Optimising data processing in the fleet ecosystem

The fleet ecosystem is made up of multiple data sources. As the volume of available data grows, it becomes increasingly challenging to structure and process. Digital fleet management allows for an agile, more efficient and cost-effective management of corporate mobility.

September 14, 2021
Fleet Management Tips

Take away 3 points

  • The growing diversity of data sources in the fleet ecosystem is making it increasingly challenging to process and obtain relevant information.
  • The advancing proliferation of digital solutions provides businesses with new opportunities to improve organisational performance.
  • Consolidating and processing data from across the fleet ecosystem via a cloud-based, digital platform leads to more cost effective and efficient management of corporate mobility.

The proliferation of digital technologies causes significant changes in how businesses and markets operate. The amount of data collected and processed continuously grows as digitalization progresses, making it increasingly difficult for users and decision makers to manage. This leads to considerable transformation in managing corporate fleets. Digital fleet and mobility solutions offered by OviDrive significantly increase the efficiency of assembling and managing data in the fleet ecosystem.

Digitalisation in contemporary business

It is widely understood by businesses that digitalisation is an inevitable process if the company wants to survive. According to a long-term series of cross-sectoral studies on digital transformation in German companies, 90% of business practitioners agree that digitalisation is crucial for the company’s future viability, 68% agree that it will increase customer satisfaction, 57% see it as a means of opening new markets, and 30% as a way to maximize profits [1]. It is evident that digital solutions are becoming the standard. However, managers still lack a clear understanding of what changes to implement and how. As a result, the innovation is too shallow to lead to substantive changes that would allow the business to gain competitive advantage [1, 2]. For example, replacing a locally hosted spreadsheet with a cloud-based one will do little in terms of increasing performance, reducing administrative burden, or optimising costs. Businesses must seek to automate data collection and processing in order to achieve that.

Digitising fleet management

Managers and decision-makers must take into consideration an ever-increasing amount of very fragmented data. In order to benefit from the information that is carried by the data in decision-making processes, they must turn to advanced data processing solutions. Therefore, there is a need for a framework that combines the data refining process with the fleet ecosystem and its management practices. The ecosystem is a network of interdependent sources of data that needs to be processed and refined. The complexity of the multi-faceted relationships between the different actors in the system requires solutions that will efficiently and holistically consolidate the data input from various sources in order to provide relevant information to managers and decision-makers. In order to have a clear understanding of the fleet ecosystem, data is assembled from all the entities involved. The data relating to the fleet are dispersed throughout the industry. For example, the original equipment manufacturer (OEM) has product data, the car owner has process data, and a service provider has the data related to a particular vendor. The fragmentation and dispersion of data across the ecosystem make it difficult to process and hinders the decision-making process, user experience, and value creation [2].

Figure 1. Data sources in the fleet value ecosystem

Manual input of such a large pool of information would be grossly inefficient or, in some cases, impossible. The process should therefore be automated as much as possible. An automated fleet operating system assembles product data from the original equipment manufacturer, i.e., the car producer, vehicle-specific data via onboard diagnostic systems or an API, integrates data collected from vendors or service providers as well as information on the drivers [2]. Such solutions eliminate possible human error, save time, increase cost-effectiveness, and improve the company’s competitive advantage.

The information introduced into the OS is based on raw data provided by the various entities from within the ecosystem. Such raw data alone provides limited knowledge to the user [2]. The fleet operating system will refine and analyse the input to deliver processed and easy-to-use information to the end-user. For example, drivers can gain access to all the necessary information related to the vehicle, available services, or disruptions through a mobile app. This facilitates the decision-making process and liberates the user of the cognitive burden associated not only with data collection but also its processing, making for a safer and more comfortable driving experience and easier fleet management possibilities.

Benefits of automated data analysis systems

Available studies on the use of mobile technologies in organisations, albeit still limited, clearly indicate that introducing cloud-based mobile technologies and linking employees via mobile solutions significantly improve organisational performance. A British study identified five capabilities that such technology brings or enhances in an organisation [3]:

  1. Leveraging mobile technologies - organisations gain the possibility of using existing resources in a new way, mobilise new ones, or acquiring and developing new skills.
  2. Transforming - organisations improve communication, asset and project management, and service offering and delivery which results in operational and cost efficiency, operational productivity, strategic and operational flexibility.
  3. Learning - employees not only gain and share new information within the organisation but, with that new pool of knowledge, may adapt better to changes in the business environment.
  4. Problem-solving - information gained through the introduction of new technologies allows for better and more informed decision-making and the creation of new solutions within the business.
  5. Leading - the introduced solutions impact the organisation’s culture and employee behaviour and are a driver for strategic change.

There is, therefore, a clear indication that introducing an automated, cloud-based data collection and analysis technology will positively impact the business. Fleet managers and decision-makers will not only receive access to clear and straightforward information but also gain new possibilities in managing assets, employee mobility, and costs. The technology will also positively impact driving behaviour. Two Israeli studies examining over 450 drivers and over 11 thousand trips in total suggest that risky driving behaviour can be significantly reduced when the driver has access to information resulting from data recorded through a fleet management system, paired with an up to 10 percent drop in fuel consumption [4].


In conclusion, the amount of available data within the fleet ecosystem is becoming increasingly difficult to manage. Manual data input is a difficult, time-consuming process that is prone to human error. The current progress in digitalisation allows for the adoption of automated data assembly and processing. Solutions like OviDrive digital fleet management systems allow organisations to reduce the costs, time, and work required to optimise the use of their fleets.


  1. Transformationswerk. Transformationswerk Report 2016. In I. Stoll & W. Buhse (2016).
  2. Kinnunen, Sini-Kaisu, Jyri Hanski, Salla Marttonen-Arola, and Timo Kärri. "A framework for creating value from fleet data at ecosystem level." Management Systems in Production Engineering (2017).
  3. Bolat, Elvira. "Discovering magic of mobile technology in business: Strategic marketing perspective." In Creating Marketing Magic and Innovative Future Marketing Trends (2017): 1125-1138.
  4. Levi-Bliech, Michal, Polina Kurtser, Nava Pliskin, and Lior Fink. "Mobile apps and employee behavior: An empirical investigation of the implementation of a fleet-management app." International Journal of Information Management 49 (2019): 355-365.

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