Companies that are data-driven demonstrate improved business performance. McKinsey says that data and analytics can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to MIT, digitally mature firms are 26% more profitable than their peers [2]. Forrester research found that organizations using data are three times more likely to achieve double-digit growth [3]. A study from Boston Consulting Group (BCG) found that four out of the first five innovative companies in the world are data firms [4]. Fundamentally, data has the potential to improve the company’s revenue, reduce expenses, and mitigate risk. 

However, many companies struggle to leverage data for operations, compliance, and performance. Harvard Business Review (HBR) research says up to 85% of big data projects fail [5]. A VentureBeat article says 87% of data science projects never make it into production [6]. There are many reasons for this. But one main reason is just capturing and storing data doesn’t help enterprises derive value from data; data needs to be used for business purposes [7]. Raw data needs to be processed to become an asset, and that data asset once derived should be further managed as a data product with clear utility and monetization. 

However, many firms often use the terms resource, asset, and product interchangeably in association with data. But all these terms are very different and have distinct meanings in the context of data and business performance. This blog demystifies the differences between these concepts from a data and business perspective:

1. Resource

A resource is defined as something that can be potentially used or consumed to generate value. It is raw and often requires transformation or processing before it becomes useful. In the data context, it is raw data collected from sensors, web pages, user inputs, or external sources in its native or natural format. The key characteristics of data when viewed as a resource are:

• Data is unrefined (i.e., captured and stored it in original or native state).
• Requires manipulation, cleaning, or processing to extract value, or rather specifically – additional value. While data in a raw or unstructured format might be valuable for operations and compliance, data needs to be transformed into a structured format if it needs to be used for deriving insights.
• Used as an input to create data products or data assets.

2. Asset

An asset is something that holds intrinsic value and can generate benefits or provide a return over time. In a business sense, assets can be tangible (machinery, land, and people) or intangible (contracts, intellectual property, brand, and data). In the data context, data is an asset if it is organized, cleaned, profiled, and structured and used for operations, compliance, and analytics. The key characteristics of data when viewed as an asset are:

• Holds measurable value and offer competitive advantage
• Often generates ongoing benefits
• Can be owned or controlled

3. Product

A product is a good or service or both designed for the end user that satisfies a need or solves a specific problem. It is the result of processing resources or leveraging assets, with clear utility or market value for monetization. In a data context, a data product is a customer-facing application or API (application programming interface) that is built on top of processed data (i.e., asset) or raw data (i.e., resource) for monetization or improved business performance. The data monetization strategies could include licensing, subscription models, API monetization, and more. The key characteristics of data when viewed as a product are:

• Packaged for external consumption or use
• Designed with the end-user in mind, providing a solution to a specific problem
• Can be monetized directly through sales, licensing, subscriptions, APIs, and more

The relationship between data as a resource, asset, and product is as shown.

To summarize, resources are raw materials, assets are refined and valuable entities that drive strategic advantage, and products are marketable solutions that directly meet user needs. For example, crude oil is a valuable resource, but its value is enhanced when it is processed or refined in the refinery to derive gasoline. But selling a valuable product like gasoline only at the refinery is not valuable; it must be moved closer to the consumer. The value of gasoline further increases when it is offered as a product at various gas stations at various branded grades (based on octane ratings) to meet the needs of different engine types, customer’s convenience/location, and more.

In conclusion, the evolution of data from a resource to an asset and finally to a product reflects the increasing recognition of the value of data in driving business performance. As a resource, data serves as the raw material that organizations collect and store. When treated as an asset, it becomes a critical component for shaping strategy and efficiency. Ultimately, as a product, data transforms into a refined, monetizable offering that generates revenue or delivers a competitive advantage. By understanding and leveraging data at each stage, businesses can unlock its full potential, turning it into a powerful driver of innovation, growth, and differentiation in today’s competitive marketplace.

References

1. Bokman, Alec; Fiedler, Lars; Perrey, Jesko; Pickersgill, Andrew, “Five facts: How customer analytics boosts corporate performance,” mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance, Jul 2014.
2. MIT, “Digitally Mature Firms are 26% More Profitable Than Their Peers,” ide.mit.edu/insights/digitally-mature-firms-are-26-more-profitable-than-their-peers, Aug 2013.
3. Evelson, Boris, “Insights Investments Produce Tangible Benefits — Yes, They Do,”forrester.com/blogs/data-driven-insights-and-ai-informing-and-automating-complex-decisions
4. bcg.com/publications/2023/advantages-through-innovation-in-uncertain-times
5. hbr.org/2020/02/use-this-framework-to-predict-the-success-of-your-big-data-project
6. venturebeat.com/ai/why-do-87-of-data-science-projects-never-make-it-into-production
7. mitsloan.mit.edu/ideas-made-to-matter/15-quotes-and-stats-to-help-boost-your-data-and-analytics-savvy