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Understanding Data Products and Data Contracts: Building Trust in Modern Data Management

Learn how data products and data contracts transform raw data into reliable assets. Explore the roles of Code & Config, Metadata & Infrastructure, and domain management in ensuring data quality, access control, and trust within your organization

By

Jatin S

Updated on

October 28, 2024

In this AI era, data is the heartbeat of every modern organization. But raw data, much like crude oil, isn't immediately useful. It needs to be refined and transformed into something meaningful. This is where Data Products and Data Contracts come in, helping organizations manage their data better while ensuring that everyone—whether they’re a data engineer or a business leader—can trust the information they’re working with.

Let's break these concepts down in a simple, approachable way, so even a 10th grader could understand the immense value they bring to data teams.

What Exactly Are Data Products?

Think of data products as specially crafted, ready-to-use packages of data designed to solve specific business problems or support particular processes. Whether you're building an AI model or performing an analysis, these "products" are the carefully curated and cleaned-up data sets you rely on to get the job done.

Just as physical products are assembled with care and quality control, data products go through various processes to ensure they are accurate, reliable, and fit for purpose. They aren’t just any collection of data thrown together—they are the result of carefully designed systems that ensure the data is useful.

Diving Deeper into Code & Config

The backbone of any data product is its Code & Config. This is the engine that powers the transformation of raw data into polished, usable products.

  1. Pipeline Code for Consumption, Transformation, and Serving: This is where the raw data gets filtered, cleaned, and reshaped. Think of it as a factory where the raw ingredients (data) are processed into the final product (the curated dataset). Pipelines ensure that data is transformed consistently and reliably, following the same steps every time.
  2. Configuration for Data Quality Checks, Policies, and Thresholds: Data quality is critical. It’s not enough to just have data; you need to be sure that it’s correct, up-to-date, and meets the standards that your business needs. Configuring rules and thresholds helps data teams automatically check for issues like missing values or inconsistent formats, reducing the risk of errors down the line.
  3. Data Governance—Access Control & Logging: Data governance ensures that data is handled responsibly. This means defining who can access what data, tracking who interacts with it, and ensuring that all actions are logged. Access control is especially important when dealing with sensitive or regulated information, and governance policies ensure that data usage complies with legal or company guidelines.
  4. Data Product-Specific Infrastructure & Access Control: Every data product may have unique infrastructure requirements. Some data might need high levels of security or specific storage solutions. This component makes sure the data is served with the correct security and access layers, ensuring that only authorized users can access it.

Expanding on Metadata & Infrastructure

When we talk about Metadata & Infrastructure, we’re talking about the "information about the information." It's like a map that tells us where the data came from, what it means, and how it can be used. Metadata is vital for navigating the complex world of data.

  1. Data Observability: In the same way that a doctor monitors a patient's vital signs, data observability tracks the health of your data pipelines. It helps ensure that data flows smoothly, without unexpected errors, missing records, or other issues that could affect your business.
  2. CI/CD Pipelines: Continuous integration and continuous delivery (CI/CD) pipelines allow teams to deploy changes in their data processes quickly and efficiently. These pipelines automate the testing and rollout of new data products or features, ensuring the system remains reliable even as things evolve.
  3. Catalog—Exposing Technical & Business Metadata, Alerts, and Metrics: A data catalog acts like a well-organized library, making it easy for anyone in the organization to find the data they need. It exposes both technical details (like where data lives and its format) and business definitions (like what the data means for a company’s operations). Alerts and metrics add another layer of visibility, helping teams quickly respond to any issues that crop up.
  4. Outcome Interfaces (APIs, BI Reports, Dashboards): This is how end-users interact with data products. APIs allow other systems or applications to tap into your data. Business intelligence (BI) reports and dashboards, on the other hand, present the data in a visual format, making it easier for humans to interpret and act on.

Sample Config files:


# Data Product Configuration for Sales Data Pipeline
data_product:
  name: SalesDataPipeline
  version: 1.2
  description: "Processes and serves sales data for consumption by the business intelligence team."
  domain: Sales  # Domain management to group data by business function
  access_control:  
    roles:
      - role: sales_analyst
        access: read-only  # Sales analysts have read-only access
      - role: sales_manager
        access: read-write  # Sales managers can read and update records
      - role: data_engineer
        access: admin  # Data engineers have full control over the pipeline

Domain Management and Access Control

An important concept in modern data management is domain management. As organizations grow, they often split their data into different domains—logical groupings of data based on business functions, such as Sales, Marketing, or Finance.

Managing these domains is crucial to maintaining order and security. By organizing data into domains, you ensure that only the right people have access to specific datasets. Each domain might have different rules for how data is processed and accessed, reflecting the unique requirements of each department or team. Access control within domains is critical to ensure that sensitive information is protected, and users only see the data they are authorized to work with.

For example, sales teams should not have unrestricted access to financial data, and vice versa. Domain-based access control allows businesses to apply fine-grained security, ensuring both privacy and compliance with regulations.

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Data Contracts: Bringing Order and Trust to Data Management

Now that we have a clear understanding of what data products are, let’s move on to data contracts.

A data contract is essentially a formal agreement between the data producers (those who generate or prepare the data) and the data consumers (those who use it). It’s a mutual understanding that defines what the data should look like, how often it will be updated, and what quality thresholds it must meet.

Think of it like ordering a meal at a restaurant. You expect certain standards—the food should be fresh, cooked properly, and delivered on time. A data contract works in a similar way, ensuring that data consumers get the "meal" they need without having to worry about whether it will meet their expectations.

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Benefits of Data Contracts for Producers and Consumers

  1. For Data Producers:
    • Clarity on Expectations: With a data contract, producers know exactly what the consumers need. This reduces miscommunication and ensures that they deliver the right data, at the right time, in the right format.
    • Streamlined Data Pipelines: Knowing exactly what data is required allows producers to optimize their processes, eliminating unnecessary steps and improving efficiency.
  2. For Data Consumers:
    • Trust in Data Quality: With contracts in place, consumers can trust that the data they’re using is reliable and meets the agreed-upon standards.
    • Reduced Data Errors: Since contracts define quality thresholds and validation steps, consumers experience fewer data-related issues, saving time and resources.

Building Trust in Data Ecosystems

At the heart of all this lies trust. Without trust, data cannot be effective. Data contracts and the careful design of data products help build that trust. When everyone in the data ecosystem—from engineers to analysts—knows what to expect, data flows more smoothly, leading to faster decisions and greater innovation.

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Wrapping Up

In the modern world, data is more than just numbers and facts. It’s a critical asset that fuels decision-making, innovation, and business success. By turning data into products and establishing clear contracts between those who produce and consume it, organizations can unlock the full potential of their data, ensuring reliability, accuracy, and trust at every step.

And this is where Decube excels. We take care of the complex details—like observability, data contracts, and domain management—so your teams can focus on what truly matters: leveraging data to drive success.

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What is a Data Trust Platform in financial services?
A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
Most AI initiatives in LATAM fail due to poor data quality, fragmented architectures, and lack of governance. When AI models are fed stale or incomplete data, predictions become inaccurate and untrustworthy. Establishing a Data Trust Strategy ensures models receive fresh, auditable, and high-quality data, significantly reducing failure rates.
What are the biggest data challenges for financial institutions in LATAM?
Key challenges include: Data silos and fragmentation across legacy and cloud systems. Stale and inconsistent data, leading to poor decision-making. Complex compliance requirements from regulators like CNBV, BCB, and SFC. Security and privacy risks in rapidly digitizing markets. AI adoption bottlenecks due to ungoverned data pipelines.
How can banks and fintechs monetize trusted data?
Once data is governed and AI-ready, institutions can: Reduce OPEX with predictive intelligence. Offer hyper-personalized products like ESG loans or SME financing. Launch data-as-a-product (DaaP) initiatives with anonymized, compliant data. Build API-driven ecosystems with partners and B2B customers.
What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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