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Companies are often surprised by the variety and amount of data necessary to automate revenue, especially under the new ASC 606 accounting standard. Just to capture all the variables, your operations team has to break the process down into pieces and define the data needed for each portion of their decision making.

Consider this scenario: a software company groups orders based on the same customer and sales opportunity. This is deemed to be one contract under ASC 606 for contract price allocation and revenue recognition. The company starts revenue recognition on the later of software fulfillment or go-live.

Even in this simple scenario, there are at least 4 decision points that affect how this company tracks data and reports on revenue:

  • Are orders from the same customer?
  • Are orders coming from the same sales opportunity?
  • When was the software fulfilled?
  • When did the client go-live?

Companies often derive this information from various sources including CRM, ERP, emails, phone calls, contracts, etc.  However, to support automation, data needs to be available and consistent at all times. So, where does data go wrong?

Data does not exist: Often times, the data we need does not exist in any system. Common examples include acceptance criteria or terms that are defined and recorded in the physical contract and manually monitored by members of the accounting or revenue team.

Data is not structured: In this case, the data is captured but not in a structured, defined fashion or it is not structured appropriately. This includes using freeform text fields to capture information (e.g. opportunity ID, contract ID, etc.). This unstructured data is often inconsistent or in a form that is not useable by revenue systems.

Data is inaccurate: Lastly, the data exists and is structured, but is often inaccurate. A simple example here may be dates—these are structured data fields, but can be captured incorrectly due to user-error.

ASC 606 introduces additional data requirements which companies will need to contemplate, for both adoption and automation. Example areas where additional data may be required include:

  • Standalone selling prices
  • Material rights
  • Modifications vs. new contracts
  • Variable consideration
  • Significant financing
  • Costs to obtain and fulfill the contract

All these areas include data critical for both performing revenue recognition going forward and supporting the transition to the new standard (whether full retrospective or modified retrospective).

Here are a few key steps to help you be the Trailblazer for your organization moving forward:

Step 1: Start with defining your use cases. What real scenarios can you identify within your business? What can you anticipate as your business grows?

Step 2: With these use cases, determine the specific rules being applied for revenue recognition. This is often the hardest part of the job. But if it’s done right, implementation becomes easier.

Step 3: Define the exact master data and transactional data you need to support the accounting and rules you have defined.

Step 4: Perform a gap analysis between the data you need and the data that exists. Recall the data challenges we outlined earlier. Where do your data needs fit along that spectrum?

Step 5: Fill your data gaps!

The good news—this approach is mostly "science"—clearly defined steps with a known and specific outcome. However, fixing the gaps is not always as straightforward. Some of the improvements may require process changes outside of the direct control of Finance and Accounting, while others may require an investment in "upstream" systems, for which there is not budget or you are competing with other initiatives.

To support revenue automation, some artful and creative thinking might be necessary for balancing near-term tactical solutions with longer term, large-scale changes. Some practical examples we have seen include:

  • Using data proxies (i.e. other pieces of data that, although not exactly matching what is needed, closely approximate the need). For example, existing financial systems may not capture the implementation go-live date (and in this example, this is deemed to be the trigger to start revenue recognition). However, business practice at the company is to invoice for professional services shortly after the go-live, therefore, the invoice date for the professional service could be used as a proxy for the go-live date.
  • Adding new Finance and Accounting procedures to fix data quality issues or capture additional data.

Our experience has shown that in all revenue automation initiatives there are data gaps. Overlay this with a new revenue standard, and we expect data gaps will be prevalent. However, this presents companies with an opportunity to solve many common data challenges that hamper businesses (e.g. duplicate data, data inconsistencies, limited reporting) while supporting revenue automation—don't let this opportunity pass you by.

Jason Pikoos is a Partner and the Financial Operations Practice Leader at Connor Group, a specialized professional services firm of Big 4 alumni and industry executives.

Want to know more about the art and science of revenue recognition and how to make your data work for you? Check out this e-book.