**London**: Many organisations struggle to convert increasing data volumes into actionable outcomes. Adopting the ‘data as a product’ model, which emphasises ownership, quality, and AI-driven usability, offers a scalable, efficient alternative to traditional BI tools and dashboard-centric approaches.
Many organizations have invested extensively in dashboards, data warehouses, and centralized reporting platforms. However, a significant number continue to face challenges in transforming their expanding volumes of data into tangible business outcomes. According to a blog post on DATAVERSITY authored by Alexey Utkin and Oleg Royz, the underlying issue is not the data itself but rather the perspective with which business leaders approach it. They argue that data should be regarded as a product—something that is constructed, maintained, and constantly refined. This approach, termed “data as a product” (DaaP), is increasingly being adopted as companies strive for more scalable and dependable methods to inform their business decisions.
Limitations of Traditional Business Intelligence
Conventional business intelligence tools often provide only a snapshot in time. While these tools serve a purpose, they lack the flexibility and reusability that contemporary organisational needs require. Business teams frequently encounter delays of several days or weeks for new insights, leaving IT departments overwhelmed with competing requests. The data-as-a-product framework seeks to address these constraints by enabling firms to develop standardized, reusable data products—examples being customer 360 views or supply chain health scores. These products are tailored for business users, featuring thorough documentation and quality assurance, and they are delivered via self-service platforms that enhance accessibility and usability.
The Importance of Structure and Ownership
A critical aspect of the data-as-a-product model is a clear ownership structure. This is where the role of the data product owner becomes crucial; this individual is responsible for understanding user needs, managing project priorities, and collaborating with data engineering teams to generate value. Much like any efficient product manager, the data product owner collects feedback, monitors performance, and ensures the product evolves in line with business demands.
Supporting these roles, organizations may implement an operational model that delineates responsibilities. Horizontal teams can concentrate on infrastructural aspects—such as pipelines, storage, and observability—while vertical teams engineer data products specific to particular domains. This division enables specialization without compromising the overarching vision.
Quality Over Speed: An Enhanced Approach
The DaaP methodology goes beyond merely accelerating access to data. It enhances the overall quality and effectiveness of data initiatives through multiple avenues:
- Access to actionable insights is expedited with pre-built data products that are readily available across departments.
- Embedded governance and data quality are integral to each product.
- Cost efficiencies are achieved due to shared infrastructures and reduced redundant projects.
- Increased agility allows teams to pivot swiftly in response to changing requirements.
Moreover, this framework modifies the conversation from “What data do we have?” to “What information do we need for informed decision-making?”
Integrating AI into Data Products
Artificial Intelligence (AI) is becoming increasingly significant within this model. AI can automate various stages of the data product lifecycle, including tasks like metadata generation and anomaly detection. Additionally, it holds promise as an intrinsic feature of the product itself—through functionalities such as recommendation engines, predictive scoring, and conversational interfaces enabling users to query data in natural language.
AI contributes not just to the construction of data products but also enhances their usability and value.
A Sustainable Future for Data Management
As organizations progress in their data utilisation, the shortcomings of project-based and dashboard-driven paradigms have become clearer. The DaaP model presents a viable alternative that prioritizes usability, accountability, and long-term benefits. While it may not be a panacea, implementing such a framework necessitates coordination, investment, and a transformational mindset. For businesses aiming to weave data more seamlessly into their operations, rather than treating it as an isolated service, transitioning to a data-as-a-product approach represents a promising advancement.
Source: Noah Wire Services
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