Data mesh is a new concept that is turning enterprise data management on its head. And enterprise leaders across industries are eagerly adopting the idea.
Why? The data mesh architecture offers organizations a way to quickly and effectively make their data more manageable, accessible, and actionable – without forcing everything into a data lake or data warehouse first. By keeping the data distributed, organizations gain all the benefits of an integrated data lake, but with much more flexibility.
In essence, the data mesh structure allows relevant teams – the ones who most closely interact, manage, and use the data – to own it. The same data is easily accessible to everyone across the enterprise through self-service platforms. That means data has better context and is fresher and more organized in the way users need it.
But embracing data mesh technology is no simple task. It requires a complete shift in the data management paradigm. And it forces teams to think – and radically change – their current data management strategy, processes, and ways of working. It's a particularly attractive idea for data-driven organizations that compete based on the strength of their data. Large, complex organizations struggling to democratize access to data-driven insights are also quickly adopting the technology.
Deciding whether a data mesh process flow is suitable for your organization will depend on your answer to five key questions:
- Is your data accessible? If your company needs to share data quickly and effectively between various functions and roles, the answer is probably yes. Up until today, this idea has usually involved shifting all data into a centralized database managed by a group of data scientist generalists. Data mesh principles, on the other hand, call for practical data catalogs to allow users to discover the information they need when they need it.
- Is your data consumable? Making data available and making it consumable are different objectives. You can make data accessible by dumping it all into a lake. But finding the data you need in the format you require is much more difficult. Data mesh turns data streams into a product owned by those who know the data best, resulting in greater data contextualization and better user experiences.
- Is your data manageable? Users who consume specific data streams often want to handle that data – to analyze it, interpret it, and apply it. Yet that becomes very difficult under a centralized data and governance model. In turn, data mesh gives product owners and users the ability to manage the data independently – all in the cloud. With a cloud-based architecture, enterprises can rapidly democratize access to data, reduce operational costs, and move from experimentation to innovation more quickly.
- Is your data actionable? A solid data mesh foundation ensures that data flow is timely enough to support relevant decision-making. In centralized approaches, it can sometimes take months for data to move from its point of origination to an executive dashboard. With a data mesh, not only is the data coming straight from the source, but it's also as fresh and relevant as possible, resulting in faster decision-making.
- Are you working as a team? Data mesh shifts data talent requirements to various data owners across the organization. As a result, it requires the ongoing support of multi-functional teams throughout the data lifecycle. For this process to run smoothly, business and IT leaders must work in lockstep to build a strong data-driven culture founded on collaboration. Indeed, moving to a data mesh architecture requires the entire enterprise to work together – enabling all employees to process, analyze, and extract information from the treasure trove of data at their fingertips.
In today's data-driven world, executives and employees need access to high-quality data – and they need it fast. Hence, it's becoming increasingly clear that the solution does not always lie at the bottom of a data lake. If you are looking for more accessible, consumable, manageable, and actionable data – without all the risks and challenges of a centralization strategy – maybe it's time to embrace the benefits of data mesh.
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