Meet the Kalibrate team: Sissi Zhao Lei, Data Analyst TrafficMetrix®

I think what makes us stand out from other traffic data providers is that we provide actual traffic counts and offer new counts to our clients as soon as they’re published by credible authorities. We also enrich our data set with additional layers, which means we now have additional elements for each traffic point.

December 16, 2020|6 minutes

At Kalibrate, we believe our success is driven as much by our people as our technology.

So, as part of our new meet the team series, we’re showcasing some of the talent and expertise within the Kalibrate family. This installment features Sissi Zhao Lei, Data Analyst for Kalibrate’s TrafficMetrix®

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What is your role at Kalibrate?

I’m a Data Analyst working on traffic data for TrafficMetrix®.

As part of the Data Operations team it’s my job to collect, collate, and curate data to make sure it’s turned into something that our users can easily access and analyze. I work closely with the Data Science team and provide them with data which they use to create AI models and predict future trends.

What does a typical day look like?

I collect published traffic counts from a vast number of trusted data sources, add extra information to enrich the data for our clients, and then add that onto the map for our users to analyze.

I get to travel around the world — particularly the US — from the comfort of my home office!

I think what makes us stand out from other traffic data providers is that we provide actual traffic counts and offer new counts to our clients as soon as they’re published by credible authorities. We also enrich our data set with additional layers. This year we started to offer OpenStreetMap (OSM) attributes, which means now we have additional elements for each traffic point, such as road functional class, maximum road speed, bridges, and tunnels. All this information is then transformed into TrafficMetrix® data ready to deliver to our clients.

To be able to handle the amount of new data that comes in for each release, we developed a whole new automated process which has enabled us to deliver over 20x more traffic counts in 2020 than in any previous year.

What do you love about your role?

Kalibrate’s the first company I’ve worked for since completing my master’s degree in data science, and it’s also the first job I’ve had in the UK. What I love about Kalibrate is the diverse culture — they embrace people from different backgrounds. In my team alone, I have colleagues from seven different countries. It’s an inclusive environment and they’ve made me feel at home.

There are some great opportunities here at Kalibrate. The company is never standing still. Over the past year we’ve made some huge improvements to TrafficMetrix® which has meant that we can now update traffic counts at a much quicker pace than ever before. There are some exciting plans in place for more enhancements to the data and the products and I’m thrilled that I get to be a part of them.

The management team at Kalibrate really empowers employees. I’m trusted to do my job competently and they give the freedom and encouragement needed to come up with fresh new ideas.

I recently proposed that adding “truck” traffic into TrafficMetrix® could enrich our data set and bring extra value to our end users. This idea has been given full support from the team and we’re almost ready to add this extra layer of data into our TrafficMetrix® exports.

Why did you choose to join Kalibrate?

I have a master’s degree in data science as well as experience in retail and marketing. Joining Kalibrate has allowed me to combine all of those things into one role. The knowledge and skills learnt through my data science degree allow me to understand and interpret the traffic data, and my experience in retail and marketing mean I also have a great understanding of how this data can be used to influence end user decisions.

My main responsibility is to prepare the data and make sure it satisfies the client’s needs, but I like to make sure that I understand the outcomes so that I can be confident the data I provide is being used to drive action.

How can TrafficMetrix® data be used to drive decisions?

Retailers can use the TrafficMetrix^® data to understand their potential turn in rates, the amount of traffic that ‘turns in’ to your store or your forecourt compared to the overall road traffic or the exposure rate for their brand when stores are positioned in different locations.

Local authorities can understand whether road conditions are acceptable for the current traffic numbers and whether this is expected to change in the future so they can better plan and prioritize their road improvement strategies.

Although I don’t often speak to clients in my day–to–day role, their end results and outcomes are always at the forefront of my mind.

TrafficMetrix® allows users to choose whichever GIS tool they’re most comfortable with to help them visualize the data. They can then analyze actual data counts, TrafficMetrix® Current Year Estimates, and TrafficMetrix® Five Year Projections to help them inform business planning decisions.

How will data analysis change in the future?

There’s a desire for “real time” data at the moment. But this is such a limited view, and it restricts the amount of insight and understanding that can be attained from the data. It is important to look at a time period rather than just a time point. We need to look at data within a certain time frame, in comparison to historical data, to give us a deeper understanding of trends and changes. In the future, as data sets grow this will only become more important as more patterns and trends become apparent.

The amount of data that’s available is ever increasing, and thanks to the changes we’ve made recently, our capability to update that data in TrafficMetrix® is increasing too. We update the data set every quarter bringing even more insight to our users. We’ve added almost one million traffic counts to our data set in the past 12 months. More data is a good thing for our clients. It means that our machine learning and data modeling are getting even more sophisticated and, in the future, we’ll be able to provide even more reliable projections and predictions.