What we do as a team By Tarik Agayr, Lead Product Manager - Data Science & Analytics at Shippeo
Over the years, we've been gathering a significant amount of extremely valuable data on shippers’ logistics operations with real-time tracking information. Our team was created to take advantage of this data to build new "data products".
Data products are products whose primary objective is to rely on data to achieve an end goal. At Shippeo, these fall into 4 different categories:
- Raw data extraction: We make the data stored in Shippeo available -as is- to everyone who wishes to extract and use it. That's the Shippeo EXPORT feature, which we keep feeding as the data model gets enriched with new information
- Analytics: We provide users (both internal and external) with analytics to get insights from the data, and help them with their decision-making. This includes INSIGHTS (overall operational performance, tracking compliance), as well as many dashboards that the team builds for our operations team to answer questions such as: “How relevant are the alerts that we are sending?” “How do the telematic providers we are connected to compare in terms of coverage, frequency of positions sent, latency?” etc... Our INSIGHTS offering will grow during months to come with in-depth analysis of the performance per lane
- Algorithms: We also build algorithms to add a "predictive layer" on top of the "real-time layer" of the Shippeo platform. We are given some data (order, itinerary, position data...), from which extract a lot of parameters in order to generate more parameters by transforming the raw data to predict an ETA for each stop of the itinerary, using Machine Learning methods
- Decision support tools: We develop tools to provide users with insights contained in the data to allow for informed decision-making. For example: we are about to release a tool called RE-GEOCODE, which detects addresses that are not properly geocoded (because of typos contained in the address details, missing digits in the postal code, etc), and suggests to the user alternative locations which are more likely to be the correct ones, based on historical tracked trips.