Creating the Data Orchestration Category
Written by Russ Artzt on September 17, 2020
This post is part of our exclusive series –”Artzt on Data.”
I was born to talk about data quality. When your last name is Artzt, you experience unique trauma over people’s spelling and pronunciation mistakes. Since before the dawn of the digital era, I’ve been called Russell Arst, Russel Zart, Art Russell, Russell Artist and worse. It is this background, coupled with my work as Executive Chairman and Head of R&D at RingLead, that has inspired me to advocate for the creation of a new industry category known as Data Orchestration.
Data Orchestration can be thought of as the nexus point between the disciplines of data quality management, data analytics, data management and business operations. It’s more than just a software tool — it’s a way to transform your company around the power of customer data. But what, exactly, is it?
Given all of the three-letter acronyms in the software space (DMP, CDP, ESP, MDM), why should we pay attention to yet another category of data-related software? As we will discuss, the concept of data orchestration, writ large, is more than just capturing, unifying, and activating customer data; it is a business discipline that, when executed well, can transform entire enterprises.
What is Data Orchestration?
Data Orchestration refers to the intersection between data quality management, data analytics, customer success platforms, data management, marketing ops, sales ops and business operations. It’s more than just technology. Data Orchestration is about taking on the totality of data as a creator of value in a business.
We are cultivating the data orchestration category at RingLead because we believe the existing industry categories for data quality and management (as defined by Gartner and others) do not adequately describe the deep need for complete, operational command of data at most companies.
Take RingLead as an example. While our offerings overlap with multiple software categories (data quality management, marketing automation, customer data platforms, business intelligence), there isn’t a single category definition that describes the scope of what we do, and how it impacts stakeholders throughout an organization. In short, we align data quality with revenue growth.
Here are the three paradigms of data orchestration, and why they matter:
Data Quality Management
Before you can orchestrate data throughout the enterprise, it must be unified, cleaned, and transformed. The new saying,“data is the new oil,” is true, but it’s also true that to make it profitable, you must extract it and refine it. Data quality solutions automate the ability to resolve customer data, enrich it with attributes, and make it available to every endpoint where it can be activated. Data quality cannot be a once-a-month exercise in deduplication; data quality is about having an always-on system continually analyzing the entirety of the data asset, cleaning the data, and aligning it to an information model that makes it consumable by every system and department. Data Orchestration is also a worthwhile emerging category because it addresses what I think is a poor assumption on the part of so many stakeholders: data is not static. The prevailing data quality and data management paradigms tend to view data as something that just sits there in repositories, waiting for analysis. Data is dynamic. It moves. It’s constantly changing.
RingLead customers understand this. For over two years, Pandora has leveraged RingLead as an instrumental role in its Data Hygiene and Governance efforts in order to drive data transformation and unification. Using a full-stack approach, we’re able to cleanse, normalize and deduplicate 15+ years of data, identify and purge invalid and/or obsolete records, and help Pandora prevent duplicates in real time.
Once unified and cleansed, data must be made available to all of the endpoints that consume it: CRM systems, marketing automation platforms, email service providers, advertising platforms, data lakes and warehouses. Most companies have built their sales and marketing operations on a series of different software tools acquired over time. Each has different ways of identifying customers, supports a different data model, and has varying ways to pull data into and out of the system. These systems are monolithic, and tend to live in silos. Companies can spend years connecting them through different data extensions and batch-oriented manual processes. One company may have the same customer in a CRM system, call center database, and marketing cloud platform — but dozens of different customer records for her.
Data orchestration systems act as a clearing house for all of this data, federating a single, durable ID across all of these systems, and provisioning clean, enriched data to the systems that power revenue, marketing, and customer success.
It’s not enough to make clean data available to different endpoints. If companies are to transform around data, then data orchestration systems must work behind the scenes, utilizing scores of proprietary processes and unique connections to data sources to keep clean data flowing between the many systems that support the enterprise. It’s not just about connecting marketing and sales operations to drive revenue. For example, other operational areas like fulfillment, logistics, and even contract management all work better when their respective systems work from clean, accurate data. Without good data, the business will see costs arising from errors in fulfillment, contract terms and the like.
Ultimately, the benefits and impact of data orchestration extend beyond revenue operations. Data orchestration, done right, will positively influence overall business operations. We are in the data orchestration business. Everything we do revolves around making your data better and enabling it to work harder for your business.
If you want to learn more about how data orchestration technology can benefit your business, let’s talk.