Standardize Data with Mapping Tables
Imagine you are in the Report Builder building a Revenue by State report. Everything is going well until you try to add a billing state grouping to your report and you see this:
How could this happen?
Unfortunately, a lack of standardization can sometimes lead to messy data and headaches when building reports. In this example, there may not have been a dropdown menu or standardized way for your customers to input their billing state information. This lead to various values - pa, PA, penna, pennsylvania, and Pennsylvania - all for the same state, which leads to some strange results in the Report Builder.
It is possible that there is a tech resource that can help you clean up the data or insert the columns you need directly into your database. If not, there is another solution - the mapping table. A mapping table allows you to quickly and easily cleanse and standardize any messy data by mapping data to a single output.
How do I create it? how
Data formatting refresher:
- Make sure that your spreadsheet has a header row.
- Avoid using commas! It causes problems when you upload the file.
- Use the standard date format
(YYYY-MM-DD HH:MM:SS)for dates. - Percentages must be entered as decimals.
- Make sure any leading or trailing zeroes are properly retained.
Before you dive in, Adobe recommends that you export the raw table data. Looking at the raw data first means you can explore all possible combinations for the data you need to clean up, thus ensuring that the mapping table covers everything.
To make a mapping table, you need to create a two-column spreadsheet that follows the formatting rules for file uploads.
In the first column, enter the values stored in your database with only one value in each row. For example, pa and PA cannot be on the same line - each input needs to have its own row. See below for an example.
In the second column, enter what these values should be. Continuing with the billing state example, if you want pa, PA, Pennsylvania, and pennsylvania to simply be PA, you would enter PA in this column for each input value.
What do I need to do in Commerce Intelligence to use it? use
After you have finished creating the mapping table, you must upload the file into Commerce Intelligence and create a joined column that relocates the new field into the desired table. You can do this after the file is synced to your Data Warehouse.
This example moves the column that you created on the mapping_state table (state_input) to the customer_address table using a joined column. This allows us to group by the clean state_input column in your reports instead of the state column.
To create the joined column, navigate to the table to which the field will be relocated in the Data Warehouse Manager. In this example, this would be the customer_address table.
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Click Create a Column.
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Select
Joined Columnfrom theDefinitiondropdown. -
Give the column a name that differentiates it from the
statecolumn in your database. Name the columnbilling state (mapped)so you can tell which column to use when segmenting in the report builder. -
The path you need to connect the tables does not exist, so you need to create a one. Click Create new path in the
Select a table and columndropdown.If you are not sure what the table relationship is or how to properly define the primary and foreign keys, check out the tutorial for some help.
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On the
Manyside, select the table you are relocating the field to (again, for us it iscustomer_address) and theForeign Keycolumn, orstatecolumn, in the example. -
On the
Oneside, select themappingtable and thePrimary keycolumn. In this case, you would select thestate_inputcolumn from themapping_statetable. -
Here is a look at what the path looks like:
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When finished, Click Save to create the path.
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The path may not populate immediately after saving - if this happens, click the
Pathbox and select the path you created. -
Click Save to create the column.
What do I do now? wrapup
After an update cycle completes, you will be able to use your new joined column to properly segment your data instead of the messy column from your database. Look at your grouping options now - no more stress mess:
Mapping tables are handy for any time that you want to clean up some potentially messy data in your Data Warehouse. However, mapping tables can also be used for some other cool use cases, like replicating your Google Analytics channels in Commerce Intelligence.