# Showing analysis results

Once you finalize creating the column analysis and setting the indicators and patterns, you can execute it and display analysis results in tables and charts.

## Before you begin

* You have opened the Profiling perspective in Talend Studio.
* You have created a column analysis. For further information, see [Identifying anomalies in data](https://help.qlik.com/talend/en-US/studio-getting-started-guide-big-data-platform/8.0/identifying-anomalies-in-data "The use case explains how to use the Profiling perspective of Talend Studio to analyze customer email addresses and phone numbers. It uses out-of-box indicators and patterns on the columns and shows the matching and non-matching address data.").

## Procedure

1. In the Analysis Parameters, select Java from the Execution engine list to run the analysis with the Java engine. For more information, see [Using the Java or the SQL engine](https://help.qlik.com/talend/en-US/studio-getting-started-guide-big-data-platform/8.0/using-the-java-or-the-sql-engine_t.html "From the Advanced settings of an analysis, you can use either the Java or the SQL engine to execute your analysis.").
2. In the analysis editor, press F6 to execute the analysis or click the Run button.

   The editor switches to the analysis results. The analysis results show the generated charts for the analyzed columns accompanied with tables that detail the statistic and pattern matching results.

   The results for the Email column look as the following:

   ![Percentage and charts for the Email column](https://help.qlik.com/talend/en-US/studio-getting-started-guide-big-data-platform/Resources/images/gs-dq-analysis_results.png)

   The results for the Phone column look as the following:

   ![Percentage and charts for the Phone column](https://help.qlik.com/talend/en-US/studio-getting-started-guide-big-data-platform/Resources/images/gs-dq-analysis_results2.png)

## Results

The result sets for the Email and Phone columns give the count of the records that match and those that do not match the standard email pattern and the standard US phone numbers respectively. The results also give the blank and duplicate counts. This shows that the data is not very consistent and that it needs to be corrected.