Data Product Management - (Part 12) Collaboration with the Tech & Data Teams

A data product manager’s vision and strategy are only as strong as the team behind them. Without the collective effort of engineering and data teams, even the most innovative ideas would struggle to make an impact. A data product manager alone cannot transform a data ecosystem or bring about meaningful change. It is the collaborative effort, where each team member contributes their unique expertise, that drives successful outcomes and the realization of a product’s full potential.

Collaboration is the cornerstone of any successful product development process. When individuals come together, bringing diverse skills and perspectives, they create a synergy that amplifies their impact. As Henry Ford put it, "Coming together is a beginning, staying together is progress, and working together is success." This quote underscores the essence of collaboration—it's not just about assembling a team, but about maintaining unity and working in harmony to achieve shared goals. The ability to collaborate effectively not only accelerates progress but also ensures that the product delivered meets the highest standards of quality and innovation.

According to research by McKinsey & Company, 40% of a product manager’s time is directly spent with their partners, and of course, the rest is also indirectly spent with others. I believe that this is where the magic happens—where ideas are refined, challenges are overcome, and great value is created for users. This article is part of my series to emphasize the critical role of collaboration in data product management, exploring how these partnerships can drive meaningful change and deliver impactful products.

Environment that Helps to Succeed

Creating an environment where data product managers can thrive and collaborate effectively with engineering and data teams requires careful consideration of several key factors. These factors shape the way teams operate, interact, and ultimately deliver value (Bootcamp). In this section, we'll explore the crucial elements that contribute to a successful collaborative environment:

Team Structure

The structure of a team plays a crucial role in how effectively a data product manager can collaborate with engineering and data teams. A key question to consider, as highlighted in a McKinsey article, is: "Are you a team of learners, or do you learn as a team?" A team of learners focuses on individual growth, which can lead to isolated efforts and potential silos, making collaboration more challenging. In contrast, a team that learns together emphasizes collective knowledge sharing and problem-solving, fostering a more collaborative and innovative environment.

To achieve success, teams should establish learning goals that are closely aligned with their intended outcomes, fostering a culture of collective mastery and shared vision. Instead of merely focusing on individual expertise, the emphasis should be on continuous team learning. For example, transitioning from a “know it all” mindset to a “learn it all” approach can be achieved by incorporating initiatives like open learning days and providing platforms for collaborative learning. It’s important to recognize moments that trigger the need for new knowledge—whether it’s a new project, a shift in team structure, or a challenging situation—and ask, “What do we need to learn to meet our objectives?” Creating an environment that encourages team learning, particularly one that prioritizes psychological safety, is crucial. When team members feel safe to share ideas and take risks, they are more likely to engage in meaningful learning, leading to improved performance and greater success.

Different Operation Models

The structure and operation models within a team significantly influence how members collaborate, communicate, and achieve their objectives. According to Asana, here’s a brief overview of ten common operation models, each with its unique impact on collaboration:

1. Hierarchical Structure: This traditional model organizes teams in a pyramid, with clear levels of authority from executives to employees. It provides a straightforward reporting structure and clear career paths but can create silos that hinder cross-functional collaboration.
2. Functional Structure: Teams are grouped based on specific skills and expertise, such as marketing, engineering, or finance. This model allows for specialization and field expertise but can lead to isolated departments that may struggle to collaborate effectively with other teams.
3. Matrix Structure: This model combines functional and project-based structures, allowing team members to report to multiple leaders. It fosters collaboration across different departments and units but can create complexities in managing dual reporting relationships.
4. Process-Based Structure: Focused on internal processes rather than departments, this structure is ideal for organizations that prioritize efficiency and process optimization. It encourages collaboration around key processes but may limit flexibility in responding to external changes.
5. Circular Structure: This model visualizes the organization in concentric circles, with leadership at the center and lower levels radiating outward. It promotes fluid communication and strong connections across all levels but is best suited for smaller teams.
6. Flat Structure: A flat structure minimizes hierarchical layers, promoting direct communication and collaboration between all levels. It encourages a unified team approach but may lack the clear career progression found in more hierarchical models.
7. Network Organizational Structure: Teams are organized into networks, often based on geographical locations or specialized functions. This model enhances communication within networks but may complicate cross-network collaboration if not managed carefully.
8. Product-Focused Divisional Structure: Teams are divided by product lines, with each division responsible for a specific product. This model fosters deep expertise and accountability within product teams but can create competition for resources between divisions.
9. Market-Focused Divisional Structure: Similar to the product-focused model, this structure organizes teams around specific markets or customer segments. It allows for tailored strategies and deep market understanding but may result in duplication of efforts across different market teams.
10. Geographical Divisional Structure: Teams are organized based on geographic regions, making it easier to cater to local needs and markets. This model enhances regional focus and communication but can lead to challenges in maintaining consistency across different locations.

These operation models shape the way data product managers collaborate with their teams and partners. Understanding the specific structure of your organization is crucial, as it influences how information flows, how decisions are made, and how effectively teams can work together to achieve their goals. Adaptability and awareness of these models enable product managers to navigate their roles more effectively, ensuring that collaboration is optimized regardless of the structure in place.

Leadership Support

Leadership support is a cornerstone of successful collaboration between data product managers, engineering, and data teams. Effective leaders not only provide the necessary resources and guidance but also cultivate a culture that prioritizes teamwork, innovation, and mutual respect. Their influence sets the tone for how teams interact, make decisions, and overcome challenges together.

Supportive leadership is essential for fostering an environment where collaboration can thrive. Leaders who actively promote and model collaborative behaviors encourage their teams to do the same. This involves breaking down silos, facilitating open communication, and ensuring that every team member feels valued and heard. Moreover, leaders play a critical role in removing obstacles that might hinder collaboration, such as resource constraints, misaligned priorities, or unclear goals. By addressing these barriers, leaders enable their teams to focus on what truly matters—delivering high-quality data products that drive business success.

Furthermore, the role of leadership extends to aligning the team's efforts with the broader organizational vision. When leaders clearly communicate the strategic goals of the organization and how each team’s work contributes to these objectives, it fosters a sense of purpose and unity among team members. This alignment ensures that all teams—whether in data, engineering, or product management—are working towards a common goal, enhancing the effectiveness of their collaboration. In essence, strong leadership support not only empowers teams to perform at their best but also ensures that their collaborative efforts are strategically aligned with the organization's long-term success.

Organizational Definition of the Product Management Job Family

The way an organization defines the role of a product manager can have a profound impact on how that individual operates within the team and collaborates with others. As Marty Cagan insightfully points out, there's a significant difference between a product manager on an empowered product team, a product manager on a feature team, and an Agile product owner on a delivery team. These distinctions are not just semantic—they fundamentally alter the authority, responsibilities, and influence a product manager has.

In many organizations, the titles of these roles are used interchangeably, which can create confusion and misalignment. Companies often label all three types as "product managers," but the reality is that the scope and impact of each role can vary dramatically. It’s essential for product managers to understand which category their role falls into, as this will dictate their level of authority, decision-making power, and the nature of their relationships with engineering and data teams.

Comparing an empowered product manager to a product owner highlights these differences. An empowered product manager is responsible for the end-to-end product strategy, with the autonomy to make critical decisions that shape the product's direction. This role involves deep collaboration with cross-functional teams, driving innovation, and ensuring the product aligns with user needs and business goals. In contrast, a product owner on a delivery team often focuses on managing the backlog, prioritizing features, and ensuring that the team delivers on specific tasks. While both roles are vital, the product owner's influence is typically more execution-focused, with less strategic input. Understanding whether your role is that of an empowered product manager or a product owner is crucial, as it affects how you collaborate with your team, influence the product roadmap, and contribute to the overall success of the organization.

The Art of Working with People

At the heart of successful collaboration lies the ability to work effectively with people. This skill is essential for data product managers who must navigate complex relationships with engineering and data teams, as well as other stakeholders across the organization. While technical expertise and strategic thinking are critical, the human element of product management—fostering communication and inspiring collaboration—can make the difference between a good product and a great one.

No matter what product you are working on or how your organization defines job families and structures, at the end of the day, your role revolves around working with people. Most of your time as a product manager is spent engaging with others, making it crucial to see yourself and your colleagues as humans first. It’s essential to act with empathy and remind ourselves that collaboration is about working together, not making things more difficult by falling into office politics. Such politics can quickly ruin the spirit of the team and make every collaborative effort painful. To truly succeed, product managers must focus on building genuine, supportive relationships that foster a positive, productive environment where everyone is aligned and motivated to achieve common goals.

Bridging Perspectives: Understanding Engineers' Views on Product Managers

Empathy is often hailed as one of the most crucial skills for product managers, particularly when it comes to understanding and designing for users. However, empathy shouldn't be limited to just external users—it’s equally important within our own teams. A truly effective product manager must cultivate a deep sense of empathy for everyone they work with, starting with their partners in engineering and data. The ability to put ourselves in the shoes of our team members, to understand their pain points, challenges, and motivations, is what ultimately strengthens our partnerships and fosters better collaboration.

The first place to build and refine this empathetic approach is within our own teams. As product managers, we work closely with tech and data professionals who are integral to the success of our products. By understanding their perspectives, recognizing their concerns, and valuing their contributions, we can create an environment where collaboration thrives. Empathy allows us to anticipate issues, address conflicts proactively, and support our teams in a way that aligns with their needs. It transforms the way we interact, making us not just better managers, but better partners, committed to achieving shared goals together.

How Our Partners Perceive Product Management

The expectations that engineering and data teams have for product managers are centered around a few key responsibilities that are crucial for effective collaboration and successful product delivery. First and foremost, teams want product managers who can truly own the problem statement. This means having a deep understanding of the challenges at hand and taking full responsibility for guiding the team towards the right solutions. Product managers are expected to build strong connections with users, ensuring that the user’s needs and pain points are clearly understood and translated into actionable priorities.

Organizing these priorities is another critical expectation. Teams rely on product managers to clearly delineate what’s most important, allowing them to focus their efforts on the tasks that will deliver the highest impact. Delivering value to users in a compelling and timely manner is essential, and this requires the product manager to not only guide the team but also to evangelize the product—showcasing its benefits and the team’s contributions to the broader organizational initiatives.

Additionally, teams look to product managers to influence stakeholders on timelines and expectations. The ability to negotiate and push back when necessary ensures that the team can work within realistic constraints, maintaining a balance between ambition and feasibility. In summary, product managers are expected to be strategic leaders who can bridge the gap between user needs, team capabilities, and organizational goals, all while fostering a collaborative and supportive environment.

Beyond these roles, one of the most critical expectations is that product managers act as the team's advocate and defender during conflicts. When failures or errors occur within the data ecosystem—inevitable in any complex environment—conflicts can arise between teams. In these situations, the team depends on the product manager to be well-informed and aligned with the issues at hand. A product manager who understands the challenges can effectively defend the team, ensuring that blame is not unfairly placed and that the team’s efforts are recognized. This defense is crucial for maintaining team morale, as it shows that their leader is committed to protecting their interests and acknowledging their hard work, even in tough times.

Engaging in Technical Discussions

In my article, “The Controversy of Technical Knowledge in Product Management,” I explored the ongoing debate about how technical product managers should be and what level of technical understanding is truly necessary. While the depth of technical knowledge may vary depending on the product and organization, for data product managers, the stakes are particularly high. Almost every conversation a data product manager has with other team members is inherently technical. It’s essential for us to grasp the concepts being discussed and to be able to connect the dots in our minds so that we can effectively lead the conversation.

I once received feedback from one of our data leaders that profoundly shaped my approach to these discussions. They pointed out that I needed to improve my overall knowledge of the data ecosystem and its components to effectively guide the technical team. Without this understanding, I risked letting the tech team lead the direction of the product because I couldn’t fully engage with the technical aspects of the conversation. This feedback underscored a crucial point: as data product managers, we must have a solid foundation in the data ecosystem. Without this background knowledge, our role can become confusing and frustrating, and our ability to influence the team and drive the product forward is severely compromised. It’s not just about understanding the technology; it’s about being able to facilitate and direct conversations in a way that aligns with both the technical realities and the product vision.

However, it's important to remember that it’s perfectly okay if we don’t understand something technical right away. In these moments, I make it a point to ask my engineering team to clearly explain the concepts to me. This not only helps me be more effective in the conversation, but it also strengthens our partnership. The tech team is our partner, and trust is the foundation of any strong partnership. By asking for their help in improving our technical knowledge, we show that we value their expertise and are committed to being better partners for them. This collaborative approach not only builds trust but also enhances our ability to contribute meaningfully to the product’s success.

Trust: The Foundation for Each Collaboration

Trust is the cornerstone of any successful collaboration, especially in the intricate world of data product management. A close partnership between the product manager, the engineering lead, and the data lead is essential. In an ideal scenario, these three roles work hand-in-hand, making decisions together, aligning on priorities, and driving the product forward as a cohesive unit. When this level of trust and collaboration is achieved, the experience can be incredibly rewarding—there’s a sense of shared purpose, mutual respect, and a smooth flow of ideas and execution. However, I also recognize that not all product managers have had the fortune of experiencing such a seamless partnership. Many have faced challenging collaborations where misalignment, lack of trust, or poor communication led to frustration. While those experiences can be disheartening, this section will focus on how to foster trust within your team, transforming challenging collaborations into enjoyable and productive ones.

Improving Trust by Splitting Ownership

One highly effective way to build trust within the team, as suggested by "PM & EM: Rules of Engagement," is through the clear division of ownership. When everyone is responsible for a decision, the reality is that no one truly owns it. To foster trust and efficiency, it's crucial to assign decision-making responsibilities to those with the most relevant expertise and context.

The PM is best positioned to own the "Why?" of a project, focusing on the problem, product vision, and strategic alignment with business goals. Their deep understanding of customer needs and market direction enables them to articulate why a particular area is worth focusing on.

The PM also owns the "What?"—deciding on the specific features and products needed to address the identified problem. This involves translating the problem into clear requirements and priorities that guide the team's efforts.

The EM owns the "How?"—determining the technical solution that will deliver the product's requirements. They make decisions about the technical architecture, ensuring that the solution is feasible, scalable, and aligns with engineering best practices.

The EM also decides "Who?" will work on the project, selecting the right team members based on their skills and the project’s needs. This ensures that the right people are in place to execute the technical plan effectively.

Finally, the EM is responsible for the "When?"—establishing timelines and deadlines for the project's completion. This involves balancing speed with quality, ensuring that the project is delivered on time while meeting the necessary standards.

By clearly delineating these roles, PMs and EMs foster a strong foundation of trust, where each respects the other's expertise and takes ownership of their part of the project. This division not only improves decision-making but also strengthens the collaborative relationship, leading to more successful outcomes.

Software Development vs. Data Product Development

I recently came across an interview with Eugene Mandel on Domino, where he discussed the differences between traditional software development and data product development. His insights resonated with me, particularly in how these differences impact the way product managers collaborate with their partners. Eugene pointed out that in traditional software development, organizations often practice Scrum or a variation of it, leading to a lot of structured conversations around deployment, story points, and user stories. However, when it comes to developing data products, you can't simply apply the same processes because the level of uncertainty is much higher.

Eugene emphasized that in data product development, trust between team members becomes even more crucial due to these uncertainties. I completely agree with this perspective. Developing data products often involves significant data discovery, and the results of this discovery can lead to unexpected changes and uncertainties. This environment can be challenging for engineering teams, who typically prefer well-defined and structured requirements before starting development. However, in data product development, it’s not always possible to have solid requirements upfront, and flexibility becomes essential.

For data product managers, this means building closer relationships with their partners is vital. The nature of data products requires a more adaptable approach, and that adaptability hinges on trust within the team. By fostering strong, trust-based relationships, data product managers can navigate the uncertainties inherent in data products more effectively and ensure smoother collaboration with their engineering and data teams.

Tips for Effective Collaboration Between Product Managers and the Tech & Data Teams

Product managers lead the product, and engineers build it. At first glance, the division of responsibilities seems straightforward, but in practice, it's far more nuanced. Both roles bring unique expertise and often have strong opinions on how to improve the product. This can lead to blurred lines, especially if a product manager tries to dictate the technical direction or if engineers begin to take over decisions about which features should be built next. Effective collaboration requires a clear understanding of these boundaries, along with mutual respect and open communication (Aha). In this section, we’ll explore key strategies that can help product managers and their tech and data teams work together more effectively, ensuring that both roles contribute their best to the product's success.

1. Align on the Data Strategy and Roadmap: A key step to successful collaboration is aligning on the data strategy and roadmap that support the overall product vision and goals. Product managers and data engineers must work together to identify data needs and priorities, sources, dependencies, models, and governance and security policies. This collaboration ensures that everyone is on the same page, working towards shared goals. As the product manager, it’s essential to set a clear product strategy with a well-defined vision, measurable goals, and prioritized initiatives. By communicating this strategic direction and visualizing it on the product roadmap, you help the engineering team understand the "why" behind their work, enabling them to implement solutions that effectively serve both business and customer objectives. A shared data strategy and roadmap help prevent duplication, inconsistencies, and misalignment, ensuring that the most valuable and feasible data initiatives are prioritized and successfully executed.
2. Understand Each Other's Roles and Skills: Effective collaboration starts with a deep understanding of each other's roles and expertise. Product managers should learn about the data engineering process, including the challenges of data sources, quality, and the technologies used. At the same time, engineers should familiarize themselves with the product development process, user needs, and the strategic decisions that guide the product. While you don’t need to know how to code, it’s crucial to empathize with the engineering team and appreciate the context in which they operate, such as infrastructure and performance requirements. By cultivating this mutual understanding, both product managers and engineers can communicate more effectively, set realistic expectations, and leverage each other’s strengths to create better products.
3. Plan with Conviction: Effective collaboration hinges on clear and decisive planning. As a product manager, it's essential to be confident in what needs to be built and fully understand the resources required. Features should only make it onto the roadmap if they are well-defined and aligned with customer needs(Aha). Involve engineers early in the process to gain their insights on potential challenges, but avoid prioritizing features based on difficulty alone. Instead, ensure that every item discussed with engineering is essential and backed by a strong rationale. This approach minimizes frustration, avoids rework, and keeps the team focused on delivering value.
4. Communicate Frequently and Transparently: Frequent and transparent communication is vital for effective collaboration between product managers and data engineers. Establishing regular and structured channels, such as meetings, emails, or chats, ensures a continuous exchange of information, feedback, and updates. It's important to use clear language, avoiding jargon or acronyms that could lead to misunderstandings. Being open about challenges, risks, and trade-offs, and seeking input when needed, fosters a collaborative environment. This level of communication builds trust, resolves issues promptly, and keeps everyone aligned on project objectives and expectations.
5. Build Relationships: Product development can be both challenging and exhilarating, with inevitable changes and unexpected obstacles, especially when working toward ambitious goals. Investing in personal relationships with your engineering team is crucial. By understanding their passions, stressors, and perspectives, you can engage with them more effectively and navigate tricky issues with respect and empathy. The best product managers advocate for their engineers by providing the necessary information, protecting their time, and admitting mistakes when they occur. Building strong relationships fosters a collaborative environment where both product managers and engineers continuously learn and improve together.
6. Collaborate on Data Discovery: Collaborating on data discovery and experimentation is essential for informed product decisions. Product managers and data engineers should work closely to define data questions and hypotheses, design experiments, collect and process data, and analyze the results. Utilizing tools like dashboards, visualizations, and reports, they can effectively present and communicate their findings. This collaborative approach ensures that data-driven insights are generated and leveraged, enhancing both the product’s value and the overall user experience.
7. Celebrate and Learn from the Outcomes: Whether a project is a resounding success or faces challenges, it's important to take the time to celebrate the team's efforts and learn from the outcomes. Product managers should recognize the contributions of their tech and data teams, celebrate milestones, and facilitate retrospectives to identify lessons learned. By doing so, you not only boost team morale but also create a culture of continuous improvement that benefits future projects.

Effective collaboration between product managers, engineers, and data teams is essential for successful product development. While product managers lead the product's vision and strategy, and engineers bring it to life, this division of responsibilities requires clear boundaries, mutual respect, and continuous communication. Aligning on a shared data strategy and roadmap, understanding each other's roles, and planning with conviction are crucial steps. Additionally, regular communication, strong personal relationships, and joint efforts in data discovery enhance the collaborative process. By celebrating successes and learning from challenges, teams can foster a culture of continuous improvement, driving innovation and delivering products that truly meet user needs.

A Bad Relationship Turned into My Best Experience

When I began one of my roles as a data product manager, I encountered a challenging relationship with my tech lead. Our collaboration started on the wrong foot, marked by frequent conflicts and disagreements on various aspects of our work. The primary source of our conflict was the operational model that the organization had implemented. While it worked well for software development, it was ill-suited for data product development. This mismatch led to a clash of priorities and goals for our team, making it difficult to align on what truly mattered.

However, much like the Japanese art of Kintsugi—where broken pottery is repaired with gold, turning the cracks into a beautiful and integral part of the object—our broken relationship became stronger and more valuable through patience, optimism, courage, and persistence. After several unproductive conflicts, we realized that in order to improve our team’s performance and reach our shared objectives, we needed to find common ground. That common ground turned out to be our shared strategy: transforming the data ecosystem. To achieve this transformation, we had to work closely together, leverage each other's strengths, and collaborate effectively on prioritization and planning. I made a conscious effort to better understand the roles of the data engineers and the complexities of the data ecosystem, which I had previously lacked knowledge about. Whenever I had questions about technical aspects, I asked my tech lead to explain, and he was patient and thorough in his responses, taking the time to ensure I fully understood the details. This mutual understanding and willingness to learn from each other allowed us to align our efforts more effectively.

We also implemented regular communication as a key part of our strategy. We committed to meeting at least twice a week for an hour each time to discuss ongoing projects, impediments, and expectations. These meetings proved invaluable in maintaining alignment and addressing issues promptly. Additionally, we began collaborating with a data partner on data discovery. This partnership was a transformative experience, as neither of us initially had a deep understanding of the data we were working with. However, through our collaboration with the data leader, we gained a comprehensive understanding of the data elements, relationships, and lineage within our ecosystem. This newfound knowledge allowed us to make more informed decisions and to plan effectively for future projects.

In retrospect, implementing these strategies—which I now realize are fundamental to successful collaboration—was crucial to turning what began as a difficult relationship into one of the most rewarding experiences of my career. Understanding the strategies outlined in the previous section, and applying them proactively, can be incredibly beneficial for any data product manager looking to build strong, effective partnerships with their tech and data teams.

Conclusion

In the realm of data product management, success is built on a foundation of collaboration, trust, and mutual understanding. The role of a data product manager is inherently complex, requiring not just strategic vision and technical know-how, but also the ability to bring together diverse teams to work towards common goals. This article has explored the critical aspects of collaboration between data product managers, engineers, and data teams, highlighting the importance of aligning on strategy, understanding each other's roles, planning with conviction, and maintaining open lines of communication. By fostering strong relationships and engaging in meaningful collaboration, product managers can ensure that their teams are not only productive but also motivated and unified in their efforts to deliver high-quality products.

To further enhance collaboration, product managers should focus on a few key strategies: aligning on the data strategy and roadmap, understanding the distinct roles and skills within the team, planning with confidence, and maintaining frequent and transparent communication. Building strong relationships is essential, as it fosters trust and mutual respect, making it easier to navigate challenges and celebrate successes together. Collaborative efforts in data discovery can lead to richer insights and better product outcomes, while regular reflection and learning from outcomes ensure continuous improvement. By implementing these strategies, product managers can create an environment where collaboration thrives, ultimately leading to the successful development and delivery of impactful data products.

As we continue this series, each article will provide practical advice and examples to help you navigate Data Product Management complexities. Stay tuned for the next article: "Career Development and Skills."