Menu

Written By: Russ Artzt

#ArtztOnData: Data Orchestration and Process Orchestration in RevOps

Revenue Operations (RevOps), the new(ish) concept gaining traction in sales and marketing departments, synergizes people and technology to streamline the revenue growth process. What can get lost in discussion about this promising trend, however, is the role of process orchestration, data orchestration and system integration that undergirds the entire endeavor.

What is RevOps?

RevOps, while having more than one working definition, is primarily an alignment between sales, customer success and marketing operations across the full customer life cycle. Its goal is to increase revenue growth by upping the operational efficiency of all three groups. RevOps breaks down silos, enabling improved collaboration between teams and more predictable business performance. It cuts down each department’s overhead and speeds up their respective workflows.

The RevOps trend is part of a bigger picture, one of several “Ops” phenomena that are changing the way businesses work—along with the relationship between businesses and their IT departments. DevOps, for example, unites software development, testing and IT operations to streamline and accelerate the development of new software features. When RevOps and DevOps meet patterns like Data Ops and Sales Ops, some analysts refer to this as BigOps. These are transformative changes in business operations that leverage technology to change the way things get done, for the better.

How Process Orchestration Enables RevOps

The “Ops” in RevOps refers to business operations. In practical terms, this means business processes, each of which relies on an underlying IT system. Thus, as a cross-departmental activity, RevOps requires integration and orchestration between multiple groups of people, business processes and systems. For instance, overseeing a marketing campaign and ensuring a great customer experience—and revenue—through the customer lifecycle means achieving continuity for the customer as he or she is managed on various systems.

The customer might first appear on a Customer Relationship Management (CRM) system, then a marketing automation solution, then sales and order management, then, warehouse management, and so forth. For RevOps to achieve its goal, the customer interaction must be seamless as the prospect turns into a customer with a transaction that needs to be fulfilled. This is all about application integration and process orchestration. It’s also a matter of data. The data has to match as the customer moves through the integrated systems that underpin RevOps.

How Data Orchestration Makes RevOps’ Process Orchestration Possible

When each of RevOps’ component groups worked independently, having discrepancies between data sets in sales, marketing and customer service was problematic. However, life still went on. With RevOps, however, there can no longer be divergence in data in the respective groups and systems that comprise the new way of doing things. If data is dirty, contains errors and duplicates, it will disrupt RevOps. If the data is not normalized, RevOps simply won’t work.

RevOps needs consistent, accurate and normalized data. Consider the following hypothetical situation: The CRM system records a customer’s state using a two-letter abbreviation like WA or MA. The ERP system, which handles sales orders, uses variable state descriptors, like Wash or Mass. This may seem like a minor issue, but the whole point of RevOps is to eliminate manual correction of errors as well as embarrassing mistakes in customer communications. Now, imagine that there are 10 customer data fields that don’t match, and a million customer records to handle. The problem starts to show its toxic potential. It’s a massive headache, one that will make RevOps impossible to realize.

How can this problem be solved? The answer is a process known as data orchestration. Data orchestration at its core addresses the issues that arise with data silos. As data enters the customer lifecycle, a data orchestration solution automatically normalizes it. That way, Wash becomes WA every time, across all systems. An automated solution also de-duplicates records, fixes errors and fills in missing fields without human intervention. It works behind the scenes. The solution can also enrich data, adding useful pieces of information like SIC codes and zip+4 postal codes. When the data orchestration solution is integrated with data exchanges, it can correct out-of-date job titles and work addresses.

A data orchestration solution can also route data to the correct system and user. This is helpful in RevOps, as the process inevitably brings together people from different groups, each of whom have different data needs. The data orchestration solution can make sure that the right person sees the data at the right time to move the RevOps process closer to achieving its goals.

Conclusion

The business world is embracing RevOps. It’s doing so because it makes business sense. Operating expenses come down as collaboration goes up. Revenue growth improves as customer engagement gets better. Making it work is a technological and organizational challenge. Integrating work groups also means integrating systems—with the need for clean, normalized data emerging in parallel. As business processes get orchestrated across multiple systems, data orchestration has a role to play, too. The data orchestration solution ensures that the data needed for RevOps, with its orchestrated processes, is of high quality and in the right format to make it all work as envisioned.

To learn more about RingLead’s intelligent data orchestration solution, visit us here.

Check out other RevOps Blogs:

#ArtztOnData: Fitting Data into RevOps Frameworks
#ArtztOnData: Understanding the Role of Data in RevOps