The Power of Data Products

Discover how a data product mindset can help reduce complexity and harness the power of data for better business outcomes all around.

In today’s data-driven world, enterprises strive to realize the true potential of their data. While a few succeed at great cost, most struggle to effectively leverage their data. This struggle manifests as a lack of trust in generated insights, dependency on IT, and lengthy development cycles for data use cases.
The root cause of these inefficiencies in data management lies in the project-oriented mindset. This approach views data use cases as isolated tasks, treating each one as a unique project. Consequently, data solutions become temporary fixes that lack scalability, repeatability, and consistency. 

In contrast, adopting a product thinking approach transforms data management into a continuous, evolving process. This mindset ensures repeatability, maintains high data quality, and allows for constant refinement. By productizing data operations, organizations achieve long-term value, better meet user needs, and support ongoing improvements, ultimately driving more effective and reliable data use for various business cases. 

The purpose of this document is to define the emerging term “data products” and explain its role in accelerating the data journey and the benefits it offers to enterprises. 

See also: The Long Due Renewal of Data Storage for Accelerated Data Applications

Project Vs Product Mindset for Data Projects 

Let’s look at the difference between project mindsets and product mindsets when it comes to managing data projects.

Project Mindset 

Traditionally, enterprises approach data applications with a project-driven mindset. For instance, when the sales team needs a specific dataset, they request it from the central data engineering team. This initiates a project to identify, collect, prepare, and deliver the dataset to the sales team. This cycle is very time consuming and repeats every time a new use case arises from any department. The cumulative effect of these requests is the creation of numerous unmanageable and non-reusable data pipelines, resulting in excessive delays, cost overruns, inconsistent/unreliable data, and an inability to meet the evolving needs of the business in a timely manner.  

Drawbacks of the Project-Driven Approach 

The “data as a project” approach has several major drawbacks: 

  • Slow Time-to-Delivery: Each new request starts from scratch, causing delays. 
  • Lack of Reuse: Data solutions are often one-time-use, leading to inefficiencies. 
  • Rigidity: The approach is not flexible, making it difficult to adapt to changing needs. 
  • Risk of Incomplete Data: There is a higher risk of delivering incorrect or incomplete data due to isolated project scopes. 

Enter The Data Product Mindset 

The limitations of project-driven data management are becoming increasingly evident. Companies are struggling with fragmented data landscapes, lengthy development cycles, and a lack of reusability. This is where the concept of Data as a Product or Data Products emerges as a strategic approach to unlock the true potential of data. 

In a product-driven approach, data products are developed with the entire enterprise’s needs in mind. Instead of creating one-off solutions for each departmental request, data engineering teams build robust, reusable data products that can support various scenarios and requirements. These products are continuously refined and improved, much like software products, ensuring they remain relevant and valuable over time. 

The product driven approach reduces complexity and enables reusability.  

From Raw Fields to Fashionable Insights: Why Data Needs a Product Mindset 

Imagine your organization is a leading fashion house, constantly striving to create innovative designs. Data is the raw material – the cotton fields and sheep pastures – that fuel your creativity. But what if working with that data felt like picking individual fibers by hand? That’s the limitation of a project-based data approach. Here’s the analogy: 

Project-Based Data (The Field-to-Fashion Fumble): 

Project-based data ignores new tools and instead relies on laborious manual processes to turn raw materials into something usable:

  • Every new clothing line (data report) requires a team to head out to the fields (data sources) and painstakingly pick raw cotton (data points) or shear individual sheep (data extraction). 
  • Then, they need to spin the fibers into yarn (data transformations) and weave it into fabric (data cleansing) specific to each design. This process gets repeated for every single collection, even if some styles share similar materials. 
  • It’s incredibly laborious, inefficient, and leaves you with a collection of disconnected, one-off garments (data silos) that are difficult to adapt or reuse

Data Products (The Efficient Textile Mill): 

Now envision a state-of-the-art textile mill– this is your centralized data platform. Pre-built, reusable Data Products act as your pre-processed materials – spun yarn, woven fabrics, and standardized patterns – all clearly labeled (metadata) for their intended uses (sales analysis, customer segmentation). 

  • Designers (business users) can easily browse and select the Data Products they need (materials) to create their clothing lines (reports, dashboards). 
  • These pre-built components are readily available, eliminating the need for endless trips to the fields. Teams can focus on their design expertise (data analysis) and quickly craft new collections (insights) for different customer segments (business needs). 

The Product Mindset Advantage: 

Data Products offer a significant leap forward compared to the project-based approach: 

  • Reusability: The groundwork (data pipelines), basic materials (processed data points), and quality control (data governance) are already established, saving time and resources. 
  • Scalability: As your fashion line expands (data needs grow), you can easily build upon existing Data Products or create new ones with different textures and patterns (data formats), ensuring your data infrastructure can adapt to evolving trends. 
  • Efficiency: Designers (business users) can focus on their core competencies (analysis) instead of getting bogged down in raw material collection (data wrangling). 
  • Consistency: Standardized Data Products ensure everyone has access to the same high-quality materials, leading to consistent and reliable results (insights). 

Skip the tangled yarn and grab the ready-made fabric! Embrace a data product approach and transform your data into a well-organized textile mill. This empowers your designers (business users) to unleash their creativity (data analysis) and stitch together stunning collections of insights that keep your organization a trendsetter in the world of data-driven decision-making. 

Now that we’ve considered the methodology, let’s see how this approach transforms your data from a complexity to a true asset.

What is a Data Product 

A data product is a curated and reusable dataset or data solution designed to meet specific business needs and support multiple use cases across an organization. Unlike one-off data projects, data products are built with long-term usability in mind, incorporating robust data governance, consistent quality, and clear documentation. They are developed to be easily accessible, scalable, and continuously improved, ensuring they provide ongoing value and can adapt to evolving requirements. Data products are treated as assets that can be accessed, used, and refined by various departments, promoting efficiency, collaboration, and better decision-making across the enterprise. 

A data product can be likened to a Docker container in the sense that it bundles together all the necessary components for data processing and usage. Just as a Docker container encapsulates an application along with its dependencies, configurations, and runtime environment, a data product encapsulates: 

  • Data: The core content that is being utilized 
  • Metadata: Descriptive information about the data 
  • Transformation Code: Scripts and logic for data processing and transformation 
  • Infrastructure: Necessary storage and computing resources required from the cloud service 
  • Output ports: These may include APIs, ODBC/JDBC interface or streaming data 

Types of Data Products 

At a broad level, there are two types of data products.

Source-Aligned Data Products: 

These data products prioritize providing data with minimal manipulation from its original source format. They act as the building blocks for other data products and ensure consistent data representation across the organization. These products focus on fidelity, standardization, documentation and lineage. For example, a product that exposes raw customer data from a CRM system in a standardized format (e.g., JSON) or a product that provides access to sensor data from IoT devices with minimal processing is source-aligned data product. 

Value Data Products: 

These data products move beyond raw data by applying transformations, analysis, or integrations to deliver specific value to users. They are designed to address business needs and provide actionable insights. The Value Data Products focus on business impact, transformation and user design. Some of the examples of value data products include a customer segmentation model that helps identify different customer groups for marketing campaigns or a product that aggregates sales data from different regions with visualizations to track performance across markets. 

Source-aligned data products act as the foundation for creating valuable data products. They provide the raw materials and ensure consistency across the data landscape. Value data products are built upon source-aligned data products to deliver actionable insights and address business needs. 

See also: Developing Secure, Compliant Data Products with Databricks Lakehouse Apps

Key characteristics of Data Products 

We are all familiar with the various physical products we buy and consume every day. To better understand data products, let’s compare them to physical products, as they exhibit many of the same characteristics. 

Characteristics Physical Product Data Product 
Discoverable Displayed prominently in physical stores or online marketplaces Registered in a centralized data catalog 
Understandable Include user manuals, packaging, and/or clear labeling Documented with clear purpose, schema, metadata, relevance, business context, data quality, and usage guidelines 
Addressable Can obtain by going to a physical store or purchasing online Addressable through secure and scalable access mechanisms, such as APIs or web interfaces, 
Secure Can be physically secured through locks, packaging, or anti-theft devices Enforce security and privacy protocols through fine-grained access controls 
 Trustworthy Reputable brands build trust through consistent quality and positive reviews Establish trust through SLO (service level objectives) matrices 
Natively Accessible Physically accessible upon purchase, requiring no additional steps Directly accessible through a self-serve platform, allowing consumers to easily explore, analyze, and visualize data products 
Valuable on its own   Fulfill a need or desire without requiring additional components Autonomous entity, encapsulating all necessary components including code, infrastructure, policies, and documentation 

Every department within an enterprise may have their own set of data products. The sales department may have products such as lead scoring, route planning and ideal sales visits while the operation department may have financial and product inventory as data products.

Benefits of Data Products 

Leveraging data products has numerous benefits for the organization.

Faster Time-to-Insights 

Data products are pre-built, well-defined packages of data, models, and functionalities designed to address specific business needs. This significantly reduces the time it takes to access and analyze relevant information for generating insights. According to research conducted by McKinsey “Companies that treat data like a product can reduce the time it takes to implement it in new use cases by as much as 90%.” 

User-centric approach 

Unlike traditional data management, which often focuses on technical aspects, data product thinking emphasizes user needs and pain points. Data becomes a “product” designed to deliver specific value to its users, whether analysts, business leaders, or other stakeholders. 

Collaboration and communication 

Product thinking fosters collaboration between data teams, developers, and users. This cross-functional approach ensures data products are aligned with business needs and effectively utilized. Clear and transparent communication about data products, their purpose, and value is crucial to help users understand how to leverage them effectively and promote wider adoption. 

Focus on clear value proposition 

Product thinking requires defining the specific value proposition of each data product. By clearly stating what problems it solves and how it benefits users, resources are allocated effectively. Data products are designed with measurable outcomes in mind. Tracking key metrics helps ensure the product is achieving its intended goals and delivering value. 


Unlike traditional data projects, data products are designed to be reused across various scenarios. By investing in the upfront development of a well-defined data product, you eliminate the need for repetitive work and save significant time and resources in the long run. 

Reusing data products eliminates the need for multiple data pipelines and redundant data infrastructure. This translates to increased efficiency within your data team, allowing them to focus on more strategic initiatives. Additionally, by avoiding duplicated work, data products help organizations save on costs associated with data collection, preparation, and analysis. This allows for better resource allocation and improved return on investment for your data initiatives. 

See also: Tapping into the Potential of Data Products

Embracing Data Products: The Future of Data Management 

Data products represent a fundamental shift in how organizations approach data management and utilization. They serve as the foundation for handling future data and AI workloads. As data volumes continue to grow and AI becomes increasingly prevalent, the adoption of data products will be crucial for enterprises to effectively harness the power of their data assets, make informed decisions, and remain competitive in this current landscape. 

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