Transforming Data into Products: Key Concepts and Benefits

Learn about data as a product, emphasizing quality, usability, and strategic advantages in data management.

The Data Engineering Summit 2024 in Bengaluru, India, showcased a plethora of innovations and discussions in the realm of generative AI and data engineering. Among the prominent speakers was Praveen Singh, Director of Data and ML Engineering at PayU. With over a decade of experience in developing distributed applications and Big Data solutions, Praveen has worked in various organizations. In his insightful talk, he delved into the concept of treating data as a product, a transformative approach in data management and analytics.

Understanding Data as a Product

Praveen began by defining the concept of “data as a product,” highlighting its theoretical underpinnings and practical benefits. According to IBM, data as a product involves treating datasets as standalone products designed and maintained with end-users in mind. This approach integrates product management principles into the data lifecycle, emphasizing quality, usability, and user satisfaction. Unlike traditional data products that focus on generating insights through dashboards or predictive models, Ddata as a product adopts a holistic methodology encompassing data quality, accessibility, and strategic advantage.

Traditional Data Management vs. Data as a Product

Praveen outlined the key differences between traditional data management and the data-as-a-product approach. Traditional methods prioritize storage, retrieval, and basic analytics, often resulting in a reactive approach to data issues. In contrast, data as a product ensures a proactive stance, encompassing the entire data lifecycle—from data quality checks and metadata management to real-time processing and API availability. This proactive approach minimizes reactive troubleshooting, enhancing decision-making and strategic planning.

Foundations of Data as a Product

Discoverability and Addressability

One of the foundational aspects of data as a product is the ease of data discovery. Praveen emphasized the importance of robust metadata management and indexing tools like OpenMetadata. These tools facilitate seamless data search across diverse sources, whether SQL databases, NoSQL databases, or message queues. Moreover, the integration of generative AI solutions enables intent-based search, further enhancing the discoverability of relevant data assets.

Addressability involves understanding and accessing the specific attributes of data, such as data types, value ranges, and refresh frequencies. Ensuring that data is easily addressable allows stakeholders to effectively utilize it for various analytical and operational needs.

Trustworthiness and Documentation

Trustworthiness is crucial in the data as a product paradigm. Ensuring data accuracy and reliability through rigorous quality checks and monitoring builds user confidence in the data product. Comprehensive documentation, including data lineage, refresh schedules, and usage guidelines, further enhances trust by providing transparency and clarity on data usage.

Integration and Security

Integration capabilities are vital for a seamless data ecosystem. Praveen discussed the importance of syncing data products with other systems, ensuring interoperability across different data platforms. For instance, integrating an orchestration engine with a data discovery platform enables streamlined data management workflows.

Security remains a top priority in the data as a product framework. Protecting sensitive data, especially Personally Identifiable Information (PII), is paramount. Implementing robust security measures and complying with regulatory requirements like GDPR and the upcoming DPDP Bill in India ensures data privacy and protection.

Designing and Developing Data Products

The lifecycle of a data product mirrors that of traditional software development, encompassing four main phases: conception, development, deployment, and maintenance.

  1. Conception: This phase involves ideating and defining the objectives, target audience, and business impact of the data product. Stakeholder engagement and requirement gathering are critical components of this phase.
  2. Development: Transforming requirements into code, followed by rigorous testing (e.g., test-driven development), forms the core of the development phase. Ensuring that the data product meets the predefined objectives and standards is essential.
  3. Deployment: Once developed and tested, the data product is deployed on servers, making it accessible to end-users. Phased rollouts and continuous monitoring are integral to successful deployment.
  4. Maintenance: Post-deployment, ongoing maintenance ensures the data product remains functional and up-to-date. Regular performance checks, alert configurations, and proactive troubleshooting are key to effective maintenance.

Roles and Responsibilities in Data Product Management

Data product ownership is multifaceted, requiring a blend of technical and business acumen. Data product managers (DPMs) play a pivotal role in defining the vision, prioritizing features, and aligning the product with business goals. Acting as a bridge between technical teams and business stakeholders, DPMs ensure that the data product meets user needs and delivers tangible business value.

Challenges and Solutions in Data as a Product

Praveen highlighted several challenges in adopting the data as a product approach, including data silos, talent shortages, and collaboration hurdles. Overcoming these obstacles requires a strategic approach to data integration, talent acquisition, and fostering cross-functional collaboration.

Netflix Recommendations

Praveen cited Netflix’s recommendation engine as a prime example of a successful data product. By analyzing user viewing patterns and preferences, Netflix delivers personalized content recommendations, enhancing user engagement and satisfaction.

Media Analytics

In another case study, a media organization leveraged data to optimize screen time for characters, providing directors and writers with insights to enhance viewer engagement. This data-driven approach to content creation exemplifies the strategic use of data as a product.

Ride-Sharing Optimization

Ride-sharing platforms like Uber and Ola use data products to optimize routes, match drivers with passengers, and enhance overall service efficiency. These applications showcase the practical benefits of integrating data products into operational workflows.

Looking ahead, Praveen emphasized the potential of generative AI and large language models to revolutionize data products. These technologies can enhance data discovery, ETL processes, and analytics, driving further innovation and efficiency.


Praveen Singh’s insightful session at the Data Engineering Summit 2024 underscored the transformative potential of treating data as a product. By adopting this holistic approach, organizations can unlock new levels of data quality, usability, and strategic advantage. As the data landscape continues to evolve, embracing the data as a product methodology will be key to staying ahead in the competitive world of data engineering and analytics.

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