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How To Optimize Data Analytics in Logistics With Data Cleansing


Cover photo for an article about Improving Data Analytics in Logistics with Data Cleansing, supported by the illustration of a woman showing logistics dashgboards on the screen of a computer

In one of our previous articles, we discussed the importance of centralized systems and storage methods to help capture more data and increase insight-gathering opportunities. Today let’s look at logistics information shared across your network and how these strategies can help you get the most out of your data.

Perhaps you have a retired employee as your delivery point of contact or an error in the destination’s zipcode. Without thorough data cleansing and validation, these discrepancies can delay orders and potentially cost logistics providers thousands of dollars.

Data cleansing is the method of correcting data based on its quality. Clean data means fewer errors, optimizing data analytics in logistics with accurate and reliable information. It can help to improve customer communications, fulfillment and delivery accuracy, speed, and experience. This way, logistics teams can maximize customer revenue and minimize fraud potential. 

So, how can professionals clean their data to increase efficiency and leverage data analytics in logistics to the fullest? Here are our top tips. 

Gather Data Silos in a Centralized Place

In 2023, Statista forecasted the total amount of global data at a whopping 120 zettabytes, up from 9 zettabytes ten years prior—and it continues to rise. This rapid growth leaves logistics professionals with the tricky task of updating their databases and operations to store and process data more quickly. As a result, only 2% of this data is currently used. 

With data stored in multiple locations, from data lakes and warehouses to internal databases, and external data sources, multiple data silos and unmapped systems prevent data visibility. 

Logistics companies must gather data in a centralized location to begin their cleansing process. This will help capture updates lost in data silos, improving the visibility and accuracy of information to drive reliable data-driven decision-making

Build a Query and Extraction Tool To Pull the Data

Let’s say you’re a container shipping company that needs to extract scans or events to identify when its containers are in a terminal, on a vessel, unloaded from a ship, or on a truck for last-mile delivery. 

You know that GPS track and trace system data and the vessel schedule from the vessel company will be helpful. But large sums of external data contain lots of unnecessary information, which can slow systems down. 

The extract and query tool can support your big data analytics tools by only pulling data based on your requirements. Think of a fire engine pole—a connection line without any bottlenecks. You must structure data to extract easily and flow seamlessly for quick use. Logistics providers can use data aggregations and filters to reduce the processing time by telling the extraction tools precisely what data they need.

But what size are the pallets to go to the retailer? Do you have this data stored from a previous sale? Have they changed their product? Logistics providers must understand all required data points and implement IT infrastructure to extract and reformat critical information for their tools.

Start Cleansing and Enriching Your Data!

Data cleansing opens many doors for data analytics in logistics, such as demand forecasting and operational optimization. With clean historical data, logistics professionals can look back and analyze patterns between customer behavior, weather, seasonality, and supply to forecast demand. However, data cleansing can be the most laborious part of the data lifecycle, so here are some steps to make it more manageable:

  • Ensure all data is in a consistent format: Does your software read special characters? Is it case-sensitive? As a rule of thumb, it’s best to convert all upper case to lower case and replace special characters with an underscore to increase readability across the network. 
  • Remove duplicates: Data often has repeated columns and rows that need to be filtered out. These may arise, for example, from two systems capturing data from the same truck or the vehicle itself having multiple issues related to the same part, generating a similar response in many of the data entries.
  • Check for syntax errors: Date, birthday, and age are common syntax issues that are simple enough to fix, but problems involving spelling mistakes require more effort. Although removing typos, additional spaces, and re-ordering dates require particular equations and tools, data teams can prevent some issues altogether by structuring the data collection format. Setting strict boundaries for fields like driver name, location, and truck numbers will help minimize data cleansing tasks and ensure quality data.
  • Estimate missing data points: When multiple data points have missing data for the same attributes, the most straightforward response is dropping entire columns, reducing data representability. Nevertheless, industry experts can solve these issues with historical data and algorithms. 

Once data is cleaned and enriched, logistics service providers can begin to implement data analytics in the logistics networks much more efficiently. This way, they increase their ability to make data-driven decisions and avoid expensive data-led mistakes. On top of that, it opens many doors for further logistics technology implementations.