OCTOBER - 202218 logisticstechoutlook.comOCTOBER - 2022 19logisticstechoutlook.comto increase exponentially. Truck telemetrics provides a strong data foundation for deep analytical research in domains such as safety analytics and predictive maintenance. Ingesting, storing, and processing the overwhelming amount of data requires modern data practices that are capable of handling these extreme data situations. Cloud platform technologies are a natural match to these increasingly challenging data sets. Data QualityIn the world of high cuisine, the best chefs insist on the very best ingredients. The world's finest sushi chefs engage in bidding wars to ensure the finest bluefin tuna for their premier quality sushi. French chefs make deals with local growers to ensure the first pick of the best produce. It is the same with data science. To ensure the highest quality results, it is important to ensure that data inputs are of the highest quality. In the world of IoT, this is a unique challenge. Sensor data is susceptible to failures in many forms. A fuel sensor could give a false reading due to a tractor unit parked on an incline. Sensors also fail when transmission devices or antennas fail or lose satellite connectivity due to obstruction. In addition, the sensors themselves, which are small electronic devices, can fail. ECM units, acting as electronic hubs, can also have issues. With all of these points of failure, it is incredibly important to have tolerance checks on data feeds to detect missing or incomplete data. These small failures might seem inconsequential in a large sea of data, and sometimes that is true. Context is important. To quote Benjamin Martin in the movie The Patriot, "Aim small, miss small." If your data science initiative involves optimization with very small tolerances, it is critically important to ensure the highest quality data. If your data science initiative only requires directionally accurate results, then small data quality considerations are potentially not worth the time and effort. Make your choices carefully and intentionally.SummaryDue to technical advances in cloud data platforms, a sustainable data science workbench can be created much more efficiently now than at any other time in the history of data analytics. Data science practitioners would be wise to become early adopters of these platform technologies to reduce the total cost of ownership of their data science portfolio and to reduce the amount of time that technical staff needs to spend simply maintaining a platform to support the work. Invest aggressively and reap the rewards. Due to technical advances in cloud data platforms, a sustainable data science workbench can be created much more efficiently now than at any other time in the history of data analytics
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