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Welcome to DataPro #146: Expert Insight Edition.
We’re excited to bring on board Sagar Lad, Lead Data Solution Architect at a leading Dutch bank, to the Expert Insight edition of the DataPro newsletter. Sagar will be sharing his hard-won lessons, practical tips, and implementation strategies for navigating the challenges of data in the Gen AI and Agentic AI era.
Each week, Sagar will guide you through his in-depth analysis and research, showing what really works in complex production environments. His goal is simple: help you turn concepts into practice and ideas into impact.
This week, he kicks things off with a deep dive into Data Products: Turning Data into Tangible Value. As always, our mission at DataPro is to bring you first-hand, practical insights from industry experts. We believe Sagar’s expertise will provide valuable guidance you can apply directly to your daily data practice.
So, without further ado, let’s jump in.
Cheers,
Merlyn Shelley
Growth Lead, Packt
In today’s digital economy, data has become one of the most valuable assets for organizations. Every transaction, interaction, and process generates data that — when properly harnessed — can unlock powerful insights, drive innovation, and create competitive advantages. However, simply collecting and storing vast amounts of data is not enough. To truly realize its value, organizations must transform data into usable, scalable, and outcome-driven solutions. This is where the concept of adata productcomes into play.
A data product is not just raw data, but rather a packaged, consumable, and value-generating asset built on top of data. Just as traditional products solve customer needs, data products solve business challenges by delivering insights, predictions, or automated decisions in a way that is accessible and reliable for end users.
What is a Data Product?
At its core, adata productis a solution designed around data to serve a specific purpose or generate business value. It could take many forms — such as a dashboard, an API serving machine learning predictions, a recommendation engine, or even a dataset curated for a particular domain.
For example:
→ Netflix’s recommendation systemis a data product built to enhance user engagement.
Characteristics of a data product include:
1. Purpose-driven— It is built to achieve a clear outcome (e.g., increase sales, reduce costs, improve customer satisfaction).
2. Reusable— A well-designed data product can serve multiple teams or applications.
3. Consumable— It is packaged in a way that non-technical users or systems can leverage it seamlessly.
4. Scalable— It is designed to evolve with changing business needs and data volumes.
Data Product: Bridge between Producer & Consumer
Data Products vs. Data Assets
It is important to differentiate betweendata assetsanddata products.
Adata assetcould be a data lake, warehouse, or dataset that stores raw or processed data. While valuable, assets by themselves may not generate outcomes unless someone analyzes them.
Adata product, on the other hand, transforms these assets into actionable, consumable outputs that stakeholders can directly use to make decisions or power business processes.
In other words, data assets are ingredients, while data products are the finished dishes that customers can consume.
Why Do Organizations Need Data Products?
Organizations often struggle with extracting value from their data investments. Billions of dollars are spent globally on data platforms, yet many businesses face the“last mile problem”— where insights fail to reach decision-makers in a meaningful way. Data products help bridge this gap by operationalizing data and embedding it into workflows.
Key benefits of data products include:
1. Faster Decision-Making
With well-packaged insights, business users don’t need to spend hours querying databases or waiting for reports. A data product like a sales forecasting model can instantly provide actionable intelligence.
2. Democratization of Data
Data products abstract technical complexity, enabling business users, analysts, and applications to easily consume data-driven insights.
3. Standardization and Reusability
Instead of rebuilding analytics pipelines repeatedly, a single data product can serve multiple business units. For example, a customer segmentation data product could be reused by marketing, sales, and product teams.
4. Scalability and Automation
Data products, once designed, can be scaled to handle growing data volumes and embedded into automated workflows.
5. Value Realization
Ultimately, data products help organizations move beyond storing data tomonetizing and operationalizing it— whether through cost savings, revenue generation, or improved customer experiences.
Key Principles for Designing Data Products
Designing a successful data product requires more than technical skills — it requires product thinking. Some guiding principles include:
1.Start with Business Value
A data product must solve a real business problem. Before building, clearly define the outcome it should drive.
2. User-Centric Design
The product should be intuitive for its target users, whether that’s executives, developers, or customers.
3. Trust & Transparency
Users must trust the data product. This requires data quality checks, explainability in AI models, and governance measures.
4. Scalability & Reusability
Build products that can adapt to future needs, serve multiple stakeholders, and scale across datasets and domains.
5. Operationalization
A data product should integrate seamlessly into business workflows and systems, rather than existing as a standalone artifact.
6. Monitoring & Improvement
Data products must be continuously monitored for performance, accuracy, and relevance, with feedback loops for improvements.
Challenges in Building Data Products
While data products are powerful, organizations face challenges in creating and scaling them:
1. Data Quality Issues: Poor data leads to unreliable products.
2. Cultural Resistance: Teams may hesitate to trust automated insights.
3. Lack of Product Mindset: Many companies treat data as IT projects, not products.
4. Scalability Hurdles: A data product may work for a pilot but struggle in enterprise-wide deployments.
5. Governance & Compliance: Ensuring data products adhere to regulatory and ethical standards is critical.
Overcoming these requires strongdata governance, clear ownership, cross-functional collaboration, and a product-centric approach.
The Role of Data Mesh and Data Products
The concept ofdata productsis also central toData Mesharchitecture. In Data Mesh, each domain team is responsible for building and managing its own data products, treating them as first-class citizens. This shifts ownership from centralized IT teams to domain experts, making data products more relevant, accurate, and consumable.
By combining Data Mesh principles with robust product management practices, organizations can scale their data strategy while ensuring alignment with business outcomes.
Future of Data Products
The future of data products looks promising as technology evolves:
1. AI-driven Data Products: With advancements in generative AI, data products will become more conversational, adaptive, and personalized.
2. Marketplace of Data Products: Organizations may buy and sell data products just like SaaS solutions, creating new revenue streams.
3. Self-Service Ecosystems: Business users will increasingly be able to design their own data products using no-code/low-code platforms.
4. Embedded Trust & Ethics: As AI governance matures, responsible AI principles will be embedded directly into data products.
Conclusion
Data products represent a fundamental shift in how organizations leverage data. They move beyond static reports or siloed datasets to create reusable, scalable, and outcome-driven solutions. By applying product thinking to data initiatives, companies can ensure that data investments directly translate into measurable business value.
In a world where data is the new currency,data products are the vehicles that convert raw information into tangible value. The organizations that master this art will be the ones that thrive in the data-driven future.
See you next time!