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Sep 9
Sand Technologies
Like most companies, this utility has treated data as a technical byproduct of business operations—dumping it into data lakes or storing it away in spreadsheet silos. This traditional approach to data makes it difficult to access and evaluate the quality.
However, by shifting the company mindset to data as a product and treating critical data assets with the same rigor, customer focus, and lifecycle management as an IT development project, companies can unlock the data’s actual value and drive consistently better business outcomes with their AI initiatives.
The accelerating growth of artificial intelligence and machine learning has turned the “data as a product” approach from a best practice into a business imperative. AI models are powerful, but they have a fundamental dependency: they are only as good as the data used to train them.
When a company’s data consists of untrusted, poorly documented, and siloed assets, AI initiatives will inevitably fail. Models will produce unreliable predictions, exhibit hidden biases, and fail to deliver business value. In fact, poor data quality is one of the top reasons that AI projects fail.
Adopting a data-as-a-product strategy is the key to developing the trustworthy, high-caliber datasets that successful AI models require. Trustworthy, well-documented, and discoverable data products enable AI teams to move faster, experiment more effectively, and build models that are more accurate, fair, and explainable. In the age of AI, competitive advantage isn’t just an algorithm—it’s the quality and usability of your data products.
Transitioning data from a byproduct to a primary product is more straightforward than it appears. In fact, a proven blueprint for success already exists in a parallel discipline: product development. The very same principles that guide the creation of successful software, services, and physical goods provide a robust and practical framework for managing data. This section directly compares the two disciplines, highlighting how the principles of one can revolutionize the other.
The vision and overall success of critical datasets require dedicated stewardship. The Data Product Owner is directly accountable for the quality, usability, and strategic roadmap of the data, ensuring it consistently meets the evolving needs of its internal consumers. Responsibilities include defining data standards, establishing clear data governance policies, and implementing robust quality control measures to guarantee accuracy, completeness, and reliability.
Other responsibilities include anticipating future data requirements, identifying potential data sources, and driving initiatives to enhance data accessibility and value. They act as a crucial liaison between data producers and data consumers, translating business requirements into technical specifications and ensuring that data solutions align with organizational objectives.
Much like any software product, a data product’s usability hinges on clear instructions and an intuitive onboarding experience. In the realm of data, this translates directly to a robust data catalog, enriched with detailed metadata.
A data catalog acts as the central hub for all data products, serving as the single source of truth when effectively implemented. It enhances discoverability, allowing potential users to easily find the data they need without extensive internal searching or reliance on individual experts. The documentation must provide unambiguous definitions for all data elements, ensuring consistent understanding across the organization, including precise data schemas that outline the structure and types of data within each product.
Adequate documentation should provide practical use examples, demonstrating how to query, integrate, and interpret the data effectively. This level of detail empowers consumers to “self-serve” with confidence, reducing the need for constant support from data producers and fostering a more agile and independent data culture. The ability for users to understand and utilize data products independently significantly increases their adoption and impact, ultimately contributing to the overall success and value of the data initiative.
When a new product launches, the company markets it. When a new high-quality dataset is ready, it too needs evangelism and discovery. This process involves communicating the purpose, content, quality, and potential applications to a broad audience within the organization. Good strategies to increase awareness are internal presentations or Workshops, internal newsletters and announcements, data community forums, and showcasing success stories and case studies. These strategies introduce new datasets, explain their features, and demonstrate potential use cases.
A crucial element for data discoverability is a comprehensive and easily accessible central data catalog. This catalog serves as the single source of truth for all available datasets, providing essential metadata and context. A robust catalog should facilitate data discovery through strong search and filtering features, offer comprehensive metadata, enable data previews and samples, and seamlessly integrate with data governance tools. Facilitate feedback and collaboration sessions to allow users to provide feedback on data quality, suggest improvements, or request new datasets.
Robust customer support backs all successful products. Data products also necessitate a strong foundation of data stewardship and dedicated support mechanisms. This foundation ensures data reliability, usability, and user satisfaction. Establishing a clear and easily accessible channel is crucial to enable users to pose questions effortlessly, report any issues they encounter, and submit requests for improvements or new features.
Organizations can cultivate a culture of data literacy and ensure that their data products are not only technically sound but also genuinely meet the needs of their users, leading to increased adoption and value generation. To accomplish these goals, this support infrastructure should encompass:
To effectively manage data as a product, it is crucial to establish a robust schema and lifecycle management process, defining a clear and consistent data structure from the outset, so the data is organized and easily accessible for its intended users. The schema should be rigorously documented, including data types, formats, relationships, and business definitions, to foster clarity and reduce ambiguity. Don’t forget to consider unstructured data and open-source data. These data types, cleaned, organized, and managed properly, can provide even deeper insights when coupled with traditional structured data.
Managing changes effectively after the project launch is crucial. As business needs evolve or data sources change, modifications to the data structure will be necessary. To minimize disruption, the change management process should guide the team in reviewing, approving, testing, and implementing proposed changes. Versioning of schemas is a key practice, allowing consumers to understand which version of the data product they are utilizing and providing a clear upgrade path.
Finally, a well-defined retirement process for datasets is critical. Data products can become obsolete, inaccurate, or redundant over time. The lifecycle management process must include criteria and procedures for identifying when a dataset is no longer needed. Sunsetting data involves communicating obsolescence to consumers well in advance, providing alternative solutions if necessary, and ensuring that data is securely archived or purged in compliance with data retention policies and regulations. Proper lifecycle management ensures that data products remain valuable, accurate, and relevant throughout their existence, from creation to eventual retirement.
What is the “data as a product” approach? Treating data as a product means applying product management principles to data, viewing it as a valuable, reusable, and self-contained asset designed for end-users, rather than just raw information. This approach includes defined ownership, a clear lifecycle, excellent documentation, usability, and a focus on satisfying customer needs to ensure the data delivers clear business value and accelerates insights and innovation.
The “data as a product” approach necessitates a fundamentally new way of thinking. Instead of viewing data as a messy byproduct of business operations (like “technical exhaust”), you manage it with the same discipline, customer focus, and lifecycle management that you would for a physical or digital product you sell. The ultimate goal is to transform a chaotic “data swamp” into a reliable “data marketplace” where high-quality data is readily available to drive better and faster business decisions throughout the organization.
Thinking of data as a product isn’t just a metaphor; it’s a powerful approach. Adopting a data product mindset is more than just a technical exercise in good governance; it’s a strategic shift that delivers compounding returns across the organization. When data becomes reliable, discoverable, and user-focused, it unlocks new capabilities and efficiencies, transforming from a cost center into a powerful engine for innovation and growth. An actual data product has distinct characteristics that set it apart from a raw table in a database. Together, these characteristics lead to several key benefits.
By adopting product thinking to data assets, companies can transform chaotic, unreliable data into a trusted marketplace of high-quality assets. This shift reduces redundancy, fosters collaboration, and accelerates the generation of insights. Most importantly, it lays the foundation for AI that is not just technically impressive but strategically valuable.
The future of AI will be won not by those with the flashiest models, but by those with the strongest data. Treating data as a product is now a core requirement, not an option.
Learn more about success with AI projects.
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