Demand for transportation in the U.S. accounts for a significant portion of the U.S. economy – between $1.3 trillion and $1.8 trillion, or 9.4% of the GDP.
According to the transportation services index, and despite the COVID-19 epidemic, freight service was not nearly as impacted as the passenger segment and has quickly recovered.
While freight service has bounced back, COVID-19 brought to light the need for trucking companies to rapidly adopt new technologies and tap into the plethora of data available in order to increase efficiency, profitability and remain competitive. These emerging technologies include the internet of things (IoT), vehicle-to-vehicle and vehicle-to-infrastructure communication, efficient and reliable cloud-based data storage, faster data transfer protocols, artificial intelligence and machine learning.
In a rapidly modernizing world, there is a need for companies to become more dynamic and drive insights from data. In order for data management to create real and viable results, it requires changes to the way work is organized, how data is collected and used, and often a company culture change.
As technology advances so does the amount of data generated, which makes data management and strategy even more vital for success. All of these factors have created the perfect storm for big data in trucking. As a result, how we define, store, visualize and analyze data can determine the company’s trajectory and competitive advantage.
According to the recent North Highland Annual Beacon Transportation Survey, executives understand the value of tapping into this data, but find it difficult to get started. Ninety-two percent of executives said that data and analytics are now a higher priority than last year. More than two-thirds (67%) said they would have a definite competitive advantage if they address this priority. However, the majority believe they are not prepared to do so.
An effective data strategy will position your company for future success
Transforming a company’s data into a dynamic and competitive asset is both an art and a science. It is estimated that 60% to 73% of all enterprise data goes unused for analytics.
