Decoding Data Products: Why Enterprises Need It

Businesses possess a wealth of data but struggle to translate it into actionable insights. This is where data products come into play, they are curated and processed data assets packaged as user-friendly tools. Unlike data-as-a-product (DaaS), which is mainly focused on external consumption, data products empower internal stakeholders with targeted and relevant insights, transforming data into a strategic asset for informed decision-making.

These products are meticulously designed to address strategic business objectives and support decision-making across various functions. They provide predictive capabilities for trend analysis, prescriptive analysis for recommendations based on historical data and automated workflows for operational efficiency.

How to Make Data Products Work: Emphasis on Discoverability, Security and User-Centric Design

The construction of data products represents merely the initial phase in an organization's data journey. The subsequent stage is the crucial one which revolves around maturing the data product. This phase enhances the data product's discoverability through the incorporation of data catalogs and easy access via unique identifiers. Prioritizing trust-building measures coupled with robust security protocols are instrumental to nurture data products and generate value. Using this methodology, organizations can turn their data products into powerful tools that drive informed decision-making.

Let’s break down each of these aspects in detail:

Finding the Right Data

The key to any successful data product is discoverability. Whether through a simple internal wiki list or a comprehensive data catalogue, users should be able to find the information they need easily. Effective data catalogues provide crucial details like data ownership, source origin and sample datasets to give users a clear understanding of what they're accessing.

Unique Identifier for Quick Access

Think of addressability as the storefront of your data product. By assigning each product a unique identifier, users can locate it effortlessly, saving valuable time for both them and data teams who might otherwise be fielding location inquiries.

Data that is Easy to Analyze

Empowering users with self-service capabilities is key. Data products should provide rich metadata – data about the data itself – that users and even automated systems can easily understand. This metadata might include details about the data sources used in the product's creation, schema and information on the format of its outputs. Imagine a data product that not only provides results but explains how it arrived at them, fostering trust and confidence in its users.

Building Trust Through Transparency

For users to fully embrace a data product, they need to be confident in its trustworthiness and security. Defining and communicating a clear set of Service Level Objectives (SLOs) upfront is crucial. These SLOs outline the level of quality and performance users can expect. Similarly, establishing measurable Service Level Indicators (SLIs) is essential to track progress towards those goals. Implementation of automated testing mechanisms allows for regular monitoring and reporting on these SLIs, ensuring that data products consistently deliver reliable results.

Security as Top Priority

Data products must be built with robust security measures in place. While registered data sets should be easily discoverable, access should not be automatic. A robust access control system should be implemented, where users request access to specific datasets and data owners have the authority to approve or deny such requests. This federated approach ensures data security while facilitating authorized use.

Conclusion

Unlike the linear nature of Software Development Life Cycle (SDLC) processes, data products benefit from iterative development cycles. This allows for continuous improvement through user feedback, ensuring the data remains relevant and addresses user needs effectively. Also, data quality and usability are of paramount importance. Last but not least, the development process should prioritize discoverability and addressability. Data products should be readily available to authorized users through intuitive interfaces or search functionalities.

By designing a development process that incorporates data quality, usability, discoverability, addressability and security, organizations can transform their data into valuable tools that deliver actionable insights.

- By Santosh Kulkarni, EPAM’s Director of Data Analytics Consulting