DigiHaul’s Head of Data, Wenjia Tang, has worked at the frontier of data science in multiple sectors throughout her career. Although this is her first foray into transport and logistics, the data challenges are often surprisingly similar. She shares her mission to disrupt the industry and shape a better future for shippers and carriers through the power of data.

Your academic background is in communication and electrical engineering, what first sparked your interest in this area?

I was enrolled in a special programme for students under 15 to study my bachelor’s and master’s degree in Communication and Electronics Engineering in China. At the time, that was the only subject available on that programme and I was the only female in a class of 30 students.

I’ve always been into maths and physics and found communication and electronics engineering fascinating and intellectually stimulating. I felt lucky to be given the chance to pursue the topic further at university.

I then came to the UK on an overseas research scholarship to study my PhD in Electrical Engineering.

Tell us about your career as a data scientist across sectors such as communications, IT and leisure.

My PhD research topic is Optimisation Algorithms and their Application in Electrical Engineering so it’s heavily involved in software and algorithm design. After I graduated I taught in the Department of Electrical Engineering at the University of Liverpool.

I then moved near London and began my consultancy journey at IBM, first as a Data Analyst then as a Data Scientist before working my way up to Senior Managing Consultant. I’ve been focusing on data science work since then, moving on to David Lloyd as Head of Data Science before joining DigiHaul earlier this year.


How has your career experience helped in your role in transport?

The beauty of my work as a consultant was that I gained valuable experience and transferable skills across different sectors. For example, I worked with a water company to understand the root causes of leakage using data analytics, and I’ve worked with big banks in the finance sector.

Best practice of data analytics is applicable across different sectors – if you have worked on other projects, sectors and industries you know that the possibilities are, what ‘good’ looks like, and how to put yourself in the client’s shoes.

A lot of the challenges that I’m currently observing at DigiHaul are quite similar: multiple data sources in varied formats, the data quality challenge, and most importantly the thinking and methodology that can be applied.


Why did you join the DigiHaul team?

I came across the role earlier this year and thought it was a fantastic opportunity because I enjoy a challenge! I like to work at the frontier of industry disruption and DigiHaul is undoubtedly one of the first disrupters in road freight business – I genuinely believe it will revolutionise the industry.

DigiHaul’s vision and mission also align with my ambition to serve society and make a positive impact on our planet.

We also have a greater responsibility to shape the data function and build the team from scratch, which may sound challenging but to me it’s fascinating.


What kind of data are you working with? How is this making a difference?

There’s ongoing digital transformation in the transport industry and some companies are much more advanced than others, so we have a very unbalanced landscape. Myself and my team are currently dealing with all kinds of data: shipment information, geospatial data, and ‘internet of things’ devices such as vehicle tracking data.

Currently, we’re working on network efficiency improvements by surfacing backhaul opportunities to tackle the empty running of road freight. This helps us improve the financial proposition for carriers by cutting costs as well as helping them to reduce their carbon footprint.


The transport process is traditionally a very hands-on and manual, why do you think it’s important to have access to data and how are you able to harness this data?

Digital transformation of the industry is a long journey. If you think about driverless cars, for example, we can think about the roadmap in the same way – how we can leverage data to drive insight.

First of all we need to collect as much data as we can to provide a single version of the truth and form a foundation, or a ‘data lake’.

The second step is to create value. By assessing the variables that the data services – such as location, distance, seasonality, vehicle type and carrier characteristics – we can better understand the market force and apply method learning to create value proposition for both carriers and shippers.

The next level is event-driven real-time response. We can take direct action following events communicated by data collectors, such as IoT devices, and process streams of events to derive insights and intelligence. Here we can tap into the real-time vehicle and the journey planning data as well as potential constraints on optimal routes. If we have the vehicle-tracking data en route, we can have a proactive response to any unplanned events such as traffic delays.

If we go another level higher, we’re at the stage where various models are being produced and the system is constantly learning how to develop the ability to generalise. With a deeper understanding of the cause and effect, we’re able to predict the general outcome of something we’ve never tried before, such as the best strategy for a new customer.

Finally, the ultimate dream for data scientists is to democratise data science where it becomes an integral part of the day-to-day process. If we have enough data, we can augment the decision-making process by introducing user-friendly analysis tools.

The data journey is not a one-off, it’s not going to appear overnight. We’re currently at the foundation level of data analysis because the industry is at the very early stages of a digital transformation.

DigiHaul is the preferred subcontractor for DHL so we also have access to historical data from our customers. Combining that with our technology allows us to really have a competitive edge and offer superb value.

It’s very exciting for me because I know we’ve got a chance to shape the business into what we feel is right for the transport industry. Not many people get the opportunity to be involved in this wave of technological transformation.


How will the work you’re doing in digital freight help to transform the way the industry works?

Currently we have a lot of challenges such as low margins, the way haulier distribution is deeply fragmented, and the way that much of the process remains offline and based on relationships. It’s as much a challenge with technology as a mindset challenge, but this is being transformed by improvements in efficiency delivered by digitalisation.

There are a few ways we can help transform the way the transport industry works. From a technology perspective, we can offer the opportunity to increase our carrier partners’ profitability using the data that we have. To change peoples’ mindsets, we can share ways that we can help to make them more profitable using the strong relationships that we already have with our shippers and carrier partners. And lastly, our team of experts can help lead them hand-in-hand through the journey and support them in using our system and integrating their existing vehicle tracking technology with ours.


What does a typical day look like in your role?

It’s a fantastic working environment here – we fully support hybrid working which means we can work at home or in the office as we see fit.

I currently manage a team of three data analysts and we’re looking to grow the team too. Typically we’ll have our internal meetings face-to-face as well as meetings with other teams across the business to make sure we’re all aligned to the same goal. Working from home means that I get time to focus on finding the best opportunities for our shippers and carriers.


What’s next for you and your team?

My main focus is to build the data lake, enhance our data pipeline with multiple data sources and build a solid foundation so that we can identify any opportunities for network optimisation.

Further along the pipeline we’re also looking to leverage machine learning to produce more efficient pricing and optimise our quotation process.


What’s the one thing that motivates you when you go to work?

My excitement comes from working for a start-up – I have a chance to witness the process of how data insights are underpinning strategy and operations. You wouldn’t experience this from scratch in a more established or mature business, but in a start-up to have the chance to influence decision making from the very beginning.

The location of our office in Hatfield Park is beautiful – that is certainly a plus!


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