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      Importing data into Edda Dealflow

      This article will guide you through the data import process on Edda dealflow.

       

      Whether you are new to Edda and would like to try importing your first sample of companies, or a long time user, wanting to edit data on a batch of companies. The data import feature is the way to go.

      This guide will give you everything you need to know regarding the data import feature within Edda Dealflow. This article will cover:

      • Pre-requisites and must knows before importing data

      • Preparing your Dealflow

      • Preparing your data set

      • Data validation rules

      • How to edit batches of data using this feature

      • This feature's purpose is to let Edda users import data to their Dealflow in an automated way. This feature is available to every user, providing you with the ability to easily upload batches of deals to the platform.

      • Once your import has been completed, it cannot be reverted or cancelled. However if you can identify the imported deals, you can batch delete them from within the table view feature in Dealflow.

      • Your first data import is always a good occasion to clean your historical data, performing actions such as: removing duplicates, unnecessary data, outdated deals etc...

      Preparing your Dealflow

      • We recommend creating your custom fields and setting up your pipelines before starting your imports. This will guarantee our Dealflow system is fully ready to process and host it.

      • When creating your custom fields, ensure the correct data types have been chosen for each field.

      Preparing your data set

      • Once your custom fields have been created and our Dealflow system is ready to host your data, you need to navigate to the data import feature from within the administration panel.

      • The first step is to download the correct template for your pipeline. Select your destination pipeline in the dropdown and download the XLS file formatted to fit your pipeline criteria.

      • The next step is to complete this template with your data (some example data should be present in the fields, these can be deleted and you can start afresh).

      • We highly recommend trying the process with a small sample of data before importing hundreds/thousands of deals.

      Validation rules whilst completing the template

      To ensure the accuracy of your imports, the system has validation rules for the different types of data in your spreadsheet.

      The structure below is: "Type of field" (Examples of fields using this type): Validation rules"

      Short text field (Taxi ID...) : Plain text field which can contain any type of data (less than 500 characters).

      Long text field (Description) : Plain text field which can contain any type of data (higher character limit than a short text field).

      Date field : The dates have to conform to the format of : MM/DD/YYYY

      Link field (website...) : It has to contain a URL, which begins with "http://"

      List field (Activity, country...) : For the list field to be validated, there should be one item per cell. There cannot be multiple data points for one list field and it should exactly match the list items from the Dealflow. During validation, you will also be able to add unmatched fields.

      Tag field (Investors, Keywords...): Tag field allows multiple values for import that need to be separated by commas.

      Amount field (Amount to commit, Pre-money Valuation...): For example, if you want to input 2 million, the system accepts the data to be formatted by either: 2M or 2,000,000.

      Importing your data:

      1. Sign in to Edda Dealflow and go to settings -> Data import

      2. From there, you will have to choose a destination pipeline (in which the data will be uploaded)

        * If the selected pipeline has an automation rule set, please disable it before starting the data import and then re-activate it after the import is complete.

      3. Download the custom provided template specific for that pipeline.

      4. Complete the template with your data

      5. Choose how duplicated data is handled e.g. multiple companies with the same name and information. (more details below)

      6. The next step will be the matching of all the fields from your file, with the fields from your Dealflow (ensuring the validation can be made)

      About the "For existing data" or duplicates in the file section

      We are matching companies based on their exact name. It can occur that in your import file, there may be:

      • Companies that already exist in your Dealflow with the same information (full duplicates)

      • Companies that already exist in your Dealflow with different information (Partial duplicates)

      This is why we decided to provide four options in order to proceed with these :

      1. Overwrite existing values: In case of duplicated data for a company that already exists, this will replace the existing data with the newly imported data. This might be useful when you want to modify the data for multiple companies. We will provide an example of this further in this article.

      2. Create duplicate: Probably the safest way to go if you are not certain. If companies do have the same name, they will be imported as totally new companies. Multiple versions of this company will now exist in your pipeline.

      3. Skip records: This option will simply detect the duplicates from the imported file and exclude them from the import.

      4. Add them as repeat submissions: Finally, choosing this option will trigger the "Repeated submission feature" which will add a new company to an existing one. Both will be completely separated and will keep their own data. They will be stored under the same company but kept separate through the "submissions" tab.

      Any company names that do not match exactly will not be considered duplicates, e.g. Airbnb will not be matched as a duplicate to AirBnb Inc.

      Fields mapping and data validation process

      Data validation is the core part of the data import feature, this is where the magic happens. We encourage you to first try the validation with a small sample of data.

      As mentioned above, the cleaner the data, the better your Dealflow will be. This is why we have a data validation process in place, whether you want to import dates, numbers, lists, or tag values, everything has to be formatted the correct way.

      For each field/column it will be necessary to :

      • Select the correct field to import the information to (each field in the validation dropdown is a field from your Dealflow)

      • Discover any validation matching errors (the system detects them automatically)

      • Correct any errors which are displayed

      • Validate and complete the import

      Once every field has passed validation, you can validate and move on to the final check verification.

      Add/replace/remove values using the Data import feature on Dealflow

      One of the great things this feature provides is to let users edit batches of companies locally using an excel spreadsheet, and re-importing them to your Dealflow. Here is a use case :

      You've added a new qualification keyword to the Dealflow and would like to apply it to several companies at once. These are the steps to achieve this in a few minutes:

      1. Export your pipeline as an XLS file (from the pipeline selector)

      2. Delete all other columns except for the company name (used for matching, VERY IMPORTANT) and Keywords.

      3. Proceed to make your changes (adding the keyword to several companies).

      4. Once your modifications are completed, navigate to the import module, choose the right pipeline and upload your file.

      5. In the "For existing companies" section, choose the option: overwrite existing values -> This is a key step.

      6. Then hit "next step", validate the columns and finish the import.

      Once your import has been fully processed, you can verify the result and see that your keywords have been successfully applied to all the edited companies.

      We hope that this document helped. Don't hesitate to suggest some modifications to this article.