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In this week's blog, we've 'assembled' the key-takeaways from our The Data For Good Podcast conversation with Data Engineering Manager, Mamdouh Tawfik. His domain of expertise sits not only in data engineering but on the challenges and solutions of Logistics and Customer Care data.
Data engineering for logistics is a topic that's crucial yet often misunderstood in its depth and breadth. Climb aboard and let's explore how these digital threads weave together freight, warehousing, and customer care into a seamless tapestry of operations. We'll explore how data teams organize, prioritize, and optimize in real-time to meet business demands.
The Integrated Approach to Data Engineering in Logistics
In logistics, the role of data engineering is akin to being at the command centre, where every decision can set ripples across a vast ocean of operations. At Zalando, the data team is laser-focused on solving problems related to logistics and customer care. With over 51.2 million customers surveyed last year, the sheer volume of data involved in such segments at a company like Zalando is staggering. This necessitates a robust strategy to not just handle data, but to make it actionable and beneficial for the entire logistical chain.
Moving from an embedded to a centralized business unit within logistics and customer care has been a strategic move. This allows the team to serve more effectively while drawing from a shared knowledge base. However, this approach can still maintain a focus on specific business areas without getting diluted, reflecting what could be termed a 'hybrid model'.
"Our priorities come from the business units we serve, not the unit we report to, making our operations straightforward and focused."
In an environment where accuracy, timing, and coordination are critical, the data team doesn’t interact directly with end-consumer customers but rather with internal customers and stakeholders. They focus on ensuring that the logistical backbone is operated at peak efficiency, thus ensuring that when a customer places an order, it’s processed and delivered in the most efficient manner possible.
Empathy and Engineering: Seeing Through the Customer's Lens
At the heart of impactful data engineering is empathy—understanding the customer's journey from placing an order to receiving a parcel. This requires that data engineers don’t just handle data with a distant, detached mindset, but rather immerse themselves in the customer experience. The advice given is simple yet critical—be a customer yourself. Use the platform, experience the shopping process, and essentially walk through the customer journey. This not only hones an engineer’s sense of relevance but also fills those “aha” moments when the data they handle starts making real-world sense.
Making frequent visits to the warehouses is a practice that offers invaluable insights into how data originates and where it's most impactful. Witnessing warehouse operations provides clarity and understanding of the logistical complexities—what can be a daunting maze on a spreadsheet is often a straightforward process in the real world.
"Visit the warehouse, see how things are working, what process flows do you see, how are you able to connect the dots."
Going the extra mile, even checking customer feedback on social platforms, though not mandatory, can be a source of additional insights into customer satisfaction and areas needing improvement. This holistic approach is key in ensuring that the data collected doesn’t just sit in silos but drives meaningful improvements across the board.
Facing and Overcoming Challenges in Logistics Data Engineering
The hurdles in logistics data engineering are monumental. From managing the intense flow of requests to tackling the intricate processes involved in logistics, each challenge requires a strategic approach. One primary challenge faced by the team was prioritization. With the pressure to manage a countless number of data-related requests, the strategy shifted towards letting business units decide what was most pressing. This democratized digitization of priorities ensured that the solutions developed were truly aligned with business needs.
Another daunting challenge is simplifying the complex, multi-layered process landscape inherent to logistics. It is indeed a fine art to take something that seems so messy and convert it into a structured narrative that data engineers can use to build pipelines and models. Documentation is key—without it, knowledge gets trapped in heads or scattered across emails.
"Document as you go; it's critical to keep sharing knowledge within the team so it's accessible to all."
Engineers are encouraged to engage with and understand the processes deeply. This minimizes the chance for what has been built to become obsolete as processes inevitably evolve. Appropriate prioritization and breaking down complexity into simple, tangible tasks are how data engineering manages to keep the ship steady even in stormy seas.
Ultimately, data engineering in logistics is a blend of technical prowess, strategic alignment, and a deep understanding of customer needs. Those who can master this balancing act will not only drive operational excellence but also contribute to a smoother and more efficient supply chain for businesses and customers alike.
What’s the biggest ‘aha!’ moment you’ve had while working on your data pipelines?
Let us know what other data topics we should cover on The Data Blog or The Data For Good Podcast. Reach out to Matt@zuma-recruitment.com or Joseph@zuma-recruitment.com, or check us out at zuma-recruitment.com.
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