02-12 2004-09-08 2005-10-17
R DataProf
varchar(8000)
Distribution of X_ASOF_DATE 4 e+05
Staging Tables
Header: Database details for field
# Rows
Add Identity, source & load batch fields
Empty, Numeric & Date only for character strings
0 e+00
Cleint Data Source
VARCHAR image of source data retaining client field names with just identity column (PK), source key & load batch key added.
2003
2004
2005
2006
Head: NA|NA|NA|NA|NA|NA
MetaData
Summary Stats if numeric or date
Profile Report
Appropriate plot type Footer: Notes about field
Saved as .wmf in Plots sub-folder. 7
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High Level Design • User picks database to profile • User selects specific tables or • For all selected tables: – For all columns in table: • Get basic stats • Select most appropriate plot type – Get data for plot
• Write panel text & plot to .wmf
R & SQL Integration (1) • Let SQL do the heavy lifting & minimize data sent to R • RODBC also reads tables & columns: odbcTables(cODBC) lTables
