If you have been attentive to what is happening in the data sphere, then you must have come across a concept that has been around for some time now. However, many fail to understand its real value from a business perspective.

The “Data Mesh”, or the revolution of data decentralisation, seems to be one of the most hyped and actively discussed topics related to modern data management. Yet, beyond the technology, the question of what it could concretely mean from a business value standpoint still stands for many organisations.

In our previous piece “The practical guide to Data Mesh”, we uncovered some of its fundamental principles, characteristics and implementation challenges.

In this blog, we will summarise some thoughts on the business value of a Data Mesh and give you the main business reasons for adopting such an approach.

Five Business reasons to consider a Data Mesh

1. Improve data quality with a domain-driven approach

Monolithic architectures are generally managed by a central team that carries out all data operations.

Centralised architectures are fit for small-sized organisations with a stable data landscape. However, for larger organisations, not only is it inefficient, but most importantly is it unrealistic to have a central team understand all the practical details of each and every domain of the enterprise.

In a Data Mesh configuration, data management is domain-driven and domain knowledge is spread within all business lines. With an organisation by domain, data know-how is no longer centralised but rather distributed to the teams that are the closest to the data.

Thereby, the Data Mesh makes it possible for these domain teams to package their own data for consumption and develop data products that immediately address business priorities.

Since the understanding of domain data is intrinsically higher for domain experts than for a centralised team, this not only maximises the diffusion of knowledge but also improves data quality by bringing both the production and use of data closer to the business.

2. Help your business scale

Monolithic architectures are too unwieldy to scale or change to meet dynamic market demands. They are usually built with a high level of dependency between their different components (highly-coupled data pipelines) which makes development more complex and slow.

Also, the lack of domain-specific knowledge often causes central teams to turn into huge bottlenecks hindering the ability of the business to react quickly and effectively.

When the number of business use cases increases, you want to get economies of scale from your data infrastructure. In other words you want to make sure that the cost of exploiting your data goes down as your business usage increases.

This is precisely what the decoupling provided by the Data Mesh provides: the data is packaged through reusable data products that can be used for multiple purposes

3. Connect the data to your business thanks to product thinking

The Data Mesh introduces a shift towards thinking about data in terms of products and domain-owners (domain-specific teams that own specific data products).

Product thinking aims at building a positive customer experience by understanding their needs and challenges, designing and developing data products based on their input and feedback.

In this case, the customers in question are merely the other business lines or data teams within your organisation.

The main paradigm shift here is for the domain owners and their teams to start thinking about themselves as subsets of the mesh. Each is responsible for delivering high-quality and ready-to-use data products that will add value to their customers (i.e. other business functions, data teams). A domain-driven approach coupled with a stronger alignment to business goals for an enhanced overall productivity.

The end result is cross-functional business and data teams working together towards delivering valuable data products that fully respond to the needs of the organisation.

4. Unleash a culture of innovation

One of the greatest benefits of decentralised architectures like a Data Mesh is that it democratises the access to data by packaging it in ready-to-use data products that are accessible in a self-service mode. This not only enables better decision making power and faster time to value but it also fosters a culture of innovation.

In fact, in a Data Mesh setup, domain teams are both producers and consumers of data products. They have the ability to run experiments, tests, simulation scenarios and explore moonshot ideas. These kinds of initiatives are what ultimately lead to the creation of valuable and useful productised data sets that all end data users across the enterprise can access and leverage.

This forges a culture where every user is invested in delivering more value, experimenting new ideas and pushing the boundaries of data innovation.

5. Build cross-domain insight in your organisation

Siloed teams and hyper-specialised engineers disconnect data from the business.

When data architectures are centralised and monolithic, the teams that run them are structured in a similar way. Hence, these teams are split into domain-specific groups at each end along with a central team of engineers in the middle. The main flaw of such configuration is that it does not take into account the chain of accountability from one end to the other.

This has two main drawbacks:

  1. Each team focuses on its own individual tasks, sometimes at the expense of the wider team.
  2. The central data team is disconnected from the business strategy and objectives.

On the other hand, the Data Mesh helps in democratising data beyond circles of experts. Its self-service design empowers data consumers to use and share different data products, given they have the required access rights.

Such a process of decentralising and productising data is a step towards a more efficient enterprise data architecture that opens the door to cross-domain insight sharing and collaboration.

Ultimately, as elegant as certain approaches can be, the test of fire is ultimately always the value that they bring to the business. To be more practical, we have built an approach for you to fast-track and operationalise your future Data mesh. Read more about how you can derisk your data transformation and get immediate business results.


You can also read


The practical guide to data mesh

This practical guide will unpack the why, what and how of the data mesh approach and how using it can solve many of the challenges of implementing a successful self-service data program.

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