Having the right data on hand is merely one step in a larger process that puts it to work. Whether you use your data effectively and to its full potential comes down to the details. 

At Reveel, data is at the heart of everything we do. From the company’s early days in 2006, when a data-based approach first informed our consulting efforts, to today when our experts fine-tune customers’ parcel operations and our platform empowers shippers and leaders in finance and operations to proactively lower their costs and streamline their businesses in real time, we have always focused on making sure that the right data is used at the right time.

Shippers often ask me if there are steps they can take to make their data more usable—not just for fulfillment and warehouse operations, but across the business for pricing strategies, accounting, and expansion efforts. The data science we apply is often complex and unique to every organization, but several practices are broadly applicable to all shippers.

Normalize Your Shipping Data

One of the key findings in our “2025 Parcel Shipping Intelligence Market Survey Report,” last year was that a vast majority of shippers, 91%, expect to expand their carrier networks to help reduce parcel shipping costs. A multi-carrier approach has many benefits, including more inherent resiliency and the ability to match the right parcels with the right carrier, but it also requires shippers to have a much better handle on their data. This includes ensuring that they are able to do everything from monitoring agreed-on volume tiers to comparing carriers’ costs quickly and easily in an apples-to-apples fashion.

Importantly, each carrier has its own vernacular for the same charges. For example, FedEx says Standard Overnight, but UPS says Next Day Saver. Yes, there are nuances, but we’re essentially talking about the same services with different names. This disparity occurs across numerous data elements within each data source and makes translating data sources into a common normalized language paramount. 

It’s vastly easier to analyze costs between carriers when the entire dataset adheres to the same format for important parameters like service, zone, currency, units of measurement for weight, units of measurement for dimensions, etc.

Speak Your Own Language

It is also imperative to add customized elements to your data that reflect your organization’s terminology. This allows data to be aggregated in ways that are familiar not only to the shipping operation, but also to business functions and departments that can benefit from shipping intelligence. The addition of custom data elements like account groups, business units, location names, location types, fiscal dates, and other parameters can immediately make data much more useful. It is much easier to report and apply data internally if it mimics the terminology used by everyone.

Check out the difference:

  • Unmodified: Next Day spend is elevated on account #s 123456 and 456789 during the week of October 15th.
  • In your Organization’s Language: Expedited spend is elevated for the Furniture Group , impacting Outbound Shipments at the Reno DC during Fiscal Week 45 because of new delivery area surcharges for the following zip codes...

Combine Data Sources 

Carrier data is sterile and purposely limited to include only the basic data elements a carrier must provide to get paid. It is also very transportation-specific and fails to touch on the many ways that shipping practices impact the business’s operations and finances. By creating true hybrid datasets, you can combine carrier invoice, TMS, OMS, WMS, and rate shop data to obtain a far more granular view into shipping performance and costs. Previously, you only had the service, weight, location, and cost for each shipment. With a combined dataset, you can append elements like order number, retail price, unit cost, customer promised-by date, and shipping costs paid by the customer.

This unlocks powerful analyses: accrual reporting, customer experience metrics, shipping revenue vs. shipping cost, rate shop accuracy, and carrier performance comparisons. It also puts SKU-level profitability metrics at your fingertips—enabling you to spot anomalies like an entire product line shipped at a loss because of a carrier’s definitional change.

Match the Granularity of Your Shipping Data

Every data source is structured differently. Before analyzing data, it is critical to know what level of granularity your data is at. In its most basic sense, this means understanding what each line of data in your database represents. I often see examples where shippers inadvertently fail to maintain the granularity of their data source in a consistent manner. 

For example, let’s say UPS issues a charge for a given shipment, then two weeks later issues a shipping charge correction for the same shipment. Now, you have two lines in your data set that reflect only one shipment. By not accounting for nuances like this in your data, it is easy to significantly compromise the integrity of your analyses. This is especially important when combining data sources. You must choose a master level of granularity and stick to it across your combined data source.

Granularity varies by source:

  • FedEx Data: Each line of data is a shipment
  • UPS Data: Each line of data is a unique charge on a unique shipment (One shipment may have four lines of data)
  • OMS Data: Each line of data may represent one order, or maybe one SKU. (One order may be split across multiple shipments, or multiple orders may be included in the same shipment.)

When combining data sources, choose a master level of granularity and stick to it.

Put Your Shipping Data To Work

By keeping these points in mind, shippers can unlock the actionable insights within their parcel shipping data—insights that lower costs, strengthen customer relationships, mitigate risks, and drive profits. In a time when reliable fulfillment and effective shipping performance matter more than ever, the details of how you manage your data have never been more important.Ready to get more from your parcel shipping data? Book a demo to see how Reveel can help you normalize, combine, and act on your shipping data in real time.