Point of viewTrade lanes & visibility

From fragmentation to global intelligence: what a logistics data lake changes

Global forwarders run dozens of systems and hundreds of reports, and still lack a management view of yield by lane. A delivered transformation at a global LCL consolidator shows what happens when the data is unified first and the dashboards, sales app and shipper platform are built on top.

7 min read By · Point of view
20–40%
sales growth across 20+ countries at a global LCL consolidator after its data was unified1

Key takeaways

  • Hundreds of operational reports are not a management view; a standard KPI layer is.
  • Unifying the systems first is what lets every later product — dashboards, a sales app, a shipper platform — agree on the numbers.
  • The payoff showed up in the commercial line: sales growth, sales productivity and customer adoption.

A global less-than-container-load consolidator does thousands of small things a day: quotes, bookings, consolidations, container moves, invoices. Each of them lives in a system built for one region or one product line. The consolidator in our case study ran three product lines across six data centres, with forty non-standardised sources and more than two hundred operational reports — and still had no single view of how each trade lane was performing.

Why reports multiply and answers don't

Every region and every function builds the report it needs from the system it has. The definitions drift: a TEU in one report is not a TEU in another, revenue is booked on a different date, a customer has three names. Leaders end up comparing numbers that were never meant to be compared, and the conversation turns to whose number is right.

  • Siloed data and inconsistent performance across regions
  • Manual tracking and delayed reporting
  • Low digital adoption by sales teams and customers
  • No management-level view across the network

Unify first, then build

The engagement started with the data, not the dashboards: more than thirty systems unified into one central data lake, cleansed and quality-checked across global operations, with one KPI layer on top. Only then were the hundred-plus live dashboards built — for container freight stations, trade-lane yield, live containers, and sales and operations — and an AI-powered sales app and a customer platform for shipment visibility fed from the same data.

Exhibit 1

What changed

Delivered results at a global LCL consolidator

MeasureResult
Sales growth20–40% across 20+ countries
Processing time30% reduction through centralised data and automated reporting
Workforce productivity150% boost with an AI-powered sales app
Digital adoption25% rise in customer adoption of the shipper platform
Dashboards100+ interactive Power BI dashboards

Note: Client results from the delivered engagement.

Why the payoff was commercial

Unified data is usually justified as an IT efficiency. Here the largest gains were commercial: salespeople walked into meetings with lane, customer and quote history on their phones, and shippers could see their own bookings without calling. Both depended on the same governed numbers the leadership team used.

What three consulting firms couldn't solve in two years, SCIKIQ delivered in three months. Complete game-changer. — CIO, global LCL consolidator

The engagement was delivered on the SCIKIQ platform, working alongside the client's strategy adviser as the execution partner.

For executives

What this means for your bank

  1. Count your reports and your definitions before you count your dashboards.
  2. Put the KPI layer in place before building apps on top of it.
  3. Measure the commercial outcome — sales, productivity, adoption — not only the IT one.
Put it to work

How DaasLabs can help

Unify booking, operations, terminal and finance systems in our Data Engineering service.

Learn more

Turn yield, CFS and container views into trade-lane decisions with Freight Forwarding, NVOCC & Trade Compliance.

Learn more

Read the full case study.

Learn more

Sources

  1. 1

Figures are drawn from the cited public sources. Opinions labelled “DaasLabs point of view” are our own.

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