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What You Need to Know About B2B CDPs, Data Ops & Data Mesh Architecture (Part 1)

Abhi Yadav, Co-Founder & CTO, Zylotech

Marketing operations (MOps) and revenue operations (RevOps) are not the same as they were five years ago. Today, MOps and RevOps teams handle far more sources and types of customer data. They need more tools and technical skills to manage and gain insights from that data. These teams often search for the latest data management trends, seeing the terms “data mesh,” “DataOps,” and “CDPs.” They see these terms but may not know their importance to their work. 

So today, I thought I’d explain why MOps and RevOps teams should care about these three key concepts.

Data Mesh: A New Enterprise Data Architecture

McKinsey defines data mesh as “an ecosystem consisting of best-fit platforms (including data lakes, data warehouses, and so on) created for each business domain’s expected data usage and workloads.” A data mesh platform lets your technical team and operational team join forces through this ecosystem, with each team accessing data from multiple sources.

McKinsey also says that: 

“Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation. The key to a successful upgrade—and significant potential rewards—is agility.”

The concept of data mesh is gaining steam for this reason, modern data architecture must allow for data agility

Zhamak Dehghani, director of emerging technologies at ThoughtWorks, covers some of the principles of data mesh in this article. In the article, she says that the: 

“Data mesh objective is to create a foundation for getting value from analytical data and historical facts at scale – scale being applied to constant change of data landscapeproliferation of both sources of data and consumersdiversity of transformation and processing that use cases requirespeed of response to change.”

B2B businesses face huge challenges when it comes to data. Activities involving data constantly change and evolve as companies access more data via first and third-party sources. Also, every team and tool use a different data model, for example:

  • CRMs work on an account basis and company HQ, using territory planning logic.
  • Many martech platforms find leads based on buyer personas, as opposed to ICP 
  • Customer success platforms use a “post sales data model” with distributed users (contacts and buyers) who are relevant for renewals.

While teams often use their own tools and data models, they expect some federated, global compliance of central data. Data mesh lends itself to the principles of trusted data governance. It allows organizations to adhere to regulations regarding data security and privacy, even if teams want to use different data models.

Another benefit of data mesh is that you don’t have to start your data projects all over again from scratch. You can move to a data mesh architecture and still keep your current vendors and solutions, such as relational database management systems (RDBMS), Snowflake, or Databricks.

Make sure to check back in for Part 2 on “An Operational Approach to Data and Analytics” and “Making Customer/ICP Data Accessible“.

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