“Data is the new oil.” - Clive Humby, a British mathematician and entrepreneur
Just as the human civilization went through the first oil rush in the nineteenth century, which transformed the way we live our lives today, the twenty-first century is witnessing a ‘data rush,’ which is metamorphosing the way we do things at an even more exponential pace.
For instance, the power of data has magnified to an extent, wherein we are on the cusp of accomplishing immortality. Somnium Space is a metaverse startup offering a solution to store your physical and behavioral data for recreating an immortal version of yourself in the metaverse.
But how does an enterprise achieve the level of democratization and scalability for data to be able to unlock its full potential? Ask anyone in the data industry today, and one of the most probable answers you will get is a ‘data mesh.’
Zhamak Dehghani, a ThoughtWorks consultant and the original architect of the term, somewhat defined ‘data mesh’ as decentralized data platform architecture that enables data users to access and query data easily instead of first transporting it to a data lake or data warehouse.
In simple terms, think of data as a product within an organization. A particular domain or a business unit controls the ingestion, cleaning, and integration of its own data based on its unique needs. However, all such domains are connected with a layer of universal interoperability, enabling data users to access, analyze, and operationalize business insights across all data sources within an organization without any requisite support from expert data teams.
Data warehouses emerged as a reliable solution for smaller and more structured data but soon got bogged down under the influx of vast quantities of unstructured data in the modern big data era. Then, we moved on to the data lake, enabling companies to store both structured and unstructured data to accelerate data processing and innovation. However, it was plagued by vast amounts of unorganized and inconsistent data, which is really of no use.
Data mesh seems to be an ideal alternative in such scenarios wherein the central data team, using either of these monolithic data infrastructures, cannot handle all the analytical questions of management and product owners quickly enough. Moreover, a considerable bottleneck with the old approach is that the data team needs to learn domain knowledge to give meaningful insights to all data users.
These problems exponentially magnify as organizations grow with branches across countless domains. The solution to this is defined by the core principle of a data mesh: domain-oriented decentralization for analytical data.
1. Enhanced data accessibility
Each business unit, domain, or team has complete control over its data. They are not dependent on another division within the organization to gather insights, making the whole process much faster. This enhanced data accessibility can accelerate innovation and ultimately deliver better business results.
2. Improved quality of analytics and insights
A domain expert is better positioned to evaluate why a particular event led to a specific set of results, considering all the necessary data points and variables. On the other hand, a data engineer might not always possess the same level of expertise to achieve a higher degree of accuracy for the data insights.
3. Customization, flexibility, and independence
A decentralized network enables data users to function more freely as they can customize a big part of the entire data management process based on their unique needs. This enhances speed and efficiency and makes working with data easier and more straightforward.
4. Better connectivity and data security
A critical aspect of a data mesh is to enable universal interoperability. It stores the analytics right where the data resides, allowing users from different domains to access these insights without taking a complicated route. Such connectivity in a distributed model greatly augments data security as it eliminates the need to copy or transfer data through a public network.
In a nutshell, data mesh is creating a self-serve design via your data architecture. It enables end-users to access and query data where it lives easily. Data mesh works by distributing data ownership to domain-specific teams that manage, own, and serve the data almost as a product.
Datalogz allows clear communication and ownership assignment across all data objects within a company. End data consumers can report issues and request metadata directly from the owners without having governance, or IT bottlenecks. Therefore, Datalogz enables data mesh type results without having to redesign any architecture.
Essentially, we have a cheaper, more refined, and easily-adoptable solution to your data problems that incorporates all the good things about a data mesh and then adds some more.
