Principles of Building Data Profitable Products

erstanding the specific purpose and business intent behind the data. Srujan Akula points out, "You start with what is the business intent, what is the use case that you're trying to execute." This approach ensures that the data is not simply an aggregation of raw inputs but is refined, enriched, and pre-processed to deliver value directly aligned with business goals. By embedding product thinking, organizations can eliminate the inefficiencies of being 'data rich and insights poor,' where vast amounts of data fail to translate into actionable insights. Instead, data products become independently valuable units, offering reusable data that drives quicker time-to-value and enhances decision-making processes.

Reliability and Security in Data Products

Building trust in data products is vital, as it affects how data is utilized across an organization. Akula emphasizes the need for setting up the right Service Level Objectives (SLOs) to assure data consumers of the quality and compliance aspects of the data. He states, "The SLOs and the data contracts implicitly will guarantee the trust in data with full audit compliance logging." This trust is key in making data products the single source of truth, allowing data to be shared confidently within and outside the organization. Security is non-negotiable; data products must be secure from a privacy and compliance perspective, often necessitating flexible governance to protect sensitive information while enabling smooth data sharing.

Data Product Accessibility and Reusability

For data products to be effective, they must be easily accessible and reusable. Accessibility is facilitated through a centralized data catalog that allows users to find and understand data products efficiently. Akula stresses, "Having a very simple discoverability, so that based on the use case, you know where to find the data product." Reusability is achieved by designing data products with standard integration options, such as APIs and connectors, which allow them to be used across various applications and business processes. This approach not only enhances productivity but also ensures that data products can serve multiple purposes without necessitating redundant data copies, thereby optimizing resource use and maximizing data's value.

Integration and Compatibility

Integration and compatibility are vital for the successful deployment of data products. Data products should be designed with ready-to-use integration options that allow them to connect smoothly with existing systems, whether it's for powering analytics dashboards or machine learning applications. Akula notes, "These ready-to-use integration options allow you to quickly deliver the data in the right formats at the right cadence." This capability ensures that data products can operate across different cloud environments and data infrastructures, providing a consistent consumption-ready layer that decouples data usage from data management. As businesses increasingly rely on data-driven insights, having a flexible and compatible data product layer is essential for maintaining agility and scalability.