The term “Data Orchestration” has been picking up momentum among sales and marketing teams in recent years as they realize their data is the foundation of just about everything they want to do and that managing that data needs to be a high priority.

Here’s a common definition:

Data Orchestration is the automation of data-driven processes from end-to-end, including preparing data, making decisions based on that data, and taking actions based on those decisions. It’s a process that often spans across many different systems, departments, and types of data.

Let’s take a look at each of the parts of data orchestration:

1.Preparing Data

Many companies run hundreds, or even thousands of campaigns. When they participate in webcasts, field events, and syndicated content campaigns, the organizer sends over a lead file. Each organizer makes its own decisions about field values like Job Level, Job Function, State, and Country. One organizer may choose to use a two-letter abbreviation for the State field, while others may choose to use the entire state name. Some will choose Job Function values like “Marketing,” “Finance,” and “Sales.”  Others will go a level lower with values like, “PR,” “Marketing Operations,” and “Demand Generation.” Companies need to prepare the data by cleansing, normalizing, and enriching it before entering it into their systems of record, such as their sales and marketing automation systems. If they don’t, they’ll have all kinds of issues later when they do reporting, lead routing, and lead scoring.

2. Making Decisions

After preparing and uploading data into a solution like Salesforce or Marketo, a data orchestration solution can make decisions about that data. This can include decisions like deciding how to score a lead or an account, or making a decision about whether or not to reject a lead record. This decision-making process separates these solutions from others. This rules-based decision-making is beginning to be supplemented with AI models as well.

3. Taking Actions

Rather than simply producing reports and relying on people to do something, data orchestration applications can take action in other systems. For example, this can include identifying a duplicate lead and contact in Salesforce and automatically merging them together with a consistent set of logic to ensure that all the campaigns associated with that lead and contact are retained so that attribution models work properly. Or, a data orchestration system can derive key fields like Job Level and Job Function based on a lead’s title, and automatically assign that lead to the most appropriate nurturing campaign based on that information.

The Evolution of Data Orchestration

Openprise first coined the term back in September 2017 to describe these concepts and to differentiate the Openprise solution from dozens of point solutions that managed specific tasks—like deduplication, lead-to-account matching, and data cleansing.

Our view was also that companies should focus on their business processes, rather than on the data itself because the root cause of just about any data quality problem is an inadequate or missing process. We recognized that duplicate records, denormalized databases, and poor lead routing and account assignment were all merely symptoms of poor business processes. The term “data orchestration” implied that different groups and systems worked together as part of a carefully choreographed business process.

The term was well-received by customers and analyst firms like SiriusDecisions because it incorporated the dynamic nature of data and the idea of automating many different processes across systems in a well-coordinated way. In recent months, a wide array of companies—from tag management companies to DMPs, content syndication, and ABM companies—have begun adopting the term. So it’s important when talking with a vendor to clarify what they mean when they say they orchestrate a given process.

At Openprise, we view imitation as the sincerest form of flattery. So we’re very excited that other companies are picking up the term. We view this as an inevitable next step as data orchestration becomes more broadly adopted as its own distinct space. We believe G2 categories, Gartner MarketScopes, and Forrester Waves are just around the corner as the space continues to gain momentum.

Leave a comment