Many businesses are exploring strategies for driving tangible value from data, but how they do this can vary greatly. Two terms often thrown around are “data products” and “data as a product.” While they might sound similar, they represent distinct approaches to the data game.

1. Data Products: Insights Delivered
2. Data as a Product: The Raw Material
3. The Interconnected Ecosystem
4. Choosing the Right Approach

Data Products: Insights Delivered

Think of a data product as a curated experience. It takes raw data, cleans and prepares it, analyses it, and presents insights in a user-friendly format.

Data products are designed to solve specific problems for internal or external users. They often leverage data visualization, machine learning models, and intuitive interfaces to deliver actionable insights.

Building a data product requires a blend of data science expertise and product development skills.

Here are some key characteristics of data products:

• Focused: They address a particular user need or business goal.
• Usable: They are designed for ease of use, often with intuitive interfaces and clear visualizations.
• Integrated: They seamlessly integrate with existing workflows and systems.
• Value-driven: They deliver tangible benefits, such as increased efficiency, improved decision-making, or enhanced customer experiences.

Examples of data products include:

• Customer segmentation tools that categorize users for targeted marketing campaigns.
• Fraud detection algorithms that flag suspicious activity in real-time.
• Sales forecasting models that predict future demand.

This eBook explores the following topics:

• From Data to Product: Harnessing the power of context and usability.
• The DNA of a Trusted Data Product: Building trust, usability, and value.
• Building the Bridge to Trust: Delivering reliable data products.
• From Blueprint to Impact: A data product success workflow.
• Why Trust is the Cornerstone: The foundation for financial services data products.

Data as a Product: The Raw Material

On the other hand, “data as a product” (DaaP) refers to the practice of treating raw or processed data itself as a commodity to be sold.

This data can be anything from demographic information to financial records, often targeted towards external users.

Here’s what defines data as a product:

• Commoditized: It’s treated as a sellable asset, often with standardized formats and pricing models.
• External focus: It’s primarily aimed at organizations outside the data provider.
• Limited usability: It may require expertise or additional tools to analyze and derive insights.

Data quality, security and accessibility are paramount when dealing with data as a product. Providers need to ensure the data is accurate, up-to-date, and adheres to privacy regulations, and that it is easy to integrate into the client’s data ecosystem.

Examples of data as a product include:

• Location Intelligence data sold to businesses for planning and customer analytics
• Market research data sold to businesses for competitor analysis.
• Consumer behavior data used by advertising agencies to target specific demographics.
• Financial market data traded by investment firms for algorithmic trading.

The Interconnected Ecosystem

Data products and data as a product are not mutually exclusive. In fact, they can exist within the same ecosystem. Raw data might be sold as a product, while also being used internally to create data-driven tools.

Choosing the Right Approach

The best approach depends on your specific goals.

Data products are ideal for organizations aiming to unlock the value of their own data and create internal solutions.

Data as a product might be a good strategy for companies with valuable proprietary data they can monetize externally.

Ultimately, both data products and data as a product play a crucial role in the data-driven landscape. Understanding the distinction empowers you to leverage data strategically and maximize its potential for success.