While related, data products and DaaP serve distinct purposes within data management. Data products are focused on leveraging data to deliver actionable insights and solutions, such as analytics dashboards and predictive models. They address specific problems, are supported by sophisticated data processing techniques and cater to a broad audience, including product managers, data scientists and end-users. Examples of data products may include something like a business analytics dashboard, a chatbot or even a recommendation system, like what you see when shopping on Amazon.

In contrast, DaaP is a holistic methodology for data management, particularly in the context of data mesh principles, designed to treat data as a marketable product that can be served to various users within and outside of the organization. A DaaP contains the code, its data and metadata and any necessary infrastructure needed to run it.

A customer insights platform designed for a retail company is a good example of DaaP. The platform aggregates customer data across multiple touchpoints—such as in-store purchases, online shopping behavior, customer service interactions and social media engagement—to create a comprehensive view of each customer’s preferences, behaviors and purchasing patterns. Both concepts, however, rest on a shared foundation of data management and governance, with the ultimate goal of maximizing the intrinsic value of data.

As enterprises began to invest in advanced data storage technologies to make data widely accessible and usable for generating business insights and automating decisions, they faced various challenges as the solutions did not scale as intended. The data that engineers were receiving was not wholly meaningful, truthful or correct and with scant understanding of the source domains that generated the data, engineers could not correct for what they did not know.

Data engineers recognized the necessity of changing their approach to designing modern distributed architectures. They saw the importance of adopting a new methodology that organizes the architecture around the specific business domains it aims to support. This approach incorporates product thinking to develop a functional and user-friendly self-service data infrastructure.¹

Product thinking is about more than the features of a product; it's about creating meaningful solutions that resonate with users and stand out in the market. It's a philosophy that influences every stage of the product development process, from ideation to launch and iteration. Engineers realized that by treating data as a product, they could significantly enhance its use and value within the organization.

In adopting an approach that treats datasets as products, domain teams within specific business areas are created to take charge of managing and disseminating their data across the organization, to better center the user experience for the primary consumers of this data—typically data scientists and engineers.

These domain teams share their data via APIs (Application Programming Interfaces), accompanied by comprehensive documentation, robust testing environments and clear performance indicators.

A successful DaaP must meet the following requirements:

1. Easily discoverable
2. Addressable
3. Trustworthy
4. Well documented
5. Able to work with other data products
6. Secure

This means that in a DaaP methodology, data must be easy to find, reliable, clear in what it represents, can be integrated with other data and is protected against unauthorized access.

Imagine DaaP is like air travel and every piece of data is an airline traveler: organizations and users need to know where every data point came from, what transformations it underwent and where it’s destined to end up. This is called data lineage and is a crucial element of effective DaaP adoption. By using tools like IBM InfoSphere, AWS Glue or Cloudera Data Hub, organizations can manage metadata and track data journeys to ensure transparency and avoid confusion.

Once each traveler has been properly vetted, they board the plane. Just as the airline needs to ensure the plane is large and sturdy enough to handle the passengers, organizations must use scalable infrastructure to accommodate growing data volumes and multiple access requests. Depending on an organization's specific business needs and market segments, there are a number of cloud-based platforms, open-source solutions and commercial platforms from which organizations can choose.

Now, imagine needing flight information, but the system is down. This breaks trust with travelers and paints an airline as unreliable and ineffective, which is exactly why DaaP tools need to consistently deliver. It’s also why organizations must provide clear plans and reports on data recovery and redundancy.

There is no air travel without security and the same goes for DaaP. Security features such as role-based access control, data encryption and intrusion detection systems protect sensitive data and ensure compliance with regulations like GDPR and HIPAA. Governance practices, including data quality monitoring, cataloging and change management, ensure the organization’s data is reliable and accessible.

At the core of DaaP lies the meticulous orchestration of datasets. These datasets are curated by data engineering practices, which involve the design, construction and management of large-scale data pipelines. These pipelines transport data from data sources through an end-to-end process, transforming raw data into structured, high-quality information stored in data warehouses or data lakes. Data platforms are the foundation for these operations, providing the infrastructure and tools necessary for data teams to perform data analytics and data science tasks efficiently.

Data models and schemas are crucial in this context, as they define how data is organized, stored and related within the data warehouse or data lake. They ensure that data is discoverable, accessible and usable for data consumers—the business analysts, data scientists and application developers who derive insights and build applications based on this data. SQL (Structured Query Language) remains a pivotal tool for interacting with data, enabling data users to query, manipulate and analyze datasets to meet their specific needs.

Data teams use metrics to assess the quality, performance and value of the data product. These metrics guide iteration and continuous improvement processes, ensuring that the data product evolves in response to feedback from data consumers and changes in business requirements.

APIs are the conduits through which data products are delivered to end-users and applications. They facilitate access, enabling data consumers to integrate and use data in various use cases—from operational reporting to advanced machine learning and artificial intelligence (AI)projects. This integration capability underscores the importance of a well-designed API strategy in the DaaP lifecycle, ensuring data is not only accessible but also actionable.

Applying machine learning and AI within DaaP enables enterprises to unlock predictive insights and automate decision-making processes. By leveraging machine learning models trained on historical data, businesses can anticipate future trends, optimize operations and create personalized customer experiences. This advanced use of data underscores the iterative nature of DaaP, where data products are continually refined and enhanced based on new data, emerging use cases and feedback from data consumers.

The lifecycle of a DaaP product encompasses its creation, maintenance and evolution over time. It involves a series of stages, including planning, development, deployment and iteration, each requiring close collaboration among data teams, business stakeholders and data consumers. This lifecycle approach ensures that data products remain relevant, valuable and aligned with business objectives.

To make data more useful within an organization, it's essential that data sets are easy to find, trustworthy and can work well with other data. The essence of making DaaP data easily discoverable and addressable within an organization hinges on implementing a centralized registry or catalogue. This registry should detail all available DaaP data, including metadata like ownership, source and lineage, enabling data consumers, engineers and scientists to efficiently locate relevant datasets.

Ensuring data integrity and trustworthiness is paramount, necessitating a departure from accepting error-ridden or unreliable data. By instituting service level objectives (SLOs) that guarantee data's truthfulness and applying rigorous data cleansing and integrity testing from the outset, organizations can bolster user confidence in the data. Furthermore, the data must be self-describing and adhere to global standards for interoperability, allowing data integration across various domains. The role of data product owners and engineers is critical in this ecosystem, defining and driving the lifecycle management of DaaP data to both delight users and meet quality standards. This approach not only requires a blend of data and software engineering skills but also fosters a culture of innovation, skill sharing and cross-functional collaboration within the tech landscape.

DaaP encourages enterprises to view all data as valuable products, reflecting consumer-based product principles in data management, selection, customization and delivery. This approach fosters a seamless flow of high-quality data from its creators to its consumers, supported by customer-centric tools and mindsets. Imagine data is like a product you’d see in the stores; under a DaaP methodology, an organization should treat its data with the same care and attention as physical products.

This means only collecting and storing data that’s truly useful, ensuring that data is presented clearly, organized and user-friendly and ensuring the data fits the industry or domain context. When these pieces are in place, DaaP enables the distribution of high-quality data within the organization. The oil has been processed and is helping to run the machine.

Applying a DaaP approach within an organization means getting stakeholders aligned and keeping them informed, developing a mindset where data is treated and managed as a high-quality product and it means building or investing in self-service tools, one of the main principles of the data mesh concept—a developing approach to decentralized data architecture.

Adopting DaaP presents challenges, including data privacy concerns, organizational resistance to change and a need for greater data literacy among employees. Overcoming these hurdles requires strategic planning, organizational buy-in and investments in technology and talent.

Navigating and complying with data privacy regulations across a global marketplace containing different regions and rules is a major hurdle to clear. Organizations need expertise and resources to ensure their DaaP products adhere to strict regulations in every location.

Data breaches can be headlining news and consumers are increasingly aware of how organizations use their data. Building trust through transparent data handling practices and clear documentation about data usage within DaaP is crucial to earning the trust of the user base. Any organization considering DaaP needs robust security measures to protect data from breaches and unauthorized access. This includes implementing encryption, access controls and data governance frameworks.

Successful DaaP isn’t just about having the right hardware and software; as always with new tools comes resistance to change. Established organizational cultures might resist changes in the data ownership, sharing and accessibility introduced by DaaP. Effective change management strategies and clear communication are essential to ensure that different departments are willing and able to share their data without fearing loss of control or competitive advantage. Fostering collaboration and demonstrating the benefits of DaaP for all stakeholders is vital and clear roles and responsibilities for data governance and product ownership need to be established to avoid confusion and inaction.

The human challenge of a successful DaaP initiative doesn’t end there. Because DaaP requires the entire organization to be mindful of data, organizations can run into gaps with employees who lack data literacy. Employees across various levels may not fully grasp the technicalities and business value of DaaP; training and education programs can help bridge this gap. Many employees might struggle to analyze and extract insights from DaaP products but providing user-friendly tools and training in data literacy can empower them. In addition, technical teams need to translate complex data insights into actionable information for non-technical stakeholders.