Transportation | Atlanta, GA

Ascend Transportation reduces manual data wrangling by 700% as they become a data-driven organization

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  • 2% Profitability Increase

    Increase in visibility across acquisition data

  • 100% Increase in Data Warehouse Usage

    Deployed Enterprise Transportation Data Warehouse Model and Analytics

  • 800 Man Hours per Year Saved

    Reduction in manual daily data compilation efforts

Systems of Record: Ceridian Dayforce, TenStreet, McLeod Software, Workhound
Targets/Destinations: Snowflake, Idelic

The Challenge

In the journey of Ascend Transportation’s data-driven transformation, a strategic decision to reinforce a robust data culture and infrastructure became evident with the introduction of key hires. The goal was clear: establish a foundation that would facilitate self-service analytics, provide consistent responses to known queries, and create a seamless data pipeline to accommodate future acquisition data and operational systems. However, this structure was merely a vision on the horizon, not yet a reality.

Adding to the complexity, Ascend Transportation brought on board a lone data scientist tasked with crafting fundamental Key Performance Indicators (KPIs) related to metrics like rate per mile and identifying seated and unseated trucks. Unfortunately, this talented individual found themselves ensnared in a cycle of writing and rewriting logic, leaving decision-makers, including the COO and CFO, waiting longer than desired for basic insights. The appetite for data was growing, even if it meant receiving information in cumbersome Excel spreadsheets.

In the transportation industry, there’s a plethora of specialized software systems tailored for load management, warehousing, and acting as the Enterprise Resource Planning (ERP) system for organizations. Following several acquisitions, a unified view of all company data emerged as a critical need to efficiently steer operations. Connecting these disparate systems and consolidating them into a single, reliable source of truth became paramount for initiating forecasting and other essential operational analyses.

Lastly, a prevalent practice within the organization involved manual data wrangling. Despite serving as the basis for daily reports, this process relied heavily on one individual waking up at 4 am every morning to compile various spreadsheets and system outputs into a comprehensive report distributed via email by 8 am. This represented a staggering four hours each day, five days a week—an invaluable resource’s time that could have been redirected toward higher-value initiatives harnessing their expertise and domain knowledge.

The Solution & Outcome

Teaming up with marked a significant milestone for our data infrastructure. Through the integration of data pipelines from McLeod Software (TMS) and Ceridian Dayforce, we unlocked the seamless flow of data into our Snowflake data cloud.

In tandem with these efforts, our dedicated development team embarked on the deployment of data warehouse models, making reporting with Tableau a straightforward process. This newfound capability not only equipped the office of the CFO and CEO with initial data insights but also laid the foundation for a holistic, data-driven transformation, starting from the top and extending throughout the organization.

As word spread about our improved data pipelines and near real-time analytics capabilities, requests from various departments began pouring in. Systems like TenStreet and Idelic were seamlessly integrated with’s SQL Query to target workflow pipelines. This integration empowered us to maintain secure connections with our transportation operations systems (TMS’s), facilitating timely updates to driver records, tracking status changes, assisting in attrition management, and offering valuable insights for accident reduction.

Furthermore, we established additional data models to replicate the daily morning report that had previously relied on the dedication of a single resource. This transition not only freed up hundreds of man-hours annually but also allowed for a much-needed focus on higher-value objectives and, perhaps, a better night’s sleep for our dedicated team members.

“With any HRIS system comes a great responsibility to ensure the sensitive data is only used to address proper operational business needs. gives us the ability to control who gets what Ceridian data and for me to know the HR controls this data access even when the data is replicated to our Snowflake data warehouse.”

Kaitlyn DaSilva
HRIS Manager, Barrett Distribution Centers

Data Insights for Continued Excellence in Operations

Achieving efficiency in critical areas like load management, rate per mile analysis, and driver attribution is an ongoing journey, and it’s one that is greatly facilitated by robust data workflows. These workflows provide the foundation for gaining insights that continuously empower operators to challenge and refine their existing models. This iterative process leads to the generation of even more valuable insights, often necessitating additional data enrichment and integrations—precisely what excels at.

The deployment of a modern data stack, with the support of, equips organizations with a suite of indispensable tools. These tools enable the seamless integration of multiple systems, culminating in a unified source of truth. This unified source is a goldmine for analyzing driver performance insights, optimizing quote-to-cash processes, and leveraging CRM data for customer segmentation and deal flow enhancement.

“ accelerated our ability to start building data models on day 1 of the project using the Ceridian data. No other tool in the market was able to bring our Ceridian Dayforce data into Snowflake and to deliver the visibility we needed to make data driven staffing decisions.”

Jordan Johnsen,
Manager, Data & Analytics, Barrett Distribution Centers

A key transformation brought about by this modernization effort is the replacement of manual processes with automated data pipelines and reporting mechanisms. This shift not only elevates operational efficiency but is also underpinned by a robust data security strategy. Additionally, a DevOps approach to delivery ensures the continuous enhancement and reliability of these data-driven workflows, supporting the organization’s quest for excellence.

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