In the world of enterprise, vast resources are funneled into creating intricate data architectures, with an expectation that such investments will translate into accessible, usable, and versatile data for business users. Yet, there remains a disconnect. The meticulous efforts in organizing and securing data often overshadow the practical needs of those who rely on this data daily.
This misalignment has led to the underutilization of extensive data repositories, with valuable insights lying dormant in the depths of data lakes and warehouses. This is also known as the 'data value gap.'
Addressing this gap requires a paradigm shift: envisioning and handling data not as a mere resource but as a product. Think about the products that stand out in the marketplace—they’re the ones that are straightforward to locate, comprehend, and employ, consistently fulfilling and often exceeding our expectations. They achieve this because behind every successful product is a dedicated person or team ensuring it remains user-centric, maintains high quality, and is simple to manage and evaluate. This fosters a harmonious relationship between the product and its user.
Now, imagine if we channeled this product-centric mindset into data management. It's about cultivating a data environment that emphasizes usability, quality, and alignment with user needs, ultimately transforming data into a strategic asset that propels business objectives forward.
This article dives into how managing data as a product can bridge the data value gap, ensuring that data is not just collected, but capitalized upon, thereby unlocking its full potential to drive informed decision-making and business growth.
What Is Data As A Product?
"Data as a Product" (DaaP) is a forward-thinking concept that treats data not just as a resource but as an independent entity that delivers value, much like any other product. This approach posits that data should be crafted, refined, and packaged to serve specific needs, creating its unique value proposition and potential revenue stream. It entails a shift in perspective from viewing data as a mere byproduct of business operations to recognizing it as a core asset that requires dedicated management throughout its lifecycle.
In the realm of Data Mesh, a modern data management strategy, DaaP is instrumental. It insists on applying product management principles to data, ensuring that it is not only well-prepared but also user-centric. Here, the users or consumers of data are considered customers, which necessitates a focus on the accessibility, quality, and usability of data.
Adopting DaaP within an organization addresses common pitfalls in data projects, such as time sinks due to data preparation tasks or project derailment from lack of quality data. It ensures that data is treated with the same rigor and strategic intent as any other product offering.
The usability of a data product can be encapsulated in eight attributes, as outlined by Zhamak Dehghani, the founder of the Data Mesh concept. These are: being discoverable, addressable, understandable, trustworthy, accessible, interoperable, valuable, and secure. For instance, a central data catalog enhances discoverability, while a consistent organizational scheme for data ensures it is addressable. Security, an equally critical attribute, encompasses access controls and adherence to regulations like GDPR, safeguarding sensitive information, and maintaining compliance.
Treating data as a product means recognizing these attributes and embedding them into the lifecycle of data, thereby elevating the role of data to be as significant as that of any product that a company might offer.
Strategic Value of Data Products
The strategic value of data products is immense and multifaceted, increasingly recognized as a critical driver in the modern business landscape.
Data as a Strategic Business Asset
When managed properly, data transcends its role as a passive resource to become a strategic business asset. This transformation occurs when data is not merely collected, but is curated, analyzed, and made accessible in ways that can inform key business decisions and strategies. High-quality data products can reveal patterns, predict trends, and provide a competitive edge. It is a foundational element for innovation and efficiency, fostering data-driven cultures that respond rapidly to market demands.
Aligning Data Management with Business Objectives
Harmonizing data management with a company's strategic aims is crucial for driving business success. It necessitates a thorough comprehension of the business's strategic plan and the identification of ways in which data assets can bolster and propel these aims.
For example, in a scenario where the objective is to elevate customer satisfaction, the design of data products should be centered around gathering and interpreting data on consumer trends and choices. Conversely, if the objective is improving operational efficacy, then data products must be capable of facilitating process enhancements and smarter resource distribution. This synergy between data initiatives and business goals is essential, guaranteeing that each step taken in data management is intentional and makes a meaningful contribution to the broader goals of the organization.
Benefits Of Managing Data As A Product
- Facilitates Distributed Ownership: This approach breaks down silos by distributing ownership among those who intimately understand the data and the business stakeholders who use it. Domain experts become data product managers, taking charge of ensuring data quality, overseeing performance, and managing changes. Despite the distribution of ownership, a centralized governance model can still be maintained through an enterprise data exchange, balancing autonomy with oversight.
- Enhances Trust in Data: Trust is a critical currency in the realm of enterprise data. Data products elevate the trust quotient by providing curated, high-quality data that is easily discoverable, understandable, and consumable. When data products are transparent and meet quality standards, business users' confidence in the data's accuracy and utility rises.
- Enables Self-Service and Accessibility: Data products democratize data access by allowing users with varied technical expertise to engage with data. Through self-service portals, users can locate, preview, filter, and analyze data to quickly ascertain its relevance to their needs, accelerating the data acquisition process.
- Promotes Value Creation Cycles: Rather than one-off data processes, data products encourage ongoing cycles of innovation and value creation. These products can serve as foundational elements that are further refined and combined to create custom data products, often within an enterprise data exchange. Such collaborative and iterative development can yield benefits across the business.
- Creates an Expansive Ecosystem: When data is managed as a product, it opens the door to wider data access across the extended enterprise, including suppliers, partners, and customers. Policy-driven management allows for controlled visibility and usage, creating a secure environment for data sharing and collaboration. The integration of third-party data into this ecosystem can further optimize investments and expand capabilities.
- Streamlines Data Operations: The lifecycle management of data products becomes more streamlined, characterized by consistent processes for creation, publication, customization, and delivery. Automated data pipelines facilitated by enterprise data exchanges ensure that subscribed users receive data products seamlessly, with regular updates and comprehensive usage reporting for continuous optimization.
Common Pitfalls With Managing Data As A Product and How to Avoid Them
- Underestimating the Need for Cultural Change: Implementing DPM is not just a technical challenge; it's a cultural one. Avoid this pitfall by actively promoting a data-centric culture and providing ample training and resources.
- Neglecting Data Governance: Without strong governance, data quality and compliance may suffer. Avoid this by implementing robust data governance from the outset.
- Overlooking User Feedback: Data products should evolve based on user needs. Avoid creating static products by establishing regular feedback loops with users.
- Insufficient Cross-Functional Collaboration: Siloed teams can lead to disjointed data products. Avoid this by fostering collaboration and communication across different departments.
- Not Setting Clear Objectives: Without clear objectives, it's difficult to measure the success of a data product. Avoid this by setting specific, measurable goals early in the process.
- Lack of Ownership: Data products require clear ownership to ensure accountability. Avoid ambiguities by assigning data product managers who take full responsibility for their products.
In conclusion, embracing data as a product is not merely a shift in operational tactics; it represents a fundamental change in the organizational mindset. This approach elevates data from a subsidiary function to a core business asset, with its potential fully realized when treated with the same strategic importance and customer focus as any product or service offered by a company.
The transformative power of managing data as a product lies in its ability to make data accessible, understandable, and actionable, thus bridging the 'data value gap' that many enterprises face today.
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