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Writer's pictureBrian Armieri

Data Quality: How 3PLs Can Build More Trustworthy Reports



Rarely do we come across a freight brokerage that is doing regular data quality checks. In fact, most logistics providers are still getting a handle on data management best practices. (That is, if they’ve made their way out of Excel and into business intelligence.) We suspect, though, that data quality isn’t prioritized nearly as high as it should be.

 

According to a study by Gartner, organizations that prioritize data quality management see an average 40% increase in operational efficiency and a 30% reduction in costs. This underscores the importance of investing in data management best practices to drive tangible business benefits.

 

Now, take a step back and think about what a 40% increase in operational efficiency would look like for your own business. It could mean that you trust automation to match loads to carriers. It could mean that you’re able to respond to potential jobs a whole lot faster. It could mean that your routes are truly optimal. It could even mean that you go from “in the red” to “back in black.”

 

None of this happens with poor data quality.


Why Data Quality Matters for Freight Brokers


Accurate and reliable data are fundamental to making informed and effective decisions. When data quality issues arise, they can lead to inefficiencies, increased operational costs, and even disruptions. In the context of shipment data, poor data quality can severely compromise decision-making capabilities that impact your margins, so it’s crucial to implement processes and generate reports that monitor and maintain data quality.


All of these factors ultimately impact customer satisfaction. Think about it: you have to win over shippers year after year. You have to answer their tough questions and make sure they leave every QBR with a smile on their face.


Decision Making


Freight brokerage moves fast and decisions need to be made swiftly and accurately. Reliable data ensures that brokers have the necessary information to negotiate rates, optimize routes, and allocate resources effectively. Inaccurate forecasting and demand planning can cause brokers to miss out on lucrative opportunities, such as securing preferred rates or optimizing capacity utilization.

 

Operational Efficiency


Efficient operations are the backbone of any successful brokerage. Poor data quality can lead to inefficiencies such as redundant processes, inaccurate reporting, and increased operational costs. Imagine a scenario where outdated or inaccurate shipment data leads to assigning the wrong carrier for a particular route. This could result in delays, increased costs, and unhappy customers due to missed deadlines.


Customer Satisfaction


Timely delivery and transparency are key factors in maintaining customer satisfaction. High-quality data enables brokers to provide real-time tracking updates, accurate ETAs, and proactive problem-solving. All of this enhances the customer experience. Incorrect or incomplete data can lead to billing discrepancies between the broker, carrier, and shipper. Resolving these discrepancies not only consumes valuable time but also strains relationships with clients and carriers.


Best Practices for Maintaining Data Quality


Over time, the quality of system data often deteriorates due to a variety of factors, such as changes in employees responsible for data entry or even data volume and complexity. This decline, known as data quality drift, can significantly impact the integrity and reliability of data within an organization.


At the organizational level, adhering to best practices for data quality can substantially mitigate issues.


Here are a few best practices that every brokerage should follow:


Awareness & Training


  • Employee Education: Ensure that all personnel who handle data are trained to recognize the importance of data accuracy. While it is impossible to eliminate manual data entry errors entirely, emphasizing the value of consistency and precision can significantly reduce these errors.


  • Incentivize Accuracy: Implement reward systems to encourage employees to maintain high standards of data quality.


Investing in Data Infrastructure


  • Data Standardization: Develop and deploy a robust data standardization framework to ensure that data is consistently accurate and reliable across the organization. (Axiom-One’s data infrastructure includes pipelines that cleanses and normalizes your data.)


  • Data Mapping: It’s important that whomever you employ to map your data from one system to another understands the nuances of transportation and logistics.


Regular Data Quality Inspections


  • Quality Checks & Audits: Conduct regular data quality inspections to proactively identify and address issues. This practice helps to minimize the impact of data quality problems on operations and decision-making.


  • Continuous Improvement: Use the insights gained from these inspections to continuously improve data handling processes and infrastructure.


Leveraging AI to Improve Data Quality

 

It’s not news to anyone reading this that AI is making its way into transportation and logistics providers everywhere are figuring out how to adopt it. Data quality is one of the many areas that AI is improving for freight brokers.

 

Take Third Axiom’s Shipment Analyzer, for example. We’ve built in a few safeguards to ensure that you receive a trustworthy report on the other side of your data. One of those safeguards is the identification and removal of what we call extreme outliers – values in a set of measurements that are much bigger or smaller than the rest. Not only do we remove extreme outliers, but we also call out what we’ve removed. This adds a layer of trust to the report that isn’t found in a typical analysis.




 

Creating Your Mission Control for Data Quality


The first step in monitoring and maintaining data quality is to move away from spreadsheets and manual data analysis and into a modern data stack. There are many components of data infrastructure that you need to consider.


To give you an idea of what to look for, I’ll leave you with this diagram of Third Axiom’s end-to-end solution.




Before any analyzing happens, you need to ensure the proper infrastructure is in place to cleanse and normalize your data, so you and your customers trust the analysis enough to confidently act on it. 

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