#139 Resilient Logistical Analytics
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on Wed May 17 2023 17:00:00 GMT-0700 (Pacific Daylight Time)
with Darren W Pulsipher, Adrian Kosowski,
In this episode Darren interviews the Adrian Kosowski CPO of Pathway about their unique ability to handle logistical data from the edge in DDIL environments with real-time analytics.
Keywords
#data #analytics #artificialintelligence #pathway #technology
Are you interested in learning about how digital transformation is affecting logistics? In this episode of “Embracing Digital Transformation,” special guest Adrian Kosowski, chief product officer at Pathway, discusses resilient analytics in logistics. Pathway focuses on studying real-world systems from a distributed computing perspective, and they specifically work with data in the logistics and transportation vertical. One of the biggest challenges in this field is aggregating data at scale and making sense of it, which is where machine learning analytics come into play. Kosowski also notes that logistics is a highly concentrated market, controlled by just a handful of companies, which makes even small improvements in processes incredibly valuable to the world economy. However, collecting data from the extreme edge, such as containers in the middle of the ocean, presents its own challenges, such as optimizing energy and communication for battery powered IoT devices. In summary, if you’re interested in the intersection of logistics and digital transformation, this episode provides valuable insights into the challenges and opportunities of this field.
The use of internet of things (IoT) devices in the logistics and supply chain industry can greatly benefit businesses with end-to-end visibility and improved analytics capacity. However, there are several challenges associated with the use of these devices that need to be addressed.
One major challenge is the stability and reliability of these edge devices. In case of a device failure or crash, businesses should have a way of optimizing these devices without them guessing or performing incorrectly. This is especially critical for real-time systems that are used to events coming in order.
Another challenge is the accuracy of data collected by IoT devices. Some data may be inferred, and contextual analysis may be required to interpret the meaning of a given data point and distinguish measurement issues from process issues. Inaccurate or untimely data can lead to risks in the supply chain and make it difficult to optimize the transportation network.
However, the widespread use of IoT devices can lead to end-to-end visibility and observability of processes throughout the supply chain. This can help businesses optimize their processes and become proactive in their approach. This can benefit all actors in the supply chain – from transportation and logistics providers to retailers and manufacturers.
While there is no specific group or segment that businesses should target for the adoption of IoT devices, widespread adoption and cooperation among all actors can lead to significant benefits for the global supply chain.
Pathway was developed to address the deficiencies in existing data streaming technologies and provide a tool for advanced analytics pipelines on top of data streams. One of the key features that sets Pathway apart is the ease of describing logic as if it were meant for a batch system, while also making sure it works in a real-time system with out-of-order data.
Pathway was developed with IoT data in mind, but it can also handle data from video monitoring, server performance monitoring, log monitoring, and other physical entities. This allows for the complexities of data when it comes to anomaly detection, alerting with out-of-order data, and data with time series and geospatial elements to be handled in a cloud-agnostic manner.
Another important feature of Pathway is the ability to use Python scripts for data streaming analytics in both real-time and batch modes. This means that data scientists and engineers can develop their analytics pipelines in the normal development environment they are used to, and conveniently work with the streaming system. Additionally, Pathway allows for a much larger amount of history to be considered when running computations, which is a big difference from lightweight stream processors that only handle small amounts of data.
Overall, Pathway offers a bridging solution for organizations that need to combine batch and real-time data processing and provides a way to add value to data by adding structure and extent information for downstream use in business intelligence and analytics.
Data Streaming technology is becoming increasingly important as businesses seek to make decisions faster and respond more quickly to customer needs. When you use data streaming, you can quickly detect and respond to trends, anomalies, and other important information. Pathway offers a suite of products specifically designed for working with time series data, logistics data, and data streaming. They offer examples and developer information on GitHub, as well as a vibrant community on Discord.
Data streaming is a critical technology for businesses in a wide variety of verticals. For example, it can be used in logistics to optimize routes and predict when shipments will arrive. In finance, it can help detect fraud and predict market trends before they become widely known. In transportation, it can be used to monitor the performance of engines and vehicles in real time.
If you’re interested in learning more about data streaming and how it can benefit your business, be sure to check out Pathway’s website and community www.pathway.com. With the right tools and expertise, you can leverage this powerful technology to improve your operations and stay ahead of the curve.