Organizations constantly seek better ways to manage and extract value from their data assets. One of the most popular approaches companies are following is the concept of Data as a Product, derived from the Data Mesh architecture. While not all companies may initially fully embrace the decentralized principles of Data Mesh, the foundational idea of treating data as a product resonates deeply. This approach enhances data governance and paves the way for maximizing data value.
One key aspect of implementing Data as a Product is determining how to enable this framework from a technical perspective. This necessity has prompted significant technology providers to evolve their Data & Analytics platforms to offer robust solutions. From AWS's DataZone to Microsoft's Purview and Snowflake's private exchange, the industry's shift towards supporting internal data marketplaces and stores underscores the significance of the Data as a Product framework.
Though technical enablement is a fundamental aspect of success, the actual key lies in focusing on organizational design and technical enablement and making these elements work seamlessly together. Zhamak Dehghani, who coined the concept, refers to it as a sociotechnical approach. Fully realizing the benefits of these frameworks requires a harmonious blend of sociological and technical aspects. Despite its appeal, many companies need help implementing and deriving value from Data as a Product.
In this article, we propose our approach to addressing these challenges, ensuring the integration of organizational changes with technical solutions. Recognizing and overcoming these hurdles is crucial to unlocking the full potential of data assets and achieving robust data governance.
Bridging the DaaP operating model with technology enablement
From initial excitement to rapid disillusionment: this is a typical trajectory we observe as companies embark on their Data as a Product (DaaP) journey. When deploying the DaaP model, organizations typically fall into two categories of struggle:
- Centralized D&A struggles: Some companies design a DaaP operating model as part of a centralized Data and Analytics (D&A) initiative. However, they often face significant hurdles in organizational change management and technical enablement. The primary challenge here is understanding the technical capabilities and limitations beforehand, leading to misalignment between the operating model and the organization's readiness for change. Additionally, there is often a failure to deliver the necessary organizational change management across the company, resulting in resistance and lack of adoption.
- Technical first approach: Other companies prioritize technical enablement by purchasing the latest tools. Unfortunately, these tools often go unused, reminiscent of the historical challenges faced with data catalog tools for data governance. Tools that do not fit the organization's operating model remain idle, and the lack of solid data management standards leads to technical implementations that are difficult to govern and do not scale, resulting in low utilization and a failure to realize value.
These approaches frequently culminate in operating models or tools that must be adopted or utilized. Consequently, companies need to achieve the desired value from their Data as a Product initiative.
Thus, we explore these challenges in greater depth and propose solutions, ensuring a seamless integration of organizational changes with technical implementations to unlock the full potential of data assets.
The N-iX framework for bringing your data as a product framework to life
One of the most critical aspects of a data strategy is bridging the operating model with technology. Our building blocks for a modern data platform reference architecture focus on ensuring that the key components-people, process, and data platform-are fully aligned.
These elements must be designed iteratively, as decisions in one area can significantly impact the others. Our unique methodology ensures that the operating model and technical enablement work together seamlessly. Here's how we do it:
N-iX framework for Data as a Product data service design:
- Step-by-step methodology. Our systematic approach ensures a structured delivery of DaaP.
- Validation. We begin with a necessary validation to determine if this model fits your organization, as many companies may still need to be ready for such a sociotechnical transformation.
- Consumer-driven design. We identify and design with the data consumer in mind-what data products they need and who will produce them.
- Technical decisions. Early technical decisions are made regarding data marketplace/data store solutions to ensure that data producers effectively deliver data products to data consumers. The choice of tools will influence the definition and functionalities of data products.
- Organizational design, data governance, and data management. We enable your organization's data design, governance, and management with appropriate technology and architecture.
- Our technology-agnostic reference architecture ensures maximum flexibility and efficiency in building a Data Product Platform.
By following this comprehensive and iterative approach, we ensure that both the organizational and technical aspects of your Data as a Product framework are harmoniously integrated, enabling you to unlock the full potential of your data assets.
Mapping our methodology to AWS DataZone: A seamless integration for Data as a Product
AWS DataZone is crucial in our framework for implementing Data as a Product, ensuring a seamless integration of organizational and technical elements. Here's how:
1. What is AWS DataZone? AWS DataZone is a comprehensive data management platform that simplifies data discovery, governance, and sharing across an organization. It provides robust features such as domain definition, environment creation, data product management, business glossaries, and seamless integration with technical data catalogs. These capabilities facilitate a self-service environment where data consumers can easily find and utilize data assets and products.
2. Our step-by-step approach. Our methodology leverages AWS DataZone's capabilities through a structured, iterative process to ensure your organization can effectively implement the Data as a Product model. Here's how we integrate AWS DataZone into each stage of our approach:
- Definition stage: During the definition stage, we begin by embedding AWS DataZone capabilities to ensure the feasibility and alignment of the defined capabilities with the platform's features.
- Design stage: Leveraging our reference architecture, we design a Data Product Platform tailored to your organization's needs.
- Implementation stage: We match our reference architecture to the specifics of your AWS architecture, including the AWS data zone, illustrating how the platform can effectively meet your organizational needs.
The final stage is a data product platform architecture design with AWS Services:
In addition to the architecture design, we created the DataZone Implementation Toolkit, which defines all artifacts that need to be made in DataZone following the definitions of your data as a product framework.
Why AWS DataZone works well with our framework
AWS DataZone's features align seamlessly with our methodology, enabling the successful implementation of Data as a Product. AWS keeps a rapid development of DataZone, constantly adding new features that allow you to deliver data as a product framework to your organization. Key capabilities include:
- Definition of domains and recently released Domain Units: This feature supports identifying and categorizing data products within various business domains.
- Data portal: It serves the role of a workspace for data producers to create and manage their data assets and product lifecycle, including necessary data governance and data management requirements.
- Business catalog interface: Offers a non-technical, user-friendly interface for data consumers to discover and subscribe to data products and assets, fostering an entirely self-service environment.
- Creation of environments: AWS DataZone supports using different consumption archetypes, adapting to the unique requirements of every data consumer.
- Data products feature: AWS recently released new functionality for delivering data products. It allows you to combine data assets to form comprehensive data products that meet specific business needs.
- Readme, business glossaries, and metadata forms: Provide the flexibility to describe and define attributes for data assets and products as part of the operating model to help data consumers understand the content.
- Technical data catalog integration: This process automatically captures technical metadata and performs data quality checks, ensuring the integrity and usability of the data.
By integrating AWS DataZone with our unique approach, we ensure that the organizational design and technical enablement work together harmoniously, unlocking the full potential of your data assets and achieving robust data governance.
Bringing Data as a Product framework to life requires a careful balance of strategic operating models and technical enablement. At N-iX, we bridge this gap with our structured framework and the powerful capabilities of AWS Data Zone, ensuring that your organization can effectively achieve its data governance and management goals.
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Wrap-up
If your organization is ready to unlock the value of your data assets and achieve robust data governance, consider leveraging our unique approach alongside AWS DataZone. Contact us to discover how we can help you seamlessly integrate organizational changes with technical implementations and transform your data strategy into a well-oiled machine. Let's embark on this data journey together and ensure your organization successfully realizes Data as a Product.
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