Setting Up Data Product Teams in the Era of Data, AI, and Gen AI: Driving Strategy with Business-Centric Product Oriented Delivery Structures (PODs)
In today’s digital landscape, where data, artificial intelligence (AI), and generative AI (Gen AI) are reshaping industries, data-driven decision-making is essential for businesses to stay competitive. The ability to leverage data and transform it into actionable insights has become a key differentiator. To achieve this, many organizations are setting up dedicated Data Product teams, structured as Product Oriented Delivery Structures (PODs), focused on building data products that drive strategy, efficiency, and growth.
What Are Data PODs and Why Are They Essential?
A Data POD is a cross-functional, business-aligned team consisting of a Business Product Owner, a Data Analyst, and a Data Engineer, working together to manage the entire lifecycle of data products. This structure enables fast, continuous delivery of data products that directly support business needs.
Key benefits of Data PODs include:
- Driving Data Strategy: Aligning data initiatives with business objectives to target key growth areas.
- Effective Data Management: Implementing governance practices to maintain data quality and consistency.
- Accelerated Decision-Making: Delivering insights quickly to empower informed business decisions.
- Collaborating for Innovation: Working closely with AI and data science teams to integrate advanced technologies.
Setting Up Data PODs: The Key Components
To build a successful Data POD, the right mix of roles is essential:
- Business Product Owner (BPO): Aligns the data product strategy with business goals, ensuring that the data product delivers tangible value.
- Data Analyst: Translates raw data into actionable insights, providing clarity and reports for decision-making.
- Data Engineer: Builds the necessary data infrastructure, ensuring data is organized, accessible, and clean for analysis.
Additional support comes from Data Scientists and AI Experts, who collaborate with the POD to address complex analytical tasks and bring AI-powered solutions.
How Data PODs Drive Business-Centric Data Strategies
Data PODs contribute significantly to different aspects of business strategy:
- Data Strategy: With the BPO leading the way, PODs create data products aligned with business objectives.
- Data Management & Governance: Ensuring data accuracy, privacy, and consistency through well-defined governance.
- Data Quality: High-quality data is the foundation for sound decision-making, reducing costly errors and rework. According to Gartner, poor data quality costs businesses up to $15 million annually.
- Faster Decision-Making: Data PODs accelerate the development of insights, giving business units a competitive edge in responding to market changes.
Data PODs as Catalysts for Innovation
Data PODs also foster innovation by facilitating close collaboration between business and technical teams. For example, a POD focused on customer data might partner with data scientists to build an AI-powered recommendation engine, personalizing user experiences and driving customer engagement.
Benefits for Functional Business Units
Business units benefit from Data PODs in several ways:
- Deeper Understanding of Data: Active involvement with Data Analysts and Engineers leads to better decision-making.
- Faster Delivery of Data Products: Teams quickly develop and deliver insights tailored to business needs.
- Strategic Alignment: Data products built by PODs ensure that business priorities are met, helping drive growth and efficiency.
Data PODs and the Future of the Data-Driven Operating Model
As businesses continue to evolve their data strategies, Data PODs are essential to creating a Data-Driven Operating Model (DDOM). This model integrates advanced analytics and AI into everyday business processes, allowing companies to make data-informed decisions that improve performance.
By embedding PODs within business units, companies can ensure data is a key component of strategy development and execution. These teams play a vital role in fostering collaboration between business, technology, and innovation teams, driving the organization’s data capabilities forward.
Conclusion
In the era of data, AI, and Gen AI, Product Oriented Delivery Structures (PODs) offer a proven approach to building and managing data products that align with business goals. These cross-functional teams drive faster decision-making, higher data quality, and greater innovation. By establishing Data PODs, organizations can position themselves to leverage data more effectively, gain competitive advantages, and ensure long-term success.
References
- Gartner Research. (2021). “The Cost of Poor Data Quality.”
- McKinsey & Company. (2022). “Building Data-Driven Operating Models for Business Growth.”
