When I first started working in the Eloqua space, I came in as a system administrator for a company that had been using Eloqua for approximately a year.  They had already used their maximum number of contact fields and were still requesting more.  As I dug in a little, I learned that there was no data dictionary and no governance established around custom contact fields. Essentially, each business unit had their own set of fields -- many of which were duplicated across business units, but simply had different standard values allowed for each.  This created a significant challenge as we looked at our marketing automation roadmap. We wanted to be able to do things like:

  • create personalized email and landing page templates using dynamic content and field merges
  • automate campaign participation based on field values
  • integrate with other systems (contact centers, sales, CRM, etc)


Eloqua is only as powerful as the data you put in it. Since there is a maximum number of 250 custom contact fields available, it is important you know what custom contact fields you have, where they are used and what is in them to truly be able to automate. This is where a data dictionary comes in.



Our first step in creating a data dictionary was to conduct a complete field audit. We created a list of all custom contact fields -- standard out of the box fields and fields that were being posted to CDO’s.  We added detailed information for each field such as purpose, status, type, standard values, update rule, standard/custom and a brief description of how they are used.


contact overview.png


Categorizations were created and used for the field purpose.  We identified the primary business process for each field field. For example, we created categories for contact profile (primary and secondary), campaign reporting, lead distribution processing, call center integration as well as a few others.



Once we knew what the fields were being used for, it was time to consolidate.  As I mentioned, we had numerous fields that were the same purpose and usage, but because the standard values were different, a unique field had been created.  Any fields that had the same purpose, usage and similar standard values were created. This step was probably the most time consuming because all of the field dependencies had to be cleaned up before the fields could actually be consolidated in Eloqua.  We created standard picklists for the newly consolidated fields and documented accordingly in the data dictionary.


The last piece of our data dictionary included a governance guide on when and where each field could and should be used.  We aligned the contact fields to our lead stages, form strategy, contact center integration and our lead distribution process. For each field and each element, we determined if it was:

  • NA - Not available/not applicable
  • AA - Additional available (optional)
  • R - Required
  • P - Required for processing (used in suppression rules)
  • S - Standard value (applied by Eloqua)

contact details.png


Once we had the data dictionary completed, the field consolidation in Eloqua completed and the new standard values (picklists) created in Eloqua, it was time to put some process governance in place.  We established processes for requesting new fields, regular field and value audits were incorporated into our monthly database health reports and the data dictionary was made available to anyone working on marketing automation campaigns.



When all was said and done, our data dictionary contained three components, the contact field overview, contact field select list (picklist) and the contact field table details. We saw numerous benefits in our Eloqua instance once the data dictionary and standards were in place such as:

  • Email and landing page templates with personalization…we could trust the data!
  • True automation…we knew what fields were available to target in segments, create decision rules within the canvas and report on.
  • System integration and data flows…introducing new integrations was much more efficient and easier to execute when we knew the data details.


  In addition to the immediate benefits we experienced, as time went on we continued to gain benefit from having a data dictionary with other elements of our roadmap and enhancements to our Eloqua instance.


Helpful Marketing Cloud Courses:

B2B: System Integration

B2B: Database Configuration

B2B: Data Cleansing

B2B: Personalizing Campaigns

B2B: Progressive Profiling

RPM: Targeting & Segmentation

RPM: Lead Quality