Data product development is the process of decomposing a monolithic legacy data platform into multiple, well-defined components. This refactoring helps manage the increasing complexity of existing solutions and enables other teams to leverage the data stored within specific data domains.
Many organizations face the challenge of data silos, where information is scattered across disparate systems. This lack of integration often leads to redundant efforts, as teams may unknowingly duplicate work already performed by others. To address this, adopting a “product development” methodology for existing data assets can be highly beneficial. By treating data as a product, data teams can:
- Identify how their data can benefit other teams and departments.
- Develop a compelling value proposition to encourage data sharing and collaboration.
- Refactor their legacy data platforms to support new use cases and drive innovation.
This article outlines the key areas to consider for successful data product development.
What is a Data Product
Data products are self-contained applications that manage and process data. They encapsulate data storage, transformation, and delivery functionalities. Data products interact with other systems by ingesting data into their internal storage. For instance, a CRM data warehouse for Salesforce would synchronize data from Salesforce into its database, handling both ingestion and transformation processes.
Data products also have “output ports” – interfaces that provide data to other data products, teams, or end-users. The nature of these interfaces varies depending on the data product’s role. An infrastructure-focused data product might provide data marts accessible through well-defined datasets or database views, enabling business intelligence users to build dashboards. Conversely, a full-fledged data product might encompass end-to-end functionality, including business applications, AI models, or dashboards.
Developing data products often involves refactoring existing solutions to extract and expose valuable data assets for broader use. This requires more than just technical effort; it necessitates identifying a clear value proposition and potential use cases to justify the investment. Therefore, successful data product development hinges on finding a balance between technical implementation and demonstrating business value.
How to Transition to Data Products
Transitioning to data products requires a fundamental shift in how organizations approach data management and solution development. Unlike traditional business applications, data products focus on acquiring data from various sources, enriching it with business value, and making it accessible through data lakes, warehouses, or dashboards.
Data product development introduces a crucial step: data valuation. This involves assessing the business value of the datasets within a data product, recognizing that their primary value often stems from integrating and blending data from multiple platforms. Therefore, data product managers must actively collaborate with other data product owners, application owners, and business users to uncover and maximize this value.
Defining clear boundaries is also essential. Data product owners must identify the external datasets they will consume and the datasets they will expose to others. This establishes a clear demarcation between the data product under development and other data products within the ecosystem.
Furthermore, transitioning to data products implies a shift towards decentralized development. Multiple data products, each with its own datasets, owners, and development lifecycle, will coexist. This necessitates a federated approach to manage shared resources and ensure consistency. Key considerations include:
- Infrastructure Costs: Avoiding dedicated infrastructure for each data product to optimize resource utilization.
- Data Governance: Aligning data product development with established organizational policies and standards.
Finally, data quality becomes paramount. Data products must guarantee the quality of the data they manage, both internally and externally. This includes validating source data, adhering to data quality standards, continuously monitoring data assets, and transparently communicating data quality metrics.
Successfully transitioning to data products requires addressing these considerations to ensure efficiency, consistency, and alignment with broader organizational goals.
The following infographic summarizes all activities required to develop a successful data product.
Data Product Boundaries
Clearly defined boundaries are crucial for effective data product management. They delineate the data a product ingests, the datasets it stores and transforms, and how it publishes its outputs. Well-defined boundaries simplify management, clarify ownership, and ensure accountability for the solution’s maintenance.
Consider these key areas when establishing data product boundaries:
- Data Sources: Clearly define ownership and responsibilities for each data source. Identify owners and establish clear interfaces between your team and them.
- Unique Functionality: Identify your data product’s unique selling points within the organization. Ensure no other product offers the same functionality.
- Output Interfaces: Specify how data will be delivered (e.g., APIs) and the user interfaces provided. Define how you will provide data, what entities you will publish, and what user interfaces to offer.
To further formalize these boundaries, consider using data contracts. These machine-readable files describe the purpose, schema, and validation constraints for each dataset, ensuring consistency and compatibility across different data products. By clearly defining expectations and responsibilities around data, data contracts promote interoperability and reduce ambiguity.
Data Product Value Proposition
Data product development initiatives require sponsorship, and securing that sponsorship hinges on demonstrating a clear return on investment. Business sponsors need to understand the value a data product brings to the organization. Therefore, data product owners must effectively communicate this value and promote their solutions to potential stakeholders.
Consider these key aspects when defining and promoting your data product’s value proposition:
- Impactful Use Cases: Focus on use cases that directly contribute to business goals. Select a few impactful use cases and include them in your feature set.
- Promote Discoverability: Make your data product easy to find. Promote it in knowledge-sharing sessions and publish your datasets in the data catalog.
- Quantify Value: Estimate the business benefits and development costs of your data product. Estimate how it can help the business and how much it will cost to build.
Beyond the initial value proposition, data products must continuously demonstrate their worth and reliability. Data consumers need assurance that the data they rely on is trustworthy. A robust approach to data quality is essential, involving:
- Data Quality KPIs: Implement metrics that measure the trustworthiness and health of your data assets.
- Transparent Communication: Regularly share data quality KPIs with stakeholders to build trust and confidence in the data product.
Scaling Data Products
Organizations aiming to develop numerous data products must find ways to reduce development costs while maintaining efficiency and quality. While decentralization empowers data product owners and fosters alignment with business goals, it can also lead to inconsistencies and a lack of standardization. Data products should not be entirely independent platforms, and a purely decentralized approach, like a data mesh without common standards and infrastructure, might not be optimal.
Therefore, a successful data product strategy should incorporate scalability while minimizing costs. A major cost factor in data management is often underutilized infrastructure and resources.
To effectively scale data products, consider these key areas:
- Shared Infrastructure: Leverage existing data storage and compute resources. Identify reusable infrastructure to optimize resource utilization.
- Standardized Monitoring: Implement consistent monitoring for infrastructure, platform stability, and data quality. Define standard monitoring endpoints to observe all three areas.
- Automated Deployment: Utilize a GitOps approach to streamline deployment and updates. Implement GitOps to automatically deploy and update multiple data products.
Another critical aspect is data quality incident management. While individual data products monitor data quality within their boundaries, organizations must also consider the interconnected nature of these products. Data flows between products, and a quality issue in one can cascade downstream, impacting other products and potentially disrupting critical business operations.
A centralized data quality incident notification workflow is essential. This includes:
- Clear Communication Channels: Establishing channels like email or dedicated alerting systems for reporting incidents.
- Escalation Paths: Defining escalation procedures to prioritize incident resolution based on severity and impact.
By addressing these considerations, organizations can effectively scale data product development while maintaining cost efficiency, consistency, and high data quality.
Ensuring Data Governance in Data Products
While data products operate with a degree of decentralization, they are not entirely independent entities. They function within a federated model, often sharing infrastructure and adhering to common standards. This presents a valuable opportunity to enforce data governance effectively.
Organizations with strong data governance policies or regulatory compliance requirements can leverage the data product approach to ensure consistent application of these principles. By incorporating data governance standards directly into the data product template, organizations can guarantee that these standards are inherited by all new data products. This promotes uniformity across solutions, data teams, and data domains.
Here are some key data governance areas that can be standardized within data products:
- Data Governance Integration: Align data product architecture with existing data governance frameworks. Review data governance concepts and include them in the baseline architecture.
- Solution Templates: Develop standardized templates to accelerate development and ensure consistency. Prepare easy-to-extend templates to kick off the development of new products.
- Shared Component Integration: Mandate using shared platforms (e.g., data catalogs). Identify shared platforms and require their integration when registering a new product.
Data quality is a critical component of data governance. Organizations often grapple with varying definitions of data quality dimensions and inconsistent measurement methodologies. A robust data quality standard should include:
- Uniform Definitions: Establish clear and consistent definitions for data quality dimensions (e.g., accuracy, completeness, timeliness).
- Required Data Quality Checks: Define a mandatory set of data quality checks that all data products must implement.
By embedding data quality monitoring into the data product template, organizations can ensure that data quality is a core consideration from the outset of every data product development initiative. This proactive approach strengthens data governance and promotes trust in data assets across the organization.
Data quality best practices - a step-by-step guide to improve data quality
- Learn the best practices in starting and scaling data quality
- Learn how to find and manage data quality issues
What is the DQOps Data Quality Operations Center
DQOps is a data observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
