You might encounter problems with how data has been structured or formatted that you must fix prior to providing the content to your target system. You can use the methods in this section to locate problems with the content or data typing of your data. 

Locate mismatched values

When  evaluates a dataset sample, it interprets the values in a column against its expectations for the values. Based on the column's specified data type and internal pattern matching, values are categorized as valid, mismatched, or missing. These value categories are represented in a slender bar at the top of each column. 

In the data quality bar, mismatched values are identified in red:

Tip: Before you start performing transformations on your data based on mismatched values, you should verify the data type for these columns to ensure that they are correct. The type against which values are checked is displayed to the upper left of the data quality bar. Below, the data type is ZIP for U.S. Zip code data. For more information, see Supported Data Types.

Mismatched values in red

Mismatched values can be sourced from a variety of issues:

Tip: When cleaning up bad data, you should look to work from bigger problems to smaller problems. If a higher percentage of a column's values have been categorized as mismatched data, it may indicate a wider problem with the data. In affected rows, verify if other columns' values are also mismatched. These rows should be reviewed and fixed first. When fixed, other mismatches may be fixed in other rows, too.

To locate data:

NOTE: Remember that you are working on a sample of your data. If the sample indicator at the top left corner of the Transformer page does not indicate Full Data, then some values in your full dataset may not be represented in the sample displayed in the grid.

Methods for fixing mismatched data

When you discover mismatched data in your dataset, you have the following basic methods of fixing it:

  1. Change the data type. If the percentage of mismatched rows is significant, you may need to change the data type for a better match. 

  2. Replace the values with constant values. This method works if it is clear to you that the values should be a single, consistent value. Select the mismatched values in the column, and then select one of the highlighted mismatched values. Use the replace transform to change the mismatched values to corrected values.

    Tip: One easy way to fix isolated problems with mismatched values is to highlight a mismatched value in the data grid. A new set of suggestions is displayed. You can select the replace suggestion and then modify it to include the replacement value.

  3. Set values with other columns' values. You can use the set transform to fix mismatched values by replacing them with the corresponding values from other columns.

  4. Use functions. Data can be fixed by using a function in conjunction with the set transform to replace mismatched values.

  5. Delete rows. Select the mismatched values and use the delete transform to remove the problematic rows.
  6. Hide the column for now. You can remove the column from display if you want to focus on other things. Select Hide from the column drop-down. Note that hidden columns appear in any generated output. 
  7. Delete the column. If the column data is unnecessary or otherwise unusable, you can delete the entire column from your dataset. Select Delete from the column drop-down.

Tip: Delete unnecessary columns as early as possible. Less data is easier to work with in the application and improves job execution performance.

NOTE: You might need to review and fixed mismatched data problems multiple times in your dataset. For example, if you unnest the data, additional mismatches might be discovered. Similarly, joins and lookups can reveal mismatches in data typing.

Mismatched values in transform code

In your transforms, mismatched data can be identified references as in the following:

Note that the single quotes are important around the value, which identifies the value as a constant.

Tip: In the above, note that the value Zipcode identifies the data type that is used for matching the column values. In this case, for greater specificity, you might want to identify the mismatched values in the column against the data type Integer, since all U.S. postal codes are positive integers. For more information on how to explicitly reference data types in your steps, see Valid Data Type Strings.

Trim data

To trim whitespace out of a column, use the following transformation:

The $col token is a reference to the column name to which the formula is being applied. For more information, see Source Metadata References

This step may increase the number of missing values (for values that contain only whitespace characters) and the number of instances of matching values (for values that have spaces before and after an alphanumeric value).

You can modify the above transformation to trim leading and trailing spaces across all columns in your dataset. The wildcard (*) applies the formula to all columns in the dataset.

You can extend the above transformation further by removing any leading or trailing single- and double-quote marks using the TRIMQUOTES function wrapped around the TRIM reference:

Tip: Keep in mind that nested functions are evaluated from the inside out. In this case, the TRIM function is evaluated first, which removes any surrounding whitespace. Then, the TRIMQUOTES function is applied.

Set values using other columns

You can use values from other columns to replace mismatched values in your current column. Using the previous example, mismatched postal codes are replaced by the corresponding value in the parent entity's postal code column (parent_postal_code):

Use functions to fix mismatched values

In your transforms, you can insert a predefined function to replace mismatched data values. In the following example, the value for mismatched values in the score column are computed as the average of all values in the column:

Tip: You can also use the IFMISMATCHED function to test for mismatched values. Unlike the above construction, however, IFMISMATCHED does not support an else clause when the value does match the listed data type. For more information, see IFMISMATCHED Function.

Bad data typing

Tip: Particularly for dates, data is often easiest to manage as String data type. has a number of functions that you can deploy to manage strings. After the data has been properly formatted, you can change it to the proper data type. If you change data type immediately, you may have some challenges in reformatting and augmenting it. Do this step last.

For columns that have a high percentage of mismatched values, the column's data type may have been mis-assigned. In the following example, a column containing data on precipitation in inches has been mis-typed as Boolean data:

Mis-typed column data type

To change a column's data type, click the type identifier at the top of the column and select a new type. In this case, you would select Decimal.

NOTE: After you change the type, review the data quality bar again. If there are still mismatched values, review them to see if you can categorize the source of the mismatch.  

As you can see in the previous example, the precipitation column contains values set to T, which may be short for true. When the data type is set to Decimal, these values now register as mismatched data. To fix, you can replace all T values with 1.0 using the set transform.

Select an instance of T in the column and click the Set suggestion card. Click Modify. For the value in the transform, enter 1.0. Your transform should look like the following:

Tip: If possible, you should review and refer to an available schema of your dataset, as generated from the source system. If the data has also been mis-typed in the source system, you should fix it there as well, so any future exports from that system show the correct type.