An implementation of data auditing for relational and/or ad-hoc reporting systems. Helps establish a glide path toward a more dimensional solution.

1. Jim Atwater
Principal Consultant
Management Analytics Practice
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

2. Ad-hoc reporting processes are largely relational,

aspiring to a more dimensional model
 More than one data source is involved, including
relational databases, spreadsheets, SharePoint lists
and flat files
 Source data is staged to a relational database and
reported using standard tools like Excel and
PowerPoint
 Data Quality is a concern, guided by a need for “One
version of the truth”
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

3. McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

4. No “T” in the “ETL” means:

 no auditing or error handling
 no added value until the report is generated
 analysts end up doing the same work every reporting cycle
QA and formatting step actually adds errors

 Column names
 User-defined aggregations
 Macros and calculated fields
Violates the prime directive

 “One version of the truth” -> “None versions are the truth”
 Automation only automates the errors
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

5. McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

6. Screen Definitions

 A screen is a specific type of an automated test case
 Screens can validate (among other things)
 physical or logical structure
 atomic-level data values
 logical aggregation values
 Screens enforce data quality
 Identify errors and score their severity
 Feed the Error Event Fact Table
Screen Order

 Allows screens to be run in parallel for better performance
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

7. Staged data from source systems

 Data quality begins and ends with the source systems
 Composed of feeds from source systems
 Driven by source system “data map” analysis
Run screens

 Run in groups to optimize performance
 Consumed by master screen-processing codebase for supportability
Data quality metrics

 Screen definition
 Data quality score
 Exception action
 Screen type and category
 Screen metadata (elapsed time, row count, byte count)
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

8. Error events

 Star-schema data structure
 Source-system feed data rows are at the fact level
 Main facts are the screen definition key and the severity score
 Primary consumer is the Information Steward
 Source of the Data Quality detail report
Audit dimensions

 One dimension table for each report data source
 One row for each class of error each time the report is updated
 Integrated with the report data deliverables
 Consumed by users as well as the Information Quality Lead
 Acts as a physical guarantee of “one version of the truth”
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

9. Source System Owners

 Owns data provided by the source systems
 Works with Information Steward to define strategy and reports
 Works with Information Quality Lead to define and enforce data
quality metrics
Information Steward

 Owns relationships with report consumers to define reports and
select data sources
 Owns relationships with source system owners to define feeds
Information Quality Lead

 Owns business rules for data quality metrics
 Owns relationship with source system owners to define and
enforce data quality metrics
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

10. Key dependencies

 Diversity of reports
 Quantity and complexity of data sources
 Responsiveness of source systems
Milestones

 Identify and automate of reports
 Stack-rank reports by impact of decisions made
 Map data from sources to reports
 Define data quality screens
 Establish infrastructure
 Acquire hardware and staff resources
 Create QA infrastructure
 Data quality process
 Iterative process between Users and Source Systems
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC

11. McKeel Research LLC

 Affiliated with Allyis, Incorporated
 Contact:
 James W. Atwater, 3rd
 Principal Consultant, Management Analytics Practice
 Office: (425) 996-0427
 Cell: (425) 766-0832
 Email/IM: jamesatwater@hotmail.com
McKeel
12/9/2008 All Rights Reserved RESEARCH LLC