“A data-rich company has a full warehouse. A smart company has a business that makes better decisions. It’s not the same, and confusing the two costs a lot of money.” This is how bluntly Fernando de Águeda, CEO of Scalian Spain, describes the way data is leveraged by the various productive sectors in Spain.
He is referring to the so‑called “dark data”, that is, the information that accumulates and is stored, with the corresponding cost, but that is not used. Accumulating is easy, he concludes, “and it gives a sense of progress. Using it is hard and yields results. The smart company is not the one that stores the most, but the one that fastest turns data into action and an action into a result.”
How is data transformed into an intelligent decision? Through good governance: analyzing it, updating it, and using it to achieve results. This means incorporating it into daily operations in pursuit of greater usefulness.
Effective data management at three speeds of business
A new question emerges here: which sectors lead this productivity based on effective information management? From Scalian they identify three speeds:
- In the early stage are the sectors that already have a mature, institutionalized data management in the information they store. In banking, for example, data acts as the “nervous system” of the business. Everything passes through it: risk, fraud, segmentation, compliance; every decision made is based on evidence.
- Telecommunications and energy operate with similar discipline and maturity, also as early adopters of these processes. In the cases mentioned, effective data governance relates to both economic efficiency and the success and continuity of critical operations. As Fernando de Águeda notes, the leadership of these companies in this area responds not only to trends or fashions, but to a real need to confront risks and costs that are too high.
- For the Scalian Spain director, it is important here to differentiate between exploitation and maturity, which leads to the second stage, where retail and logistics stand out. Retail, he explains, has achieved something difficult, with a relatively modest footprint: bringing data—assortment, price, promotion, stock—into day‑to‑day decisions and translating it into immediate commercial decisions. As for logistics, “it demonstrates one thing that is sometimes forgotten: data adds value even when it never touches the end customer. An optimized route, an asset that doesn’t stop, an incident you anticipate… that’s pure margin.”
Healthcare and manufacturing accelerate their data investments
The third level comprises those accelerating investment in the data area, with enormous development possibilities, but still far from turning them into a business asset. The healthcare sector would be the best example, with dormant potential that currently prevents it from utilizing a valuable raw material. The problem, according to de Águeda, could lie in data fragmentation as well as regulation and interoperability.
Alongside healthcare, manufacturing is pursuing today a good synergy between plant-floor information, actual production, maintenance, and the supply chain. The leap consists of turning process monitoring into a raise in productivity, reducing downtime and optimizing resources.
Beyond the classification, these sectors share a common lesson: the value of data does not depend on the volume of information available, but on its integration into decision making. The difference is not in how many data are generated, but in the ability to turn them into business decisions.
Timelines in data management that endure: who owns the information?
One of the historical mistakes in adopting new technology for the company has consisted of confusing data with artificial intelligence. They are not the same; one relies on the other. A good artificial intelligence is not possible without the right data. If the data are dirty, biased, or poorly governed, AI becomes ineffective. It translates them into information of little value, unreliable, or simply incorrect. The foundations of a good business tool are the data.
To this error, others are added, highlighted by Scalian:
- Adopting a technology before fine‑tuning the decisions that will update or improve the business. In other words, in many cases a data platform is acquired, structured, and only then are business objectives defined. The correct order is precisely the opposite.
- Confusing governance with bureaucracy. “Governing data is not about setting up a committee that says no to everything; it’s about making business people trust the data they use. When governance is experienced as a brake, it’s because it’s poorly done,” warns Fernando de Águeda.
- Treating data quality as a project with a start and finish date. For that work to be valuable, it requires ongoing maintenance and periodic updates.
- Data management relegated to the technical department, leaving out the business side. The owner of the data must be the one who uses it to make decisions.
In short, the executive concludes, the future belongs to those companies that stop counting the terabytes they store and are able to turn them, by building a culture, into processes and actions that respond to increasingly competitive environments.
