- G (green)
- A (amber)
- R (red)
In most cases, the red flag will have been assigned because of a discrepancy between the company name and the other data points you selected as matching criteria. Depending on the quality of your file, these other data points may be less accurate than the name. If so, you may want to review the red flags.
The amber flags are usually caused by the absence of incorporation types (limited, PLC, etc.) in the company name in your file but in some cases there may be other causes.
The easiest way of verifying that DataWorks has found the company you are looking for is by looking at the company on DueDil by using the DueDil company URL (“DueDil company profile” column).
Step 1: Create project and import CSV
Click “Create new project”. If you already have projects, the button will be in the top right of the page. Later, you will see your projects on the left navigation panel.
Select a CSV file to upload. The maximum number of rows that you can upload for a single project is 100,000.
You can now see the file you have uploaded.
Step 1.2: Configure matching criteria
Select whether your file has a header row or not. A preview of your file will be displayed.
You will need to choose the correct header for each field in your file. Select the header from the dropdown above each column. If you are missing some of these fields, you can skip them.
If your file contains a CRM ID and/or a DueDil Universally Unique Identifier (UUID), you will need to ensure the correct headers are chosen for these columns. The CRM ID is used to link parent/child companies inside your file. You will only have a DueDil UUID if you have already used DataWorks (the DueDil UUID is included in the output) and selecting it at this step will make matching faster.
Tip: The quality of the data points that are used for matching is important. For example, if your phone numbers are mobile numbers of contacts in the company and not the ‘main’ phone numbers, it won’t help to match on them.
Tip: Take the time to add a country column to your data if possible; the accuracy of the matching will be improved significantly.
Once matching is complete, you will be notified by email.
Step 1.3: Matching complete
On the Results summary tab, you can see the number of total rows and matched rows, as well as unique companies that were matched against DueDil data.
Step 1.4: Matching summary
You can click on the the ‘Results sample’ tab to see a sample of how your data file matched our records (maximum of 100 rows).
You must confirm that you are satisfied with the matching. By clicking yes, you will no longer be able to change your file or match criteria in the project.
The number of matched rows will be deducted from your quota at this point.
Step 2: Enrich and Cleanse
You now have the option to add group information (including global and domestic ultimate parents) directly into your file.
If you choose “Add all missing parents.”, you will get all intermediate parents, as well as domestic ultimate and global ultimate parents.
You have the option to remove any duplicate records from your file. If you chose not to remove duplicates, they will still be flagged for you to manage later.
Step 3: Append columns
You have the ability to enrich your file with additional DueDil information. The rows marked with an arrow are expandable should you need to refine your options, you can also search for specific fields at the top of the table.
You can always return to this step so don’t worry about getting this right first time. You can also choose to add all rows and filter the relevant ones in the export if you prefer.
Step 3.2: Additional Files
You have the option to append information as additional files.
Step 3.3: Download
Your CSV file(s) are now ready for download as a zip.
Your input data comprises the first columns in the file, information about the match (duplicate flags, sources) are in the next columns and then the DueDil company data will be in the final columns.
Step 3.4: Match flagging
To help you review the output data from DataWorks, we provide a “Match quality traffic light” column. The possible values here are:
Typically a file should contain a very small number of red matches, a small number of amber matches and the vast majority will be green. If your data is particularly dirty then the number of red and amber rows may be more significant. If matching accuracy is critical to your use-case, we recommend manually checking the red matches and possibly the amber matches too.