In a previous blog article, we talked about the differences between a data monolith and a data mesh, and which structure works best for you.
Now, we’ll dive deeper into data mesh specifically, and share tips on how to deliver data and serve users within your domain in this structure.
While a monolithic architecture may still be manageable for smaller organizations, data mesh is a more scalable model for the inevitable surge in data and sources that will come as your company grows. That’s why we’re doubling down on it.
Remind me, what’s a data mesh again?
“Data mesh” was introduced in 2019 by Zhamak Dehghani, the former Director of Emerging Technologies at Thoughtworks. It’s a disruptive approach and mindset behind the management and the sharing data for analytical use cases, that poses a change to both organizational structure and technical architecture of businesses. While it’s considered a “socio-technical” shift, there’s a significantly bigger emphasis on the “socio” - the mindset, the people, and the organization. After all, the mindset is what has to come first and foremost, with technology following after. 
Why do we need a new mindset? Starting bigger picture, Zhamak noticed that companies these days all strive to be “data-driven,” putting data at the heart of their mission statements. They set ambitious goals around leveraging data to be more customer-centric, to anticipate needs, to grow revenue, to drive efficiencies, to reduce costs - you name it. According to Accenture, 90% of all businesses are expected to explicitly mention data as a key success factor by 2022.  For companies that already incorporated data as a crucial part of their company trajectory, they’re really putting money where their mouth is. Companies are investing millions into being “data-driven” - 65% of enterprises are investing over $50 million into data initiatives.  However, despite these ambitious (yet lofty) goals, and all the money behind “data,” the results were consistently low and didn’t reflect everything that was going into these efforts. That’s when Zhamak started investigating and re-thinking the way data is currently structured, and why business results weren’t adding up.
The status quo of how data teams are structured in organizations is predominantly monolithic, where there’s one central team that sits in the middle, responsible for dishing out data to all other functions and departments. This centralized approach has a few challenges:
Oppositely to a monolith, a data mesh is a decentralized data management structure, where data experts sit in different domains and are responsible for delivering “data as a product” to these teams. Thinking about “data as a product” means that the data experts for any particular business domain can tailor how best data is shared, adopted, and used for that function. This model provides more freedom and autonomy for both the data experts and the business users, and a shared sense of accountability and responsibility.
Data mesh has 4 main principles outlined by Zhamak :
Clear boundaries of responsibility between domain teams (the ones focusing on creating business-oriented data products), and the platform teams who focus on technical enablers for the domains. This is different from the monolith structure where the data team is responsible for both domain-specific data for analysis, as well as the underlying technical infrastructure.
Data sets should be served as if a product is going to a customer. This concept refers to the application of product development principles to data projects, such as identifying and addressing goals and needs, agility, iteration, etc. This methodology empowers data experts sitting within their respective domains to apply product management principles for their data work to be more valuable and scalable for the consumers. Data products need to be easily accessible by all the data consumers within the organization, with special focus on the consumers within their own domain.
Even in a data mesh approach, everything must rest on a common platform and tools that are easy to use, even for those who don’t have technical data expertise. Domain teams must be able to autonomously build and maintain their own data products. Without a self-service infrastructure in place, domain teams will have to rely on limited resources that aren’t dedicated to their domain, and won’t be able to truly own their data. Self-service platforms enable this autonomy and data adoption across the organization.
Federated governance sets metadata and documentation standards that each domain must follow and apply to their own data products. It ensures that data products from different domains can be combined and modeled against each other easily. Of course, there needs to be a balance between global governance and the autonomy that each domain team should have. Maintaining consistent access controls and data protections is important in a data mesh approach, to ensure overall quality.
So, how do I serve my domain?
Congrats! Your company has “officially” adopted this data mesh approach, and you are the data analyst sitting on the sales team.
In summary, unlike the traditional monolithic approach in which everything is managed from one central data lake, the data mesh supports decentralized, domain-specific data consumers, with data experts sitting in the respective teams. These data experts are in charge of delivering their data experience as a product, managing their own data pipelines and figuring out the best way for their domain to consume and make use of the data so that it adds business value. Beneath the data mesh, there’s a standardized layer of governance to ensure that the data is reliable, accurate, and trustworthy.
We hope this article has inspired you to shift towards a data mesh mindset in which “data is a product” and get excited about owning your data experiences and delivering them in a way that best serves your domain consumers. This mindset will undoubtedly maximize your chances of making an impact on your domain - and organization - and driving a true “data-driven” culture.
To learn more about data mesh and how you can drive this in your organization, talk to one of our data experts!
 Zhamak Dehghani, "Delivering data-driven value at scale" 2022
 Ammara Gafoor, Ian Murdoch, and Kiran Prakash, "Data Mesh in Practice: Getting off to the right start" Thoughtworks 2022
 L. van der Sande, M. van der Meijden and M. Geleijns, “Why you need to capitalize on the rise of the data-driven enterprise,” Accenture blog, May 2021
 S. Bokil, “Nearly 65% of Organizations are Investing Over $50 Million in Big Data and AI,” Enterprise Talk, January 2020
 Eleks, published by DataDrivenInvestor, "Data Mesh: The Four Principles of a Distributed Architecture," March 2021