From Data to Decisions: Building Effective BI Systems in Transportation

It should be noted that these improvements did not immediately impact all areas of data processing and transmission. Paper-based reporting has remained (and in many cases still remains) in use. Furthermore, the availability of large volumes of data does not inherently ensure its structure or consistency. It is still common for different departments to use identical metrics without alignment or awareness.

As a result, top management often receives fragmented and disconnected information that does not form a unified view. Instead of enabling data-driven decision-making, this leads to limited transparency and an inability to clearly determine how and where the business should evolve.

At first glance, Business Intelligence (BI) systems appear to address these challenges. However, there are important nuances to consider.

It is often assumed that BI systems are largely standardized across industries. In practice, however, significant differences exist between business domains. These differences extend well beyond the simplified points typically presented in marketing materials.

Let us begin with the specifics of the business domain.

The transportation services market represents a highly complex system that facilitates the movement of goods and passengers. It encompasses a wide range of participants and activities, from direct transportation services to comprehensive logistics management. In addition to primary stakeholders such as carriers and customers, numerous other participants play critical roles, including intermediaries, logistics operators, maintenance and repair providers, brokers, consulting firms, and government authorities. The macroeconomic and social importance of transportation — particularly for large-scale businesses — is both substantial and evident.

According to analytical data, in 2025 the global transport services market demonstrates growth in monetary terms, while certain segments show a decline in physical transport volumes. Experts highlight reduced demand in areas such as container shipping, along with a decrease in “grey” imports. The overall market is projected to reach approximately USD 7.5 trillion by 2029.

One of the key operational trends is multimodality. Companies increasingly combine multiple modes of transport to optimize costs, achieving savings of up to 30% or more. At the same time, management flexibility is becoming increasingly critical. This enables organizations to mitigate risks and respond rapidly to changes through supplier and route diversification, inventory optimization, and customized customer approaches based on delivery requirements and transport conditions. In essence, adaptability is becoming a core capability.

Another prominent trend is sustainability and environmental responsibility. While its importance varies depending on regulatory and economic conditions, reducing carbon footprint and improving non-operational metrics often directly enhances competitiveness.

Automation and robotization are also advancing rapidly. Many operational processes — from loading to autonomous vehicle systems — are already automated, and this trend is expected to continue.

Digitalization remains a fundamental and ongoing trend. As previously mentioned, the use of sensors and real-time data transmission continues to expand. Technologies such as IoT devices and RFID tags are becoming standard for both vehicles and cargo. This creates significant value: continuous data collection combined with high-speed transmission (e.g., 5G) establishes a strong foundation for advanced analytics, such as identifying schedule deviations, recalculating ETAs, and notifying stakeholders.

Automated processes are increasingly integrated rather than isolated. Supply Chain Management (SCM) systems unify operations, enabling real-time tracking of status changes and immediate responses to key events. In this context, Big Data is no longer a theoretical concept but an operational reality. Accordingly, advanced data storage and processing tools — including AI-driven solutions — are being implemented. These technologies support not only decision-making but also predictive analytics, delivering measurable business benefits.

Like any enterprise system, a BI system is designed to address specific business challenges. In the transportation industry, problem statements are often formulated as follows: delivery plans are disrupted due to vehicle delays caused by external factors such as weather conditions; additionally, fleet utilization is suboptimal.

A logical solution is to integrate and consolidate telemetry data, order management data, and external data sources such as weather services, and to provide near real-time dashboards that enable route optimization and cost reduction.

Business intelligence in transportation — similar to other industries — represents both a tool and a process for end-to-end analysis of historical and current data, aimed at generating insights for informed, data-driven decisions. It is therefore essential that individual use cases are integrated into a comprehensive solution. Only in this way can organizations analyze cause-and-effect relationships, explore data across multiple dimensions, and align analysis with actual business needs.

It is important to emphasize that regardless of company size — from small fleets to large-scale operations — structured data collection, processing, and visualization provide tangible business value. However, as organizations grow, adopting a holistic approach to BI system development becomes increasingly critical.

The process should begin with a fundamental question: who are the consumers of BI data? In most cases, the answer is broad and includes multiple roles. While users can be grouped by department or function, the primary focus should be on clearly defining the business objectives the system must support.

For example:

• For top management, the objective is to obtain a comprehensive view of business performance, enabling root-cause analysis and strategic decision-making.

• For dispatchers or operators, the objective is to access real-time vehicle data to support operational decisions, such as route adjustments.

The number of users and objectives can be substantial, and documenting them may require significant effort. However, this step is critical, as these objectives define the value of the BI system. A system that exists solely to generate reports is unlikely to deliver meaningful business outcomes.

The next step is to design user scenarios — structured pathways that enable users to achieve their objectives. This represents a key design challenge. While general UX principles remain applicable, B2B systems differ from consumer applications. The goal is not user retention or monetization, but the efficient completion of tasks with minimal friction.

Each scenario must be refined based on its usage context. For example, dashboards may be accessed via desktop devices, mobile phones, or large display panels. While responsive design addresses basic requirements, optimal solutions require tailored visualization and interaction models for each context.

In many cases, simply presenting data is insufficient. Factors such as readability, lighting conditions, and display characteristics must be considered. Users frequently interact with dashboards through filtering, sorting, and switching between visualization formats. Drill-down capabilities are essential for multi-level analysis, and even simple interactions — such as selecting a reporting period — must be carefully designed.

Additionally, systems should actively support users in decision-making. This includes visual cues (e.g., color coding), automated sorting, alerts for threshold breaches, and similar mechanisms. These requirements should initially be defined at a conceptual level to maintain flexibility and enable optimal design solutions.

Consistency in terminology is essential. The same metric must not be labeled differently across the system. All definitions — including names, units, and timeframes — must be standardized and consistently applied to ensure a unified information environment.

Equally important are data collection and processing methodologies. Every data point must have a clear and transparent origin. Data presentation standards — such as formatting conventions — must also be defined and consistently implemented.

Information architecture defines how data is structured and interconnected within the system. It includes entities, relationships, navigation paths between reports, and logical connections between indicators. Although not directly visible to users, a well-designed architecture significantly improves usability and scalability.

A robust information architecture ensures that new features and data domains can be integrated without structural limitations.

In addition to telemetry data, BI systems must integrate multiple data sources, including employee data, business processes, organizational structures, orders, contracts, pricing, and exchange rates.

Without clearly defined business objectives, organizations risk continuously expanding data sources and dashboards without achieving meaningful outcomes. This often leads to situations where numerous dashboards exist but are rarely used.

Only after defining objectives and assembling the required data should organizations proceed to selecting technologies and defining detailed technical requirements.

Modern transportation businesses do not require static reports — they require rapid response capabilities. The goal is to enable timely decision-making while minimizing risks.

Consider the following indicators:

If even some these issues are present, it indicates the need to implement or comprehensively enhance a BI system. Feel free to contact us for more guidance on this topic.