Let me ask you a question: is hiring data specialists enough to transform a company that has always relied on experience and intuition rather than measurable information?
In reality, the answer is more nuanced than a simple yes or no.
Companies like MICROingranaggi, which are actively going through this transformation, know that it involves confronting deeply rooted habits, long-established routines, and a natural resistance to change.
From our direct experience, we can say that
simply adding new skills is not enough. Two essential conditions must be in place first: a clear strategic direction and someone capable of turning that vision into reality.
The first requirement is genuine commitment from management—not merely a statement of intent, but a concrete decision. Without it, any initiative is likely to remain confined to a single department.
The second is the ability to translate that vision into practical tools, processes, and operating methods. This means going beyond analysis and actually building the infrastructure required for success: a roadmap, data collection systems, and everything needed to make data genuinely usable.
There is another important consideration.
This transformation is not an overnight process.
Before discussing optimization, a solid foundation must first be established. In practice, this means creating reliable KPIs, trustworthy data, and processes that can be measured consistently. Only then does it become possible to move into a phase of continuous improvement.
Once every department has clear performance indicators that remain consistent over time, the objective changes.
The focus shifts from measuring in order to understand, to improving what is already understood.
Cost is another factor that cannot be overlooked.
Every data-related initiative—IT infrastructure, data collection systems, cybersecurity, storage capacity—comes with an economic cost, and that cost must be sustainable.
Every investment needs to be proportionate to the company’s resources. Whether it involves upgrading hardware, expanding storage, or strengthening security systems, each decision must strike the right balance between long-term improvement and day-to-day operational needs.
Otherwise, the risk is building a technically sound system that simply cannot be justified from a business perspective.
One of the most difficult aspects to understand and manage is measuring the actual return on these investments.
This is particularly true for support functions, where the benefits are not always easy to quantify.
In production, for example, reducing cycle times or lowering scrap rates generates measurable financial results.
But there are situations where improvement actually requires longer operating times.
Additional quality inspections are a good example. They may initially slow down production, yet significantly reduce defects, customer returns, and overall costs.
The real question, therefore, is not whether a process should be faster or slower.
It is about choosing between two different models of efficiency: one focused on immediate speed, the other on long-term stability and control.
Is it better to have a faster process that generates defects, or a slightly slower one that consistently delivers reliable results?
Data does not automatically provide the answer.
What it does provide is the ability to understand the consequences of each decision.
And that is perhaps the most important lesson we have learned during MICROingranaggi’s own digital transformation.
The objective is not to measure everything—it is to understand what is truly worth measuring, and to what extent.
