Forrester on Data Mesh: Approach, Characteristics, Tooling Capabilities, Use Cases, and the Future
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Leverage Forrester’s research to understand data mesh and use it for your data and analytics ecosystem.
Forrester is a research and consulting firm that specializes in offering insights on technology, marketing, customer experience (CX), product, and sales functions. Forrester runs annual surveys of 675,000+ consumers, business leaders, and technology leaders worldwide to get these insights, along with its Forrester Wave™ evaluations.
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The data mesh is a modern data architecture that enables data consumers to take the lead in creating value with data. Forrester labels this as “putting business semantics first”.
In this article, we’ll review how Forrester perceives data mesh and its impact on data and analytics use cases.
Table of contents
- Forrester on data mesh
- Challenges with implementing the data mesh approach to the modern data architecture
- Business semantics and data mesh
- Related reads
Forrester on data mesh
In the Trends report titled “The Modern Data Environment Uses Both Data Fabric and Data Mesh”, Forrester defines data mesh as “a business-led strategic approach to data and data practices that enables a communication plane between applications, machines, and people.”
Forrester sees data mesh as a modern, decentralized approach to data architecture. The data mesh can help “keep humans and machines in sync and speaking the same language.”
Note: Forrester acknowledges that the data mesh concept was introduced in 2018 by Zhamak Dehghani while she was at Thoughtworks. For more on Dehghani’s take, check out our comprehensive guide to the data mesh.
The data mesh empowers data consumers and helps them use data to drive business decisions.
According to Forrester, data consumers can cease to be data bystanders and instead, play an active role in the design, development, and management of data capabilities.
The four principles of the data mesh
According to Forrester, the data mesh framework embraces four principles:
- Domain ownership for context, understanding, and responsibility
- Federated computational data governance (FCDG) for ambient trust and control
- Self-service to scale data use and business value
- Data-as-a-product to assign and manage the business value of data capabilities
We recommend checking out the complete report to delve into the specifics of each principle.
Also, read → The 4 fundamental data mesh principles
The five data mesh success factors
- Defining and developing data products
- Portfolio management, i.e., data product management
- The role of DataOps
- Federation with strong subject matter experts
Challenges with implementing the data mesh approach to the modern data architecture
The primary challenges with implementing the data mesh are:
- Costs: According to a Forrester report on data fabric and data mesh, setting up a single domain can cost up to $50 million. So, building data infrastructure with multiple domains will be expensive.
- Semantics: Goetz predicts that data mesh as a technology will continue to get more confusing because of “conflicts in definition, message, technology, and value.”
- Technology: Decentralizing domains and decoupling the application and data layers is tricky. Domains could have multiple owners depending on the scope, context, use, etc. Moreover, keeping data in sync with business use cases at scale will also be challenging.
- Data governance: Ensuring data governance within decentralized domains while adhering to global policies and controls can be tough. Goetz even labels federated computational governance as the weak link.
So, how do you implement the data mesh in practice? And how can technology help?
The answer lies in getting the business semantics right. Goetz highlights how “by applying the business language in the form of relationships, classifications, labels, and tags, working with data becomes declarative.”
This should be followed by examining your existing data infrastructure from the lens of the four data mesh principles.
Let’s see how.
Business semantics and data mesh: Data mesh in practice
According to Goetz, data mesh “models data as a twin of the business in the language of the business.” In terms of your data stack, Goetz sees the data mesh as the contextual distributed orchestration layer.
The Trends report (mentioned earlier) also highlights how the data mesh puts business semantics first. This is possible by thinking of data as a product.
Here’s how Goetz sees the data mesh:
“Technically, data mesh is the orchestration layer. As a product, data mesh is what ensures that the technology serves and tunes data and insights for the consumer, unique point-in-time value, and the best outcome.”
Implementing the data mesh in practice doesn’t require you to reinvent the wheel. According to Goetz, you have already invested in the four principles of data mesh independently.
Focusing on data as a product and self-service capabilities is an essential part of making data mesh pragmatic and realizing a return on data.
Goetz also highlights how the data mesh provides “a framework to unify the principles and practices as a standard operating model to make decentralization easier and more effective.”
So, data and business leaders should focus on designing data for the customer experience, value, and outcomes expected. Ultimately, the data mesh is an approach that preaches prioritizing business semantics and ensuring that the business lens is the leading factor for data capture and consumption.
Forrester Data Mesh: Related reads
- Forrester Wave™: Enterprise Data Catalog for DataOps, Q2 2022
- Forrester on Data Quality: Approach, Challenges, and Best Practices
- Forrester Forrester Wave™ Data Governance Solutions, Q3 2023
- Past, present, and future of data catalogs: A masterclass with Forrester
- Forrester changed the way they think about data catalogs
- Forrester on data fabric and its role in your data and analytics ecosystem
- What is Data Mesh?: Examples, Case Studies, and Use Cases
- Data Mesh Principles: Top 4 Fundamentals and Architecture
- Data Mesh vs. Data Fabric: How do you choose the best approach for your business needs?
- Data Mesh and Data Lake: Understanding Use Cases & Reasons to Deploy
- Data Mesh Architecture: Core Principles, Components, and Why You Need It?
- Data Mesh Setup and Implementation - An Ultimate Guide
- Snowflake Data Mesh: Step-by-Step Setup Guide, with Detailed Notes on Scaling and Maintenance
- Data as a Product: Applying Product Thinking To Data
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