The 2024 Data Guide for eCommerce Logistics / Supply Chain Leaders

Author headshot
Arthur Wu
Co-founder, Dataland

When it comes to scaling eCommerce logistics, data plays a central role. It's not just about tracking shipments or inventory levels. The real challenge is building data infrastructure and tooling that evolves with your team's needs and the complexities of modern supply chains.

In this guide, we'll cover:

This post is not about metrics or KPIs to memorize. It's about how to benchmark your logistics team's data maturity and strategically set up your data landscape for scaling operations over the next 1–3 years—from the perspectives of leaders who have navigated these challenges firsthand.

First, Why Is Data So Challenging in eCommerce Logistics?

Logistics + supply chain in eCommerce is a complex ecosystem involving multiple stakeholders: carriers, third-party logistics providers (3PLs), warehouses, suppliers, and customers. Each entity generates its own data, often in disparate systems and formats. Consolidating this data into something your team can actually use is frustrating to say the least:

Key Challenges:

These challenges make it difficult to gain a holistic view of operations, leading to inefficiencies and missed opportunities.

Stage 1: Recognizing the Data Silos

Diagram showing Stage 1: Data Silos with disconnected systems

At this stage, data is scattered across various platforms—WMS, ERP systems, carrier portals, CRM systems, and customer service tools. Teams spend excessive time gathering data from different sources, leading to inefficiencies and errors. Here’s what this looks like in practice:

When to evolve:

How to Approach This: Creating a Centralized Data Model

The solution lies in building a centralized data model that represents the key entities in your supply chain: purchase orders, shipments, inventory levels, and more. This model should also integrate customer helpdesk tickets and CRM information to provide a holistic view of your operations and customer experience.

A holistic data model that spans your supply chain and customer data then enables you to ask powerful questions like: “how many high-value (defined by customer LTV) customers experienced SLA violations due to 3PL unreliability?” or “what’s the root cause of a higher return rate for a certain product SKU from a vendor in the past month, based on customer helpdesk data?”

Steps to Build a Centralized Data Model:

The key step here is to identify or calculate common attributes across the points of variation. Let's use shipment tracking as a practical example. Each system (1P WMS, Shipbob, Amazon FBA) tracks shipments differently, but they share common core data elements. The key is identifying both unique and shared attributes:

Diagram showing Stage 2: Centralized Data Model with shared and unique attributes

Shared Elements (In Green):

Unique Elements (In Red):

By mapping these variations to a standardized structure, you create a master shipments table that maintains the essential shared data while accommodating source-specific attributes. This enables unified tracking and reporting regardless of where the shipment originated.

This standardization approach should be repeated for each core entity in your supply chain - whether it's inventory levels, purchase orders, or customer data. The goal is to create a "single source of truth" that all teams can rely on while preserving the granular details needed for specific operations.

Here’s a list of master tables that you could consider for your supply chain + customer experience:

How to build standardized master tables:

The best way to approach this challenge is to use data engineering techniques, such that your data lives in SQL - the lingua franca of data. Chances are, key datasets you may need from the customer experience or finance/accounting side are already going to be stored in SQL as well.

  1. Sync data from source systems: Depending on your sources, you may need technical staff to build a custom data connector to regularly sync data from an ERP or WMS into SQL.
  2. Data warehousing: The destination for data syncs land in a database or data warehouse, like Snowflake, BigQuery, Postgres, or MySQL.
  3. Data cleaning / processing: This is where you standardize tables into a common structure. You can use tools here like dbt, Dagster, or Airflow to write SQL transformations.

If this sounds overwhelming because you don’t have a technical resource on-hand – Dataland.io does all three steps for you in one platform, and comes with white-glove implementation to build the connectors to make sure that you’re able to run those key analyses we mention later in this guide.

By consolidating these data sources, you create a single source of truth that enhances visibility across the entire supply chain.

Stage 2: Operationalizing the Value of Centralized Data

With a centralized data model in place, the next step is to operationalize this data to drive insights and action.

Diagram showing Stage 2: Operationalizing the Value of Centralized Data

There are three key ways for how to operationalize the data model you’ve built.

KPI reporting & dashboarding

There’s a temptation to overload on every possible metric here – this is the wrong approach. If every metric is important, then nothing is. To focus your dashboards, ask yourself:

Example:

There are tons of dashboarding + business intelligence tools that allow you to configure reports like this, like Dataland.io, Metabase, Tableau, PowerBI, Looker and more. Dashboards are just part of the equation – once you notice a concerning spike or dip in a metric, your team will need to be good at ad-hoc investigations, which leads into our next topic.

Ad-hoc investigations

Ad-hoc investigations, operations fire-fighting, reacting to global supply chain events – this is the actual day-to-day of a logistics/supply chain team, and the critical source for operational improvements that you can make to your supply chain. Here are some examples:

  1. Customer complaints and order cycle time: For orders that took 7 days to ship - did that result in more customer complaints requesting refunds?
  2. Return rates and carriers: For SKUs with a return rate of >5% in November 2024, what carriers did we use?
  3. Delivery delays to a specific region in a specific timeframe (i.e. due to port strikes or inclement weather): Why did orders take so long to get to Florida in the past two weeks?
  4. Spike in COGS: Why did our COGS increase so much for this time period? Can we look at the wholesale prices?

Most data tools aren’t built for investigative work; they’re great for the dashboarding case, but often lack the speed, flexibility, and search / AI features that help an operator iteratively test a bunch of hypotheses and do a root cause analysis. This is precisely why we’ve built Dataland.io.

As a litmus test - if you can’t quickly take a list of delayed / undeliverable order IDs and jump to a list of customers + their locations within a few seconds – then your data tools are likely holding your team back from actually driving the operational improvements needed to scale your supply chain.

Alerts and notifications

By centralizing data, your team can now define alerts that take into account the holistic customer experience and your supply chain. Here are some example alerts that become possible:

  1. Alert #cx and #operations if a perishable SKU heading to Florida gets ordered in the next 2 weeks
  2. Alert [email protected] if VIP customer (as defined by customer lifetime value) complains about "wrong item" in a Zendesk ticket
  3. If forecasted product sales rate exceeds re-order points for certain SKUs with long lead times

The key piece is making sure that the alert is easy to configure without knowing code, and fits into your teams existing workflows (like sending an alert to an email where you can use a project management tool like Asana/Jira), or to a Slack team channel where you can thread responses during your root cause analysis. Tools like Dataland.io allows supply chain operators to do this on real-time data.

Stage 3: Reaching Data Maturity

Most advice about data maturity reads like weird consulting speak. The reality is simpler: you've reached data maturity when your team stops talking about data and just uses it.

The telltale signs:

  1. Junior ops people routinely answer complex questions that used to require a data scientist
  2. Your team catches problems before customers complain
  3. Nobody argues about whose numbers are right anymore

But data maturity is a local maximum, not a destination. The most dangerous thing you can do is think you've "solved" data. I've noticed that companies usually regress in one of three ways:

First, they optimize for convenience. Someone adds a new warehouse management system that "doesn't need to be integrated because we'll barely use it." Six months later, it's critical infrastructure, and you're back to data silos.

Second, they mistake dashboards for understanding. Teams get comfortable looking at the same charts every day, until something unprecedented happens and no one knows how to investigate it.

Third, they stop evolving their data model. Supply chains change constantly - your data model needs to keep up. If you're still using the same schema you built two years ago, you're probably missing important nuances.

The companies that maintain true data maturity aren't the ones with perfect systems - they're the ones who remain painfully aware of their blind spots. They treat their data infrastructure like a product that needs constant iteration.

The best logistics teams I know have weekly rituals where they pick apart recent incidents, asking "what data would have helped us catch this sooner?" Then they actually build it - where tools like Dataland.io can help.

In other words, data maturity isn't about having perfect data. It's about having a team that's habitually curious and uncommonly honest about what they don't know.

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