Data Product Rolesand Responsibilities

Justifying investment in data solutions is increasingly challenging, especially in the current economic climate where data leaders must prove value beyond the allure of ‘data is cool’.

While data can unlock significant value, its misuse can lead to inefficiencies, margin gains and sometimes reputational damages. With the rise of GenAI, the bulk of the expenses are shifting from CAPEX to OPEX, requiring ongoing evaluation and analysis of data and analytic assets.

A common challenge I’ve observed in organizations is justifying and verifying the financial impact of customer segmentation engines or propensity models (e.g. predicting a customer’s likelihood to buy an iPhone). The key question is how these advanced analytics products effectively drive and support marketing and customer experience personalization efforts. Implementing a Product thinking approach plays a critical role in addressing this issue.

When business teams are skeptical about data insights or how the data is presented, they end up manually verifying data, creating their own workarounds, or avoiding the data altogether in favor of intuition and expertise (what’s wrong with that some of you would say?). This distrust leads to duplicated efforts, inconsistent data and fragmented insights across the organisation. Put this theory to test and ask your different departments for the number of customers.

This can yield varying figures!

The challenge, then, is to rebuild trust and foster effective collaboration between data, IT, and business teams. This involves lowering barriers for business teams to effectively use data while simultaneously enhancing the data team’s business acumen and promoting knowledge sharing.

Although the most significant challenge is implementing an overarching product mindset across the entire organization, it can be broken down into the traditional technology, people, process framework. A successful data product implementation requires not just the right technology but also effective collaboration, agile processes and consistent standards.

Organisations that have succeeded are those that have integrated technological solutions with collaborative practices.

I recently had a passionate discussion with a peer about the critical need to define and design the data product experience, ensuring that technology, people, and processes are all aligned to support it.

While this topic could certainly merit its own article, it’s crucial to highlight several fundamental roles and responsibilities integral to this experience. Data products are transforming the ways of working across organizations and impacting the employee landscape, from business analysts to cloud architects to data engineers.

Below, I will aim to summarize the key actors involved and explore their roles in a world driven by data products.

Consumers are users who interact with data, either internal or external to the organization.

• Internal Consumers: These are employees, such as business analysts or marketers, who use data to generate reports or drive personalized marketing campaigns through automated decision-making engines.
• External Consumers: These individuals or entities are outside the organization and often use data as part of broader digital products. For instance, a website displaying key performance indicators or organizations leveraging data from third parties for revenue.

Generally, consumers should be able to easily search for data products using keywords or descriptions and assess whether they meet their needs – whether this might be through a marketplace, or a simple data product excel catalogue. Depending on their role, they may access data by default or need to request access. If the required data does not currently exist, they can submit a request detailing the new dataset needed. This request should include the domain from which the data is expected, the potential team responsible, and any specific applications or external sources.

But most importantly, the consumer should provide a business perspective on the data’s intended use and use case. This approach enables the data team to share different insights into the most suitable dataset and therefore, can evaluate options with the consumer to better fulfill the request. This collaborative process helps enhance the insights and relevance of the data provided.

The Data Product Owner is responsible for managing the entire lifecycle of a data product. This role ensures that the data product is used effectively, maintains high quality and relevance, and is neither misused nor underutilized. The Data Product Owner listens to feedback, oversees the product’s evolution in response to changing business needs, and reviews requests from consumers and feedback from users.

While the Data Product Owner is aligned with a specific domain and business unit, they prioritize requests based on value rather than departmental alignment. If a request extends beyond the product’s intended scope or domain, the Data Product Owner coordinates with other Product Owners to redirect or reallocate the request appropriately.

The Data Product Team is central to the Data Product ecosystem. These teams specialize in transforming data from providers into valuable business insights and ensuring its availability to consumers. Each team manages the end-to-end delivery of a specific data product, including debugging, deploying new features, and addressing technical issues.

This team works closely with the Data Product Owner, who is embedded into the team, to understand development needs, acceptance criteria, and requirements, translating these into technical specifications.

The Data Platform Team manages the underlying data infrastructure and technology. They provide essential tools, utilities, and services that support the Data Product Teams, making it easier for them to perform their tasks efficiently and support the consumers in accessing and using the data.

By building and maintaining the infrastructure, frameworks, and services necessary for data product development and consumption, the Data Platform Team removes barriers and optimizes the overall process from a technology perspective.

They are also responsible for onboarding users to the data product platform—whether it’s a marketplace, data repository, or another interface—and continuously enhancing the platform to improve the experience for consumers, Data Product Owners, and development teams.

The Data Enabling Team, though not typically named as such, consists of various specialized groups dedicated to managing changes and supporting data product teams and consumers. For example, they help address obstacles, offer guidance, and promote best practices in data management and governance.

They will help consumers define requirements and use cases, they will help platforms understand what is required and how to enable the data product teams, they will support data product owners in managing their products. By providing this support, these teams ensure that the new processes are effectively integrated and widely adopted by all stakeholders.

By understanding the strengths and responsibilities of each team, organizations can refine roles and interactions to effectively develop and manage data products, addressing both current challenges and future needs.

Roles may vary depending on the organizational structure and it may be valuable to explore different operational models that support data product development in a separate article.

However, as organizations mature, additional roles may emerge to address increasingly complex data and business environments, ensuring that data products continue to meet evolving demands and drive value.