Data is the driving force behind every successful business decision. Among the different types of data, product data holds a unique and essential role.
According to a recent study, businesses that leverage product data effectively see around a 30% increase in conversion rates. That’s why it’s so important to understand what product data is—and why it matters.
What is Product Data?
A data product is a collection of data, accompanied by the necessary code for its consumption, and metadata that describes its characteristics, such as product content and product attributes.
This product-based approach to managing data stems from the Data Mesh framework, which focuses on decentralizing data management by organizing data around Domains—areas of data that are homogeneous based on their origin, use, or other criteria.
The interdisciplinary teams managing these domains have direct ownership of the data, ensuring that those with the best knowledge of the data are responsible for its handling. This approach helps avoid common pitfalls in product portfolio management and product configuration, which can arise when data isn’t managed effectively.
However, merely organizing data by domains can create silos, which is where the concept of a data product helps integrate and streamline the use of data across different domains.
Data products are designed to address specific business needs and ensure consistent, trustworthy data access. They integrate data from internal operations or other data products within or across domains, and they are listed in a centralized catalog to enable easy discovery, syndication, and integration,
Each product must meet certain criteria to be considered complete, such as being easily searchable, usable, understandable, and interoperable. These characteristics are ensured through the inclusion of metadata that describes the data’s origin, reliability, and usability.
Key principles that govern data products include:
- Domain Ownership: Data is organized by domains, where interdisciplinary teams manage data related to specific business areas. This reduces the chaos of central data management by giving ownership to those who understand the data best.
- Data as a Product: Data is treated as a self-contained product, complete with defined ownership, quality assurance, and clear delivery methods. This ensures that data products can be used effectively across business operations, analytical research, or reporting.
- Accountability and Accuracy: Data products come with guarantees of accuracy and integrity. This involves applying techniques like data cleansing and providing metadata that ensures trust in the data’s reliability.
- Interoperability: Data products must comply with global standards to enable seamless integration across different domains, while also ensuring secure, regulated access.
Additionally, applying product management principles to data products is important. These principles ensure the data product’s value is measurable and that it is built with user experience and trust in mind.
For ecommerce product data enrichment, these principles are particularly valuable, as they ensure product data across online platforms is accurate, consistent, and optimized for customer engagement.
Data products are designed for business users, allowing them to access data for decision-making through self-service tools. A strong focus is placed on ensuring each product is delivered and maintained by a responsible owner throughout its lifecycle.
Real-Life Examples of Data Products
Organizations can use various methods to process data, transforming it into valuable data products. These products serve different purposes depending on how they manage and deliver data.
Here’s a breakdown with real-life examples:
Types of Data Services
- Raw Data
Raw data is unprocessed and in its most basic form. Although not immediately useful, it can be transformed into more meaningful insights through further processing.
Example: Sensor readings from a factory’s production line.
- Derived Data
Derived data is raw data that has been processed or transformed to provide more actionable information. This often includes calculations, aggregations, or summaries of the raw data.
Example: Analyzing foot traffic patterns in a shopping mall to determine peak hours.
- Algorithms
Algorithms process data to provide results or insights. These algorithms can range from basic calculations to advanced machine-learning models. The outcomes of these processes are often used for either decision support or automated decision-making.
- Decision Support
This type of algorithm assists users in making decisions by offering recommendations or insights based on processed data. However, it still requires human input for final decisions.
Example: A financial tool that analyzes investment portfolios and suggests diversification strategies but leaves the final decision to the investor.
- Automated Decision-Making
Here, the algorithm makes decisions without human intervention, offering a fully automated solution based on the data it processes.
Example: An e-commerce platform that automatically adjusts prices based on competitor pricing data.
Services Provided by Data Products
Automated Decision-Making or Data-Enhanced Products
These data products drive decisions autonomously, often using machine learning or AI to make real-time adjustments or recommendations. By analyzing customer behavior or environmental factors, they optimize outcomes without requiring human oversight.
Example: A smart thermostat that automatically adjusts home temperatures based on the homeowner’s usage patterns and external weather conditions.
Data as a Service (DaaS) Products
Data as a Service products provide customers with access to datasets via APIs. Subscribers can integrate this data into their own systems for various purposes, from analytics to decision-making.
Example: A traffic data API that delivers real-time congestion reports and route optimization for ride-hailing services.
Data as Insights
These data products are used internally to gain insights that improve operations, drive innovation, or refine business strategies. They are not directly sold to customers but help companies optimize their own processes.
Example: A retail chain using in-store purchase data to predict stock shortages and optimize inventory levels across its locations.
Each data product example demonstrates how data, once processed and contextualized, can drive decision-making, improve customer experiences, and enhance business operations.
Advantages of Data Products
Data products offer significant long-term benefits over traditional data projects. While there may be some initial investment, they quickly evolve to support various outcomes and accommodate emerging use cases.
This flexibility allows organizations to address multiple scenarios without launching entirely new projects, ensuring a higher return on investment (ROI) and lower cost-per-use over time.
For data consumers, data products provide:
- Faster Time-to-Insight: Pre-built data products reduce the time needed to derive insights, as opposed to starting a new data project from scratch.
- Full Data Integrity: They ensure consistent, complete, and compliant data every time, providing reliable information for decision-making.
- Situational Awareness: Data products can augment data with real-time insights, giving users a clear understanding of their current environment.
- Real-Time Data Provisioning: This supports timely, informed decisions in operational scenarios.
- Data Governance: Ensuring high-quality and compliant data is embedded into every product, making it easier to meet regulatory standards.
- On-Demand Accessibility: Authorized users can access data products anytime, improving productivity and decision-making speed.
For enterprises, data products are:
- Business-Driven and Outcome-Focused: They are built with business goals in mind, delivering value where it matters most.
- Agile: They evolve incrementally, adapting to new requirements quickly and efficiently.
- Reusable: Data products are designed to be built once and reused across multiple applications or departments.
- Future-Proof: Their architecture is designed to adapt to future technologies and datasets.
- Trusted: They foster trust in data by maintaining high integrity and transparency.
- Collaborative: Data products create a common language between business and IT, ensuring better alignment across teams.
Product Data Command: Getting Started with Data Products
To get started with data products, we first need to deploy the right platform. Product Data Command offers a structured product information service interface with product manufacturers to manage a wide variety of workloads, including product content management and product feed enrichment.
The platform’s Data Product Service empowers data teams to quickly define and maintain metadata for their data products, including schemas, connectors, sync policies, data transformations, governance, and more.
Once deployed, each data product manages its dataset within its own high-performance micro-database, ensuring scalability, resilience, and agility for enterprise-level operations.
For example, a “Customer” data product collects data from all relevant sources, prepares it, and delivers it to authorized users in real time.
Appoint Data Product Managers
The next thing we need to do is to assign skilled data product managers with expertise in data, analytics, enterprise applications, and business analysis. These managers oversee the entire lifecycle of a data product, ensuring:
- Development of effective data strategies and performance metrics.
- Maximization of Return on Data Investment by ensuring data drives business value.
- Bridging the gap between business and IT by communicating the needs of data consumers to data engineers and enhancing data accessibility.
Like a software product manager, a data product manager gathers the needs of users and collaborates with data engineers and data scientists to deliver on them. Ultimately, they are responsible for defining and championing data products within the organization.
Prioritize Flexibility
After we have assigned the managers, businesses should select a platform capable of deployment on-premises, in the cloud (iPaaS), or across hybrid environments.
Supporting modern data architectures is necessary for adaptability to new trends.
Two key architectures that leverage data products include:
- Data Fabric: A modular data management framework that integrates existing data and analytics tools. Here, data products are defined centrally by a data and analytics team and adapt over time based on automated analysis of active metadata.
- Data Mesh: A decentralized approach where business domains have the autonomy to create data products tailored to their needs. It establishes a federated data network, creating a common framework for building and scaling product-driven data solutions in real-time.
While both data fabric and data mesh architectures have their pros and cons, they share one key component: data products as the cornerstone of modern data strategy.
