Data debt detox: Cleansing logistics operations to boost revenue

AI, machine learning and quantum computing are emerging as the breakthrough technologies that can help freight forwarders and airlines boost on-time performance and profitability. However, to maximize ROI from these technologies, companies must address a critical issue: data debt.

Outdated systems and processes often contain data that could hinder your technological transformation efforts, known as data debt. This occurs when your business evolves without adapting your data practices. For freight forwarders and air cargo operators, dark data could be lingering in your various systems, such as transportation management, tracking, warehouse management and communication channels.

To overcome this obstacle, you must understand how data debt impedes the integration of emerging technologies and learn how to conquer this challenge.

Understanding data debt

Data debt is the accumulation of data-related issues over time — ranging from data quality and categorization issues to security concerns. Data debt builds when there’s a lack of reliable and repeatable data governance and management processes across various sources, including manual data entry errors, incomplete or inconsistent data records and outdated systems.

As operations expand and evolve, the volume and complexity of data increase, leading to the accumulation of dark data and exacerbating data debt. In other words, data debt can wreak havoc on your entire process, hurting operational efficiency and decision-making.

Data debt and missed revenue opportunities

Inaccurate or incomplete data can lead to delays, errors and inefficiencies in shipment management, resulting in missed deadlines, increased costs and dissatisfied customers. Even more critically, data quality issues can directly result in missed revenue opportunities for freight forwarders and airlines, making it one of the costliest challenges stemming from data debt.

Data debt can severely limit your visibility into key performance metrics, customer needs, market trends and other critical information. Without a clear line of sight into this intelligence, you could miss out on chances to win over new accounts, cross-sell or up-sell additional services, expand into new regions or verticals, and ultimately boost revenue.

Don’t let data debt rob you of potential revenue. Here’s how you can cleanse your data and get your organization’s data back on track.

How to start your data detox

By addressing data debt head-on and implementing proactive strategies to enhance data quality and management practices, freight forwarders and airlines can unlock the full potential of optimization technologies. It begins by wiping your data slate clean:

1. Understand your current state

Before making any changes, understand your current data landscape:

• Create an inventory of all data sources, systems and applications that generate, store or process data.
• Document the data formats, structures and schemas used.
• Identify the storage locations, databases and repositories.

2. Uncover dark data sources

Identifying dark data sources is critical to understanding your data landscape. Dark data is typically generated through various sources, including customer interactions, daily operations, sensor data and transaction records. However, it is undocumented, may even exist in someone's head, and cannot be used. To uncover dark data sources:

• Interview and survey frontline employees to identify undocumented processes, exceptions and workarounds.
• Analyze system logs, error reports and support tickets to uncover issues, exceptions and deviations from processes.
• Deploy sensors, IoT devices or wearables to capture data from physical processes and operations that are not currently documented.

3. Clean up your data

Next, start making changes by removing duplicate, outdated or inaccurate information from your systems and operations. To effectively clean up your data:

• Establish data validation rules and checks to ensure incoming data meets quality standards and adheres to defined formats and constraints.
• Perform data cleansing and scrubbing to correct or remove incorrect, incomplete or inconsistent data values.
• Set up data archiving and purging protocols to remove outdated or unnecessary data from your active systems.

4. Establish governance policies

Once you’ve cleaned up your data, the next steps will help ensure you never backtrack. Establish robust data governance policies and processes:

• Assign data owners and stewards accountable for maintaining data quality and enforcing policies.
• Establish data access controls and security measures to protect sensitive or confidential data.
• Implement data quality monitoring and reporting processes to assess your data's accuracy, completeness and consistency continuously.

Having addressed your data debt, you can now explore adding new technologies and tools to your operational toolkit. Consider how AI can optimize your pallet-building process or how advanced analytics and reporting capabilities can help you measure carrier performance to find common root causes for delays.

Pave the way to next-generation logistics management

Data debt shouldn't be a roadblock to enhancing your operations with emerging technologies. To embrace next-gen capabilities, it’s crucial to first streamline your data management processes and foster a culture of data-driven decision-making.

To learn more about best practices to implement emerging technologies into your air cargo operations, download this free guide to learn more or contact us today.