Discover the data product platform and the power it has to unleash the full potential of the data mesh architecture.
Architecting a data mesh may appear daunting, but a data product platform offers a simpler route to decentralized data management.
While centralizing all enterprise data into a "single source of truth" seems logical in theory, practical implementation poses numerous challenges.
Data mesh, a modern data architecture and operating model, presents an alternative – a federated approach to managing and analyzing enterprise data.
Enterprises adopting a data mesh architecture gain significant advantages, such as reduced dependence on centralized data teams, increased business agility, and faster data delivery. However, challenges can arise, especially if you’re unprepared.
Data mesh has many advantages, such as:
Enhanced scalability and business agility
Data mesh's decentralized architecture empowers domain autonomy and offers data infrastructure as a service, promoting agility. It reduces IT backlogs, operating costs, and time-to-market.
Simplified compliance
While data governance is decentralized, global rules ensure adherence to privacy, access controls, quality, discoverability, and compliance standards, promoting data quality and access.
Reduced reliance on centralized teams
Unlike traditional data architectures, data mesh distributes data ownership among cross-functional domain teams. This improves efficiency, data quality, and reduces demands on data teams.
Increased flexibility
Distributed data mesh infrastructure avoids vendor lock-in, fostering vendor-agnosticism and flexibility in connecting with multiple systems.
Data mesh challenges include:
Cultural and process shifts
Adopting data mesh requires significant change management, team restructuring, and tool adjustments for data engineers and product managers within domain teams.
Enforced federated governance and quality assurance
Neglecting data quality and compliance can lead to interoperability issues, noncompliance, and data integrity loss. Identification and federation of responsibilities become necessary.
Risk of data redundancy
Data mesh can lead to redundant or inconsistent data products across domains, undermining data reliability and increasing management costs.
Decentralization issues
Data mesh might not suit all organizations; it's ideal for those with distributed expertise, mature data management practices, and multiple divisions.
“Data as a product” is a fundamental principle of data mesh.
A data product is a reusable data asset, engineered to deliver a trusted dataset, for a specific purpose. It integrates data from relevant source systems, processes the data, ensures that it’s compliant, and makes it instantly accessible to anyone with the right credentials.
A data product shields data consumers from the underlying complexities of the data sources, decoupling the dataset from its systems, and making it discoverable and accessible as an asset. They have well-defined interfaces, metadata, and SLAs, making them easily and safely consumable by other teams within the organization.
The data product lifecycle represents the stages through which a data product evolves, ensuring its relevance, accuracy, and value over time.
Enterprises that embrace the data product concept benefit from better:
Efficiency
Reduce the time and effort needed to access and process data, accelerating business processes.
DIY data access
Empower teams with self-service data access (e.g. BI and analytics), reducing dependency on data engineering.
QA
Encourage data quality monitoring, leading to higher data accuracy and reliability.
Collaboration
Foster effective collaboration among teams with a common understanding of data facilitated by data product documentation.
Scalability
Support scalable data access and usage, adapting to evolving data needs and growing data volumes.
A data product platform addresses data mesh challenges by enabling domains to create and manage their data products autonomously. The platform provide seamless connection to multiple data sources and enforce quality and privacy policies.
It also provides all the tooling required to manage the data product lifecycle – from design, through engineering and testing, to monitoring of business impact and SLAs.
The data product platform empowers organizations to embrace the benefits of a decentralized data mesh architecture, or centralized data fabric architecture, or data hub architecture, without complexities. It democratizes operational data access, fosters business agility, and drives data-driven innovation.
One standout feature of the K2view Data Product Platform is its underlying patented Micro-Database™ technology, which allows data products to deliver a trusted, complete, real-time view of any business entity (such as a customer, device, or order).
The K2view platform integrates data from various source systems into secure “mini data lakes” (one for each customer, or any other entity), ensuring data integrity and global governance standardization – and ultimately accelerating access to operational data and enhancing reliability.
Learn more about K2view Data Product Platform.