A data mesh architecture treats data-as-a-product as the new way to empower each stakeholder or domain to unravel insights from data.
According to Forbes, companies that use insights from big data experience an average increase in revenue of around 44% – recognizing customer needs that need attention and serving them at just the right time. Businesses bought into the data claims and started collecting data, yet business users find it difficult to find, use, and customize the data they want when they need it. Data and insights can transform organizational outcomes only when meaningful data is made available to every data consumer (internal and external)
Thus, data as an outcome-driving asset has been recognized by businesses, but they still struggle to manage the data coming from varied sources without standardization and governance. Thus, this creates unused and invaluable data silos (called data gaps).
The approach to viewing data like a data product manager is to make it available to anyone as and when required for a particular function. Businesses can align their objectives with the help of insights from data products. In simple terms, treating data as a product means providing data that is accessible as and when required by the team or customer from any diverse source within the organization. For example, a software development team requires adequate and accurate data for application development.
The “data-as-a-product concept is a paradigm shift from data centralization (a central warehouse or lake) to a decentralized network of domains. This approach can improve data accuracy, accessibility, and security.
The practice of treating Data-as-a-product is a foundational building block of an enterprise data mesh.
Data Mesh is an agile method of managing take-on data architecture that promotes decentralized data management and governance. It is a domain-driven data architecture that requires product thinking for collecting data for each domain, where each stakeholder of data is the owner of their data to provide clean, valid, and reliable data products. Thus, every stakeholder or team within an organization adopts product thinking and views data as a product where the ownership lies beyond their department to make data accessible, clean, and adequate for every consumer (anyone who requires data for any purpose or development) within an organization.
The conventional method of extracting and integrating data adopted by many businesses failed to align with business needs and use cases. The diverse data did not conform to the necessary level of governance and quality, which limited businesses’ ability to derive the required and sustainable value from data. Data-as-a-product can help reach the maximum potential of data as an asset.
- Accurate Data: The maximum potential of data is achieved when it is accurate and trustworthy. In treating data-as-a-product, the accountability of each domain ensures that there is contextual knowledge and required information in their managed data. It brings more accurate and understandable data to consumers as they can serve themselves with their data requirements without any delay.
- Accessible and consumable Data: When treating data as a product, the intention is to make data accessible and consumable for the consumer. Thus, adequate information is available readily. In this approach, each business domain is responsible for preparing its data and making it accessible to others as well for consumption. Metadata provides data context to consumers to speed up data discovery and access.
- Secure Data: While easy access to valuable data, data governance is equally crucial. The data-as-a-product approach assists in managing and extending data access to all the customers within an organization, including all domains. With managing data-as-a product, appropriate access control – who can view, use and export each data product is involved and tracking all activities performed on any data set. It enables interoperability of organizational domains with global compliance and implementation of necessary policies.
Enterprises need to have an environment that treats overflowing information as a product to sustain the effectiveness and quality of incoming data. To provide quality data, enterprises need to understand the data needs of their teams and the life cycle of that data.
Tapping on a modern-day data capability (Data-as-a-product) can be challenging and brings along ownership within an enterprise. The product thinking in data ensures the underlying analytical and historical data of each domain is intact to safeguard the context of the data.
One such platform that I have used is called the ‘data product platform’ from K2view, which provides the deployment, management, and monitoring of data products and aligns with the contemporary “data-as-a-product concept. By keeping metadata for each data product, the data product studio assists data owners in aggregating and managing data for all business entities (such as a customer, product, location, or any transaction) and further prepares this data for consumers in the form of an integrated data product. To reach enterprise-grade scalability, robustness, and agility, the platform maintains and provides each dataset as a micro-database in an original manner. This can help enterprises unlock the potential of data-driven insights by transforming the relationship between the inflowing data and its consumers.
As data drowns in centralized data platforms, such as data lakes or warehouses, requiring more people and tools for management has proven to be limited in achieving business outcomes. Thus, the data mesh architecture treats data-as-a-product as the new way to empower each domain to unravel insights from data. A data as a product approach brings an agile way of managing data at every level of an enterprise and makes data more accessible to all stakeholders.