In this podcast, Jason talks to Keith Goldthorpe, Lead Solution Architect here at Cynozure, about Data Mesh and how it’s being used by many organisations to effectively grow and scale data strategies.
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As our lives become increasingly digital, the amount of data we produce is growing at an alarming rate. Management of this data can be a challenge, but new technologies and approaches are emerging to help us make sense of it all. This is where Data Mesh comes in. Data Mesh is an approach that allows for the management and analysis of large amounts of data in real-time. It does this by connecting all the different sources of data together, making it easier to find and use. This makes Data Mesh a powerful tool for businesses and organisations looking to make better decisions at scale.
[01:07] Keith’s retail background in data, and his 30 year career in data and IT
[05:10] What is Data Mesh? How it provides a new perspective
[15:05] Why Data Mesh is so useful
[22:21] The scenarios where Data Mesh is most viable
[29:05] How companies are integrating Data Mesh into their organisation
[32:20] 3 ways in which Data Mesh can be organised
[35:50] Why Data Mesh has seen a surge in popularity
[40:59] Examining where Data Mesh is at now and how it will be utilised in the future
As the volume and variety of data continues to explode, so too does the need for better ways to manage and use that data. One promising solution is Data Mesh, which offers a more holistic and efficient way to organise, process, and access data. Data Mesh is an approach to how teams and logistics are organised and is a framework that allows organisations to easily scale their data needs. There are four components to Data Mesh:
The most important benefit that Data Mesh provides is scalability. In larger organisations, Data Mesh is about having a network of interconnected data points, where each data point is autonomous and can manage its own resources. The framework allows organisations to add – or remove – nodes from the network as needed, without affecting the rest of the mesh. This decentralised approach is what makes it so scalable.
In recent years, the role of data teams within businesses has evolved. Once responsible for simply gathering and reporting on data, these teams are now often at the forefront of innovation, and help to drive business value through data-guided decision making. However, while this shift in focus is welcome, it can also create a bottleneck within businesses as data flows through multiple centralised data teams. This can limit the speed and agility with which businesses can act on insights from their data.
In order to overcome this limitation, many businesses are turning to a Data Mesh model, in which data flows freely between functions and teams. By doing so, they’re able to break down the traditional barriers between departments and get more value from their data faster.
Data Mesh is best suited to organisations that have a large data infrastructure, and don’t want to be held back by potential bottlenecks involved with having only one central team in charge of everything. It’s also perfect for companies that may be small (such as startups), but want to rapidly scale – and ensure everything is in place for rapid growth.
As data becomes increasingly complex and voluminous, managing it effectively becomes a greater challenge. Data Mesh solves this problem for organisations, and with its increased adoption its principles are becoming more standardised and easier to implement. Different industries are finding new and innovative ways to utilise the framework to scale rapidly with less risk.