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The Business Intelligence Structure Framework is multiple-faceted. One aspect particularly important to infrastructure construction is the quality of data that a data warehouse is comprised of. To make a data warehouse the foundation of enterprise intelligence, one has to guarantee data quality.  It is inconceivable to make important enterprise decisions basing on inaccurate and incomplete data. Therefore, one precondition for enterprise intelligence is to have good data quality throughout the environment of a data warehouse.

 

Necessity of Data Quality Analysis

Ø  Without guarantee of the quality of source data, one can not estimate the needed workload and time in data cleaning, nor the resulting cost and profit of data cleaning, hence impossible to prioritize data cleaning work. 

Ø  Without guarantee of the quality of data, one can not determine the effect of dirty data on the reliability of decision-making, hence impossible to identify the scope of data users and data owners.

Ø  If the data in a data warehouse are dirty and inaccurate, query results and statements provided to users will be wrong. And if users use the query results and statements, they may likely make wrong decisions.  Before long they would lose confidence in the data warehouse and refuse to use it anymore.

 

The Effect of Data Quality on BI System

 

Data Quality Analysis Methodology

 

First Step

Second Step

Third Step

Fourth Step

Fifth Step

Determine Scope and Strategy

Analysis on Needs

Define Data Quality Benchmarks

Data Quality Testing

Feedback.

Major Task

Determine the Scope of Source Data in Analysis, Analyze Rules, and Determine Analysis Steps and Tools to be Used

Deep Understanding of Business Needs and Business Analysis Models. Delineate Data Elements subject to Data Quality Analysis.

Work with Business Experts and Project Directors to Determine the Data Quality Benchmarks to Meet for each Data Element

Test Data Quality.  List out Conversion Rules for Data Fail to Meet Data Quality Benchmarks

Communication with Project Director on the Status and Progress of Current Data Quality Analysis

Output

Table of Data Quality Analysis Steps, Table of Analysis Rules

Table of Data Elements subject to Data Quality Analysis

List out the Required Data Quality Benchmarks in Detail in the Table of Data Elements

Table of Test Output

Make Adjustment and Amendment on the Portion Affecting Project Progress

 

Basic process of Data Quality Analysis Strategy

 

Solution to Business Problems

Enhancing data Quality: After data quality analysis, data analysts shall submit a data quality status report, and shall work with ETL designers, data management personnel, business experts, and users to determine the degree of cleanness for data elements and to come up with rules on cleaning dirty data.