The "Cart and Engine" Illusion in the Data Race

Imagine you run a national railway network, and before you are hundreds of tons of precious cargo to transport.
Does economic logic suggest dedicating "an entire engine" to pull each "car" separately? Of course not.
Logic dictates building a giant engine to pull dozens of cars behind it through standardized infrastructure.

Yet this obvious error in the world of transportation is precisely what major institutions commit today in the world of "data".
As the pace of digital transformation accelerates, many companies still manage their data programs with an "isolated project" mentality, building expensive, costly technical solutions to serve a single business objective. The result?
Scattered data programs, choked return on investment (ROI), and complete inability to scale.

In 2022, the shift began toward the concept of "Data Products" — high-quality, unified sets of data ready for reuse across different departments of the organization.
Today, with the unprecedented explosion of generative artificial intelligence (Gen AI), expanding data products is no longer merely a technical luxury, but a strategic necessity for survival.

Through analyzing experiences of dozens of leading global companies, five strategic lessons emerge that transform data from mere "raw assets" into "operational levers" that double profits and slash costs.

1. Return on Investment First: The Myth of "Perfect Data"

In executive corridors, Chief Information Officers (CIOs) often fall into the trap of pursuing "purer data," forgetting that the primary goal is "greater business value".

The new rules of the game state that no data products program should be launched before leadership has a rigorous understanding of the economic value it will generate.
The secret does not lie in building the product, but in "clustering use cases."
If there are five high-value business tasks that depend on the same underlying data set, that is strategic justification for building a data product.
But if the task is orphaned, there is no point in investing.
Leaders must have a clear roadmap linking each data product to expected financial returns over 12 to 24 months.

2. Cost Engineering: Activating "The Flywheel Effect"

Here the language of numbers that Wall Street loves comes into play; misunderstanding the economics of data leads to resource waste.
The true value of a data product does not appear in its first use, but in the marginal returns resulting from its reuse.

About 60% to 80% of the costs of building a data product (such as cleaning, scheduling, and quality assurance) are foundational costs paid once (sunk costs).
Once the product launches, these costs are amortized with each new use case.
Studies have proven that companies expanding one data product to serve 5 use cases experience a cost reduction of 30% to 40% compared to building separate data pipelines.
In short: each additional use accelerates profit harvesting (sometimes by 90%) and reduces marginal cost to near zero.

3. Scalable Infrastructure: Transitioning from "Craftsmanship" to "Industry"

Companies that shortcut infrastructure investment (Data Engineering) pay a high price later.
For the "flywheel" to work, data products must be designed flexibly to allow future evolution without demolishing the foundation.

This requires an industrial methodology that includes:

  • Data Simplification:
    Collecting base data (such as birth date) rather than pre-calculated data (such as age at first purchase), ensuring flexibility for reuse.
  • Application Programming Interfaces (APIs):
    To ensure smooth data flow to existing systems.
  • Process Automation (DataOps):
    Transforming slow manual processes into automated and documented workflows.
  • An internal data store:
    Similar to an (App Store) allowing all business units to search for and access data products with a single click.

4. CEO Mentality: Managing Data as an "Independent Company"

The most important and pressing lesson:
Data products are not IT projects, they are business lines.
Failure is guaranteed if leadership is handed to software engineers alone.

Successful organizations appoint "Data Product Owners" (DPOs) with entrepreneurial mindset.
These leaders do not focus on writing code, but on key performance indicators (KPIs), cost reduction, finding new sales opportunities for the product within the company, and linking data to profit and loss (P&L).
Moreover, business units—not technical departments—should take responsibility for determining the shape and function of this product to ensure it touches the real needs of the market.

5. The Technological Catalyst: Generative Artificial Intelligence as an Operational Lever

Generative artificial intelligence (Gen AI) has caused an earthquake in the data product development cycle; proving its ability to accelerate the building process by up to three times compared to traditional methods, at much lower cost.

Instead of settling for traditional structured data alone, Gen AI enables companies to extract enormous value from "unstructured data" (such as customer reviews, images, and videos), and integrate it into data products for sentiment analysis and future behavior prediction.
It also takes on routine tasks such as writing transformation code and testing data quality, freeing human minds for strategic thinking.

From Crude Oil to Refineries

Let us borrow a classical analogy: "Data is the oil of the twenty-first century."
But crude oil in its natural form has no value; its real value lies in "refineries" that turn it into fuel that drives the wheels of the economy.

Data products are the "refineries" of the digital age. They are the missing link that transforms random data streams into strategic decisions and decisive actions. But this value will only be realized when executives abandon the "build once" mentality and adopt a strategy of "scaling and reusing." The winners in tomorrow's economy are not those who own the most data, but those who excel at manufacturing, marketing, and scaling it professionally.