What is aData Product?

And why do they matter?

Definition

According to Gartner®, a data product is essentially a set of data, metadata, semantics, and templates that are curated and maintained to be immediately useful for specific business objectives. This principle ensures reliable, high-quality data that drives informed decisions and strategic insights.

Data products are a trusted source of information for both business users and data experts. They are ready to use, easily accessible, organized, governed, and designed to solve real business problems.

Once created, data products are made available in a Data Product Marketplace where they can be leveraged and reused for business and D&A (Data and Analytics) use cases.

Quote

Data as a product is about applying product thinking to how data is modeled and shared.

Zhamak Dehghani

Why treat data as a product?

The business value of data products lies in improving decision-making by providing organized, high-quality data tailored to specific business needs.

They streamline data management, break down silos and enable more efficient and cost-effective use of data resources across the enterprise. In addition, data products drive innovation and competitive advantage by enabling data-driven insights and strategies. This directly impacts business performance and growth.

According to Zhamak Dehghani, a high-quality data product can be easily used by everyone in the organization to create value. It must be feasible, valuable, and usable. In essence, a data product should have these characteristics:

Data Product Properties

A few benefits at a glance

Increased data access

Data products streamline data retrieval processes
Example: A marketing team quickly accesses customer behavior data to tailor campaigns, significantly reducing data search time and optimizing marketing spent.

Effective collaboration

Unified data sources improve cross-functional teamwork
Example: Product design and marketing collaboratively use customer feedback to align product features with marketing strategies. This leads to effective product launches, with marketing campaigns highlighting the most appealing product features.

Reduced data storage costs

By eliminating redundant data, storage costs decrease
Example: An IT department cuts cloud storage by consolidating duplicate databases, translating into annual savings for data storage, reducing operational expenses.

Enhanced decision-making speed

Faster access to high-quality data leads to quicker decisions
Example: A sales team rapidly adjusts strategies based on real-time customer purchasing data resulting in faster response to market trends and increased sales.

Increased ROI from initiatives

Better data utilization leads to higher returns
Example: A product development team uses customer feedback data to design features, leading to increases in product sales and a rise in customer satisfaction ratings.

Time saved in data preparation

Automated data cleaning processes save time
Example: Data scientists spend less time on data prep, allocating more time to value-generating tasks like advanced analytics projects.

Improved data quality metrics

Standardized data quality checks ensure accuracy
Example: A finance department observes a reduction in reporting errors due to cleaner data. This leads to more reliable budget overview and more precise financial planning.

Facilitating data governance

Streamlined governance processes ensure adherence to standards
Example: A pharma company easily meets strict regulatory requirements by implementing data governance protocols within its data products, ultimately enhancing market reputation.

Scalability in data handling

Data products adapt to growing data needs without significant resource increases
Example: A rapidly growing online streaming service uses data products to efficiently manage its expanding user data. This allows them to maintain high service quality and user experience despite growing user numbers.

Cost efficiency in operations

Optimized data management leads to overall operational cost savings
Example: A logistics company reduces fuel costs by analyzing and optimizing routes using data products, eliminating unnecessary and costly transportation.

According to Gartner®, data products enable faster, self-service access to pre-integrated, use-case specific data sets, reducing IT dependency and improving decision making. They foster trust and scalability, create new revenue streams through data sharing and monetization, and align with DataOps for efficient, agile data management and integration.

Quote

Data experts spend 90% of their time finding data and just 10% on generating real value. Making data available and usable to all is the key to success—and we tackle just that with One Data.

Dr. Andreas Böhm, Founder and Managing Director, One Data

Get started with building data products

What do you need to build such a data product? With One Data you pave the way for your first data products and have the unique opportunity to implement Data Mesh in your organization. Your first steps towards a data product are:

Problem definition and objective setting

Business users define the business problem or opportunity and set objectives for what the data product should achieve.

Data collection and integration

Data experts gather and integrate relevant data from diverse sources, ensuring it aligns with the defined business objectives.

Data cleaning and preparation

Data experts clean, normalize, and structure the data to ensure its quality and usability for the intended purpose.

Development of analytics and models

Data experts apply analytical methods or develop machine learning models to extract insights or predictions. They closely align with business users to ensure these insights are actionable and relevant to business needs.

Deployment and accessibility

Data experts deploy a data product in a user-friendly format. They ensure technical robustness while business users provide feedback on usability and integration into business workflows.

Continuous improvement and monitoring

Data experts and business users collaboratively engage in ongoing monitoring and refinement of the data product. This includes tracking performance metrics or gathering user feedback. It ensures the data product remains effective, relevant, and aligned with evolving business objectives.

With One Data, you can build, manage, and share such data products.
The Data Product Builder uses AI in every step of data product development, significantly reducing manual effort. This makes it fast and easy to generate new, measurable business value from data.

Learn more about data product building with One Data →

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