How to Build Business Value With Scalable Data Products
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Data products are essential for companies to transform information into a strategic asset, unlock distinct competitive advantages, and accelerate accurate decision-making. Two complementary approaches drive long-term success: the modern data stack for technical sustainability, and a data product approach to address real business challenges. Technical sustainability and business-focused data solutions work together to create resilient, high-impact data products that enhance efficiency, drive smarter decision-making, and deliver measurable business value.
At Indicium, we believe that successful data solutions must be both technically sustainable and strategically aligned with business objectives. Our approach integrates the modern data stack for technical sustainability with a data product strategy designed to solve business challenges that drive impact and scale. This combination equips organizations with the agility and intelligence needed to turn data into a powerful business asset.
Define the Value: What Makes a Data Product?
The concept of data products was first defined by DJ Patil, former Chief Data Scientist for the United States, in his 2012 book Data Jujitsu: The Art of Turning Data into Product. He described a data product as “a product that facilitates an end goal through the use of data.” Zhamak Dehghani expanded on this idea in her influential article, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh, to emphasize that domain data teams must treat data as a product. She advocated for applying product thinking to data assets, considering data consumers—such as data scientists, machine learning engineers, and data engineers—as customers.
A Practical Example: Is Netflix a Data Product?
No, the Netflix platform itself is not a data product. However, Netflix’s content recommendation algorithm is a data product. The recommendation engine helps users achieve a specific goal—selecting a movie or series—through the use of data, including viewing history, content similarity, and algorithmic predictions.
Key Principles of Effective Data Products
To maximize value, data products should adhere to several core principles:
- Discoverable – Data products must be easily located within a centralized catalog that provides metadata, ownership details, and lineage. A well-organized data catalog helps prevent redundancy and ensures consistency across the organization.
- Addressable – Each data product should have a unique, standardized identifier that allows for programmatic access. Clear naming conventions reduce friction and make it easier for different teams to find and use the right data.
- Trustworthy – Reliable data is critical for decision-making. Data products must maintain high quality, ensuring they accurately reflect real-world events and insights.
- Secure and Governed – Robust access control mechanisms must be in place to enforce data security and regulatory compliance. Individual data products should have clearly defined access policies to protect sensitive information.
- Managed by Cross-Functional Teams – Building and maintaining data products requires collaboration across disciplines. Effective teams include data product managers who focus on business value, as well as data engineers, AI specialists, and analytics professionals who ensure technical sustainability.
At Indicium, we go beyond these foundational principles to develop data products that give our clients a competitive edge. Our methodology incorporates additional best practices designed to enhance usability, scalability, and business impact.
Design With Users in Mind
We believe that the development of data products should place data users and consumers at the heart of the discussion. The concerns of data development teams should go beyond questions like:
- “Which tables should we integrate?”
- “How can we build incremental models more efficiently?”
Instead, they should focus on:
- “What business process generates this data?”
- “What challenges do decision-makers face related to this business process?”
- “Who are the users/personas that will interact with the data product, and what is their level of technical expertise in data?”
- “What are the key pain points and limitations in the current format of data consumption?”
We pride ourselves on having a multidisciplinary team of specialists and proprietary training programs that set us apart in the market. Our expertise spans careers such as:
- Data Product Managers
- Data Experience Designers
- Data Consultants
- AI Engineers
- Data Engineers
- Data Analysts
- Data Scientists
Bridge Tech and Business With the Right Team
We believe data teams should support the business in decision-making to drive results. Data products must integrate into daily operations to align with business objectives and produce measurable impact. Multidisciplinary teams play a crucial role in achieving this by bridging technical expertise with business needs. Their role goes beyond building pipelines and dashboards. They create data solutions that drive informed decisions across the organization. Without this alignment, even the most advanced data products fail to create real business value.
To ensure data products deliver real value, we apply the Plan, Do, Check, Act/Adjust (PDCA) framework:
- Plan – Establish business goals and objectives.
- Do – Implement data solutions aligned with those goals.
- Check – Monitor business indicators and measure performance.
- Act/Adjust – Refine strategies based on real-world feedback and insights.
User-centric design and multidisciplinary expertise create data products that are technically robust, practical, and aligned with business needs to drive measurable impact.
Drive Adoption. Ensure Business Impact.
Building and sustaining data products demands substantial resources, from infrastructure and engineering to ongoing refinement and user adoption. Adoption determines the success of a data product. Data products deliver business value only when users actively engage with them. Organizations must track usage metrics such as user engagement, frequency of access, and the most and least utilized features. Tracking adoption provides insights that help teams refine data access, usability, and presentation. This ensures decision-makers receive relevant and actionable data and maintain alignment with business needs.
To maximize adoption and effectiveness, organizations need a structured approach to data product development. Indicium applies real-world experience, delivering over 300 data products to 60 clients. Our 4D methodology provides a proven foundation for creating scalable, high-impact solutions that align with strategic goals. A strong methodology ensures adaptability, business alignment, and measurable success in a data-driven world.
- dbt Templates and Migration Accelerators: Pre-configured templates expedite common tasks, while migration accelerators facilitate core transformation processes.
- Data Quality and Auditing Tools: Automated testing throughout the migration to guarantee data quality, mitigate risk and enhance reliability.
- Infrastructure Support: Comprehensive support to minimize disruption, for a seamless transition.
What Are the Types of Data Products?
Data products follow key principles that can be broadly categorized into five types:
- Raw Data: products that involve collecting raw data with minimal transformation or cleaning. These require users to put in the effort to generate value.
- Derived Data: products where data is transformed, cleaned, and enriched before being delivered to the user. Examples include star schema modeling or enriching operational data with customer segmentation models.
- Algorithms: data products that process data through computational algorithms and return new data or insights.
- Decision Support: products that help users make decisions without making the decisions for them, like dashboards and other visualization tools.
- Automated Decision-Making: systems that make decisions autonomously based on data, such as autonomous vehicles and drones.
Interfaces of Data Products
Beyond functional requirements, it’s important to categorize the interfaces that users interact with to generate value.
Data products can have a single interface or a combination, tailored to technical or business users:
- Application Programming Interface (API): a technical interface for integrating data products with other systems.
- Visualization: dashboards and reports tailored to the technical knowledge of the user.
- Apps: applications that directly provide value to end users, simplifying the complexity of underlying data and algorithms.
Categories of Data Products
Combining product types with interfaces gives rise to various data product categories. Here are a few examples:
- A BI dashboard for visualizing company KPIs is a Decision Support data product with a Visualization interface.
- A data warehouse combines Raw Data, Derived Data, and Decision Support, with an API interface (e.g., SQL, ODBC).
- A business group model provided by big data companies is a blend of Raw Data Collection, Derived Data Construction, and proprietary Algorithms, typically accessed via API.
- A data app for collection planning leverages Algorithms through an App interface to facilitate better decisions.
- An autonomous car uses Automated Decision-Making with the App being the vehicle itself.
- Table categorizing types of data products with interfaces such as API, Data Visualization, and Apps, featuring examples like Data Warehouse (DW), Group Model, BI, Planner, and Autonomous Car.
Build a Data Product Strategy That Delivers
According to Gartner, data products are one of the main technologies that are being adopted or will be adopted in a 12-month timeline by the world’s leading companies.
A strong data product strategy requires a structured approach across people, organization, and data. At Indicium, we apply our People, Organization, and Data (POD) framework to ensure data products create real business value.
People
A successful strategy starts with generating and disseminating knowledge among employees to enhance technical expertise related to data products. Organizations must create multidisciplinary teams that combine technical and business skills, introducing non-traditional roles such as data product managers, data experience designers, and data product analysts. Reviewing and optimizing data team structures ensures seamless collaboration and alignment with business objectives.
Organization
Organizations must promote a data product culture by fostering data reuse and encouraging collaboration across departments. Strong leadership plays a critical role in supporting and sponsoring data product initiatives to ensure alignment with strategic goals. Dedicated budgets for development and long-term maintenance enable the sustainability and growth of data products.
Data
Establishing strong data governance ensures security, compliance, and data quality. Organizations must implement structured processes for data ingestion, transformation, orchestration, and cataloging while maintaining observability. Providing intuitive self-service tools enhances accessibility and encourages broader adoption of data products.
A well-executed data product strategy connects data to business objectives, enhancing decision-making and operational efficiency.
Turn Data Into Business Results
Data products give organizations a competitive advantage by transforming raw information into actionable insights. Indicium specializes in designing and implementing solutions tailored to business needs. Our experience in delivering over 300 data products enables us to build scalable, high-impact solutions that drive measurable success.
Ready to maximize the potential of your data? Our team can help you develop a strategy that aligns technology, business objectives, and impact
About Indicium
Indicium is a global leader in data and AI services, built to help enterprises solve what matters now and prepare for what comes next. Backed by a 40 million dollar investment and a team of more than 400 certified professionals, we deliver end-to-end solutions across the full data lifecycle. Our proprietary AI-enabled, IndiMesh framework powers every engagement with collective intelligence, proven expertise, and rigorous quality control. Industry leaders like PepsiCo and Bayer trust Indicium to turn complex data challenges into lasting results.
David Eller is the Group Data Product Manager at Indicium. With a background in industrial engineering, he focuses on helping businesses create competitive advantage by developing advanced data-based solutions.
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