The Challenge

One of the main focus areas in their digital transformation journey was the introduction of a Microsoft Azure data platform, modernising their data warehouse to boost efficiency and improve data accessibility company-wide.

They quickly noticed increasing pressure on their operational data stores. After evaluating the data pipelines, the team recognised the need to accelerate data flow into the lake, ensuring a quality user experience with no performance lag. As a result, they decided a real-time and automated pipelines would be the best solution.

“The Smart Technology team also uses the data lake to troubleshoot what’s happening in production without putting pressure on the operational database,” said a member of their Engineering team. “That means the data lake needs to be as close to real-time as possible, so they can diagnose what users are experiencing, get to a solution faster, and prevent escalation.”

Business Benefit

“With Qlik feeding the Azure platform target, we’re seeing more readily available data in our system, which gives shippers and freight vehicles up-to-the-minute information on how they are performing,”said a Supply Chain leader. “The combination of real-time data and insights we’re now experiencing, is really helping advance the company’s expertise in supply chain technology.”

Since the programme's company-wide rollout, latency has been trimmed down to a matter of minutes, meaning the team is now on track for their internal KPI for data delivery. Converting slow data to fast data, Qlik and Microsoft have also proved helpful in automating logistics dynamic pricing process estate-wide, using information from freight vehicle availability and current market conditions. Similarly, Machine Learning has been implemented to automate competitive counter offers to carriers depending on available freight.

Finally, customer experience has improved, thanks to greater ease of access to data across the data marketplace, and improved application performance. This, in addition to now having the capability to adapt to shifting data architecture requirements with a platform-independent solution, will help the organisation future-proof their data lake.