In the digital age, data became an invaluable resource for companies to make informed decisions and set their strategies. Simultaneously, while product management was once seen as a nice-to-have, it is now essential in managing the production, delivery, and betterment of products. However, product management is set to be replaced by "data product leadership," which will be the principal influencer in modern data organizations. The Rise of Product Management In digital businesses, product managers hold significant influence, actively driving strategy and curating products to address customer needs. As a result, it's no surprise that product management is beginning to shift away from the CIO/CTO's purview and secure a place within the C-suite as a full-fledged member of the executive team. However, even as this shift is occurring, the core skills that defined the product manager's role over the past decade are quickly becoming outdated. Product management has gained its current notoriety by focusing obsessively on UX design. Product managers have curated these skills over decades of designing online tools, social media platforms, mobile apps, and internal business systems, all meticulously engineered for efficiency. This focus has produced legions of product managers obsessed with user experience, fostering a tendency to view a product solely in terms of what appears on a screen and what it takes to get there. Locked-up Data Unfortunately, deadlines and pressure often force product managers focused on UX to take shortcuts that result in use-case-locked data, giving them significant challenges in retooling for alternative applications. They struggle not only with the effective utilization of their existing data but also with their ability to respond quickly and effectively to new opportunities, particularly in the realm of AI innovation. Use case-locked data can severely limit a company's ability to harness the full potential of that data. This constraint not only impedes swift decision-making, crucial for maintaining a competitive edge in today's rapid business environment but also confines data to a single use case. This is limiting the data's potential for broader application. A dashboard designed for marketing insights, for instance, might not be suitable for financial analysis or product development and especially not suitable for AI. AI algorithms require clean, structured data to function effectively and produce accurate insights. Without this, the implementation of AI can become a challenging, time-consuming, and costly process. Moreover, as AI continues to evolve and its potential applications broaden, companies with unstructured or chaotic data will find themselves ill-prepared to seize new AI-driven opportunities that emerge. These opportunities could range from improving operational efficiency and enhancing customer experience to creating innovative new products and services. Data Products & Data Product Leadership Enter data products. A data product transforms raw data into a format that enables decision-making or action. Unlike interfaces that merely present data curated to a specific use case, data products are designed from scratch to meet multiple use cases. In contrast to an interface-driven approach, a data product-driven approach liberates data from these restrictions, making it available for multiple use cases. By focusing on structuring and organizing data effectively, businesses can ensure that the same dataset can be used for a variety of purposes - from driving AI products, powering interfaces, and informing strategic decisions to sharing with integration partners, thus maximizing the value derived from the data. The leadership of your Data Product should ideally be entrusted to your Data Product Leader (DPL). You might wonder, isn't the DPL simply the role of a data engineer or a data architect? The answer is more intricate than it first appears. While the skills of a data engineer or data architect are indeed crucial for creating data products, their role is not identical to that of a DPL, in fact, it's not dissimilar to how an engineer's role differs from a product manager. The DPL plays a unique role, acting as a bridge between data engineering and customer use cases. They are tasked with anticipating the needs of internal and external customers, even before these needs arise, and considering the data requirements and architecture necessary to support those needs. Just as a Product Manager oversees the delivery of the product to the end client, the DPL manages the data assets that the Product Manager uses to create their interfaces. In essence, the DPL's role extends beyond mere data structuring; they ensure that the data assets are effectively leveraged to meet customer needs and drive product success. DPL vs Product Manager A common question that arises in this context is: Who holds the responsibility for setting the product strategy - the Data Product Lead (DPL) or the Product Manager? The answer is nuanced. Both the DPL and the Product Manager share the responsibility of understanding customer needs. However, the DPL has an additional layer of responsibility. They must comprehend not only the needs of the customers but also the requirements of other facets of the business. This includes understanding the needs of business partners and internal stakeholders. Moreover, the DPL is also responsible for determining how the data product aligns with and meets the needs of all product managers within the organization. In essence, while both roles play a crucial part in shaping the product strategy, the DPL has a broader scope of responsibilities, encompassing various internal and external needs related to the data product. The good news is that two decades of intense focus on User Experience (UX) and back-end systems by product managers have made the construction of APIs and interfaces more straightforward than ever. A well-managed data product can swiftly be transformed into an interface and made readily available to customers. This contributes to enhanced business flexibility and promotes more agile practices. The DPL oversees the creation, management, and strategic direction of data products. They play a vital role in shaping the data strategy, which has far-reaching implications for the entire organization. As such, it becomes important to empower them in the organization by placing them in a senior role. Depending on the organization, some may have a DPL leading product teams, and others may place a preference on putting the DPL in the Chief Data Office. Regardless, the DPL should be empowered to shape data product strategy and held accountable to the executive team. Finding Your Data Product Leader Data Product Leadership is a relatively new field; as such, finding a qualified and experienced Data Product Leaders can be a challenging task. However, there are viable alternatives for sourcing talent for this pivotal role. Product Managers often possess a wealth of relevant skills and experiences that can be valuable in the role of a Data Product Leader. They typically understand the product lifecycle, have experience working with cross-functional teams, and are adept at aligning product strategies with business goals. If they also have a strong affinity for data and are willing to deepen their knowledge in this area, Product Managers can make a successful transition to becoming Data Product Leaders. Data Architects have a deep understanding of how to structure, integrate, and maintain data. They are skilled in designing data systems that are scalable, reliable, and secure. These technical skills, combined with a broader understanding of business strategy and user needs, can make them effective Data Product Managers. Data Engineers have hands-on experience in managing and manipulating large datasets. They are familiar with various data technologies and understand the technical challenges involved in building data products. If they can complement these technical skills with strategic thinking and user-centric design, Data Engineers can also transition successfully to the role of Data Product Manager. With the right training and support, these professionals can evolve into effective Data Product Leaders, driving the organization's data strategy forward. Conclusion As we navigate the data-driven landscape of the 21st century, it's clear that traditional product management roles, while still valuable, are becoming less equipped to meet the evolving needs of modern businesses. The focus on UX design and functionality, while important, often results in data being locked into specific use-cases, limiting its potential for broader applications. As businesses continue to generate and rely on increasingly complex and voluminous data, this approach is proving insufficient. Enter the era of Data Product Leadership. As the custodians of data assets, Data Product Leaders (DPL) are uniquely positioned to bridge the gap between data engineering and customer use cases. They not only anticipate and meet the needs of customers but also consider the data requirements and architecture necessary to support these needs. Their role extends beyond data structuring to ensure that data assets are effectively leveraged to meet customer needs and drive product success. As the influence of traditional product management wanes, the rise of Data Product Leadership signals a pivotal shift in the business landscape. This transition underscores the growing recognition of data as a crucial business asset and the need for specialized roles to manage and leverage this asset effectively. This shift isn't just about staying current; it's about paving the way for the future of modern data organizations – a future where actionable insights are at everyone's fingertips, driving faster, smarter decisions that propel businesses forward. For any product individual eager to build efficient modern data organizations, embracing this shift is no longer an option but a necessity.
