Nearly all businesses in the era of self-service business intelligence identify as “data-first businesses,” but not all businesses are approaching their data architecture with the democratization and scalability it requires. For example, your business sees data as a catalyst for innovation. The architecture paradigm known as “data mesh” is revolutionizing the sector. Some businesses do not give their data architecture the democratization and scalability it needs.
What exactly is Data Mesh?
By utilizing a domain-oriented, self-serve design, a data mesh is a sort of data platform architecture that embraces the pervasive nature of data in the company. A data mesh facilitates dispersed, domain-specific data consumers and considers “data as a product,” in contrast to conventional monolithic data infrastructures.
An important feature of Data Mesh
· Data meshes use domain-oriented design concepts to provide a self-serve data platform that allows users to abstract technological complexity and focus on their specific data uses cases.
· Data meshes federate data ownership across domain data owners, who are responsible for selling their data as products, in addition to facilitating communication between distributed data across many locations.
· A common set of data standards that support each domain makes collaboration between them easier when it’s essential, which it frequently is. Some data—both from raw sources and from cleaned, converted, and served data sets—will inevitably be useful in more than one domain. The data mesh needs to define formatting, governance, discoverability, and metadata fields, among other data properties, in order to facilitate cross-domain collaboration.
Why use Data Mesh?
Data meshes address the disadvantages of data lakes by giving data owners more autonomy and flexibility, enabling more data experimentation and innovation, and reducing the pressure on data teams to meet the needs of all data consumers through a single pipeline.
Meanwhile, the data meshes’ self-service infrastructure-as-a-platform provides data teams with a universal, domain-neutral, and frequently automated method of data standards, data product lineage, monitoring, alerting, logging, and data product quality metrics (in other words, data collection and sharing). When combined, these advantages offer a competitive edge over
conventional data infrastructures, which are sometimes constrained by a lack of data uniformity among both customers and suppliers.
To Mesh or Not to Mesh?
To establish whether it makes sense for your company to invest in a data mesh, we came up with a quick calculation. Please provide a numerical response to each of the questions below. Add up all the answers to receive your data mesh score.
· How many information sources are there? How many different data sources does your business use?
· What’s your data team’s size? How many product managers, data engineers, and analysts are there on your data team?
· How many data domains? How many products does your firm have? How many data-driven features are being developed? And how many functional teams (marketing, sales, operations, etc.) rely on your data sources to make decisions? Total the sum.
· Obstacles in data engineering on a scale of 1 to 10, where 1 is “never” and 10, how often is the data engineering team a bottleneck to the implementation of new data products?
· Data management. On a scale of 1 to 10, how important is data governance to your company?
The more points you receive, the more complicated and demanding your company’s data infrastructure requirements are, and consequently, the more likely it is that your business will gain from a data mesh.
