Elisabetta Villa

A logistics manager opens a dashboard displaying fulfilled orders, warehouse utilization, delays, operator productivity, shipments, costs, carriers, exceptions, and all those operational insights that would normally require opening multiple Excel files, requesting data from different departments, or waiting for reports that often arrive only after the operational issue has already had its impact.

At that moment, the reaction is almost always positive because the need is obvious and highly tangible. Most logistics companies, and businesses managing complex logistics operations, do not suffer from a lack of data. They struggle to transform that data into information that is readable, up to date, and genuinely useful for decision-making.

As a result, many decisions are made using incomplete information, information that arrives too late, or information that requires an unreasonable amount of effort compared to the value it should generate. Business Intelligence was created to reduce this gap between the time data becomes available and the time decisions need to be made. However, as soon as interest turns into implementation, another question inevitably arises: where is the data, and how can we make it available?

This is the point where many BI projects begin to slow down, not because Business Intelligence loses its value, but because the process enters its least visible and most delicate phase. It is the stage where logistics, IT, software providers, and management must work together to transform data already stored within business systems into a reliable, secure, and consistent flow that can power decision-making dashboards.

The problem is not the lack of data

In most cases, the data already exists. It simply was not originally designed to feed a Business Intelligence platform.

A Warehouse Management System (WMS) is built to manage receiving, storage, picking, shipping, inventory, and warehouse movements. A Transport Management System (TMS) is designed to plan routes, assign carriers, calculate transport costs, monitor deliveries, and manage documentation. An Enterprise Resource Planning (ERP) system manages orders, invoices, master data, accounting, and broader business processes. These are essential systems, often robust and highly reliable, but they were designed primarily to execute operations.

Business Intelligence requires a different approach. It is not limited to reading what happened inside a single system. Instead, it must connect multiple data sources, reconcile information created using different business rules, maintain historical records, apply calculation logic, build KPIs, and transform all of this into information that managers can easily understand.

Although this work clearly has a technical component, it is fundamentally an organizational challenge. It forces the company to answer critical questions: What do we actually want to measure? Who truly understands the meaning of the data? Who owns it? Who can provide it? And who is responsible for validating it before it becomes the basis for business decisions?

Start with decisions, not with database tables

One of the most common mistakes is starting a BI project by asking for every available piece of data, assuming that completeness automatically creates value.

In reality, this approach immediately creates resistance because the request is too broad, difficult to estimate, hard to manage, and disconnected from clear business objectives. Asking the IT department or a software provider for “all the data from the WMS” opens an enormous project before anyone has even defined which decisions that data is supposed to support.

The right question is different: Which decisions do we want to make better through Business Intelligence?

If the objective is to understand warehouse capacity utilization, the required dataset will be completely different from a project focused on measuring picking productivity, analyzing carrier delays, comparing supplier performance, calculating transport profitability, or identifying slow-moving inventory.

Every business objective requires specific data, specific business rules, and different levels of detail.

This approach reduces ambiguity and makes discussions far more productive. If the goal is warehouse utilization, the project will require data on warehouse locations, storage capacity, inventory levels, products, and warehouse movements. If the focus is transportation, the necessary information will include shipments, carriers, routes, costs, delivery times, proof of delivery, and customer data. If productivity is the objective, then activities, operators, processing times, volumes, and allocation rules become essential. In every case, the project becomes easier to manage because it starts from a clearly defined business need rather than from a generic request for “all available data.”

The value of a manageable first scope

A Business Intelligence project does not have to be perfect from the first release. It simply needs to be useful enough to demonstrate value and build trust. Yet many companies fall into the trap of trying to map everything at once, integrate every data source, build every dashboard, and solve every possible exception before seeing a single concrete result. It is an understandable approach, especially when investing in a new project, but it risks turning a high-potential initiative into something too complex to get off the ground.

In many cases, it is far more effective to define a manageable initial scope consisting of a limited number of dashboards, a small set of KPIs, and only a few data flows, carefully selected according to the company’s priorities. A dashboard for warehouse utilization, one for inbound operations, one for outbound operations, one for warehouse productivity, one for service levels, or one for transport profitability may be enough to create a first concrete use case, especially if they allow the logistics manager to access information that previously required days of manual work.

Once the company sees its own data presented in an up-to-date, interactive, and consistent dashboard, the project changes its nature. It is no longer a promising presentation, but a real tool that people can start using, refining, improving, and expanding over time. The first phase should therefore prove that the model works, that the data can be extracted, that it can be interpreted correctly, and that Business Intelligence can deliver value without waiting for every business process to be fully mapped.

Involve IT before it becomes the bottleneck

Another common mistake is involving the IT department too late, after the logistics team has already seen the solution, management has already formed expectations, and the project is perceived as something that simply needs to be “enabled” from a technical standpoint. At that point, IT is brought in not as part of the journey, but as the department expected to remove an obstacle. This approach makes resistance, delays, and misunderstandings much more likely.

IT should be involved much earlier, but in a very practical way. There is no need to bring them into a commercial demo full of dashboards and features. Instead, it is much more valuable to organize a working session to clarify which systems contain the data, who manages them, whether external vendors are involved, which extraction methods are available, which security requirements must be respected, how frequently the data can realistically be updated, and which environments can be used without creating risks for operational systems.

This type of discussion allows IT to contribute to building the project instead of being asked to approve it after expectations have already been created. When IT is involved as a project partner, it can recommend safer approaches, avoid technically fragile solutions, provide realistic timelines, and identify potential issues before they become roadblocks. It shifts the conversation from “we need your approval” to “let’s build the best way to feed the Business Intelligence platform together.”

A clear data mapping reduces meetings and misunderstandings

One of the most effective best practices is to establish a clear data mapping from the very beginning, because a data map transforms a generic request into an operational roadmap. This is not simply about listing technical fields. It means defining which information is needed, which dashboard it supports, what business purpose it serves, how important it is, and how frequently it needs to be updated. This reduces ambiguity, shortens discussions, and allows everyone involved to work from the same shared understanding.

One of the key advantages of Business Intelligence solutions specifically designed for logistics and transport is that they start from processes that are already well understood, with KPIs that have already been modeled and data structures designed around recurring activities such as inbound, outbound, picking, warehouse utilization, inventory, shipments, carriers, routes, costs, and profitability. This does not mean that integration is automatic or that every company has identical data, because every organization has its own specific characteristics. It simply means that the project does not begin with a blank page.

Having an initial data map does not eliminate the work involved, but it makes the entire process more structured. And in data projects, structure is a decisive advantage because it helps distinguish what is truly necessary, what can be added later, which information is essential, and which elements would only make the initial implementation unnecessarily complex. An effective Business Intelligence solution is not built on the amount of data collected, but on the ability to collect the right data, interpret it correctly, and make it available exactly when it is needed.

BI doesn’t create data problems. It makes them visible.

Many companies only discover the true quality of their data when they start a Business Intelligence project. Duplicate master data, customers entered under different names, inconsistently coded carriers, missing dates, transaction codes used without common rules, warehouse movements recorded differently across departments, costs stored in one system while volumes are stored in another are far more common than most people imagine. As long as data remains inside operational systems or manual reports, these inconsistencies can remain partially hidden.

When BI brings them to light, it may seem as though the project is creating problems. In reality, it is simply making visible issues that have always existed. This can be uncomfortable because it forces the company to look not only at the performance of its logistics operations, but also at the quality of the information used to measure that performance. Yet this is precisely where Business Intelligence delivers one of its greatest benefits: it helps the organization understand how reliable the data behind its decisions really is.

A dashboard is not only designed to provide a clearer view of what is working well. It also reveals where processes generate weak, incomplete, or inconsistent data.

In this sense, Business Intelligence is not just an analytics tool. It is also a tool for organizational maturity, encouraging companies to treat logistics data not as a by-product of their operational systems, but as a strategic asset that deserves proper governance.