As Web(No Text) 700x500


Jane Leong is the Head of Data at Love, Bonito. She leads a team of data enthusiasts & problem solvers who are united to drive the organisation’s strategy and growth through data-driven decisions. Prior to joining Love, Bonito, Jane worked at Openspace Ventures where she sourced, executed and managed investments in early-stage technology companies in Southeast Asia. She continues to serve as an advisor for Openspace Ventures. Jane started her career at J.P. Morgan Technology investment banking in New York and Hong Kong. She graduated with High Honours from Princeton University with a degree in Operations Research and Financial Engineering.

Could you tell us about your career path to date?

I studied Operations Research & Financial Engineering, so my studies equipped me with a strong technical foundation for working with data science and analytics. 

I started my career on Wall Street, where I learnt the ins and outs of corporate finance and business strategy. I was later headhunted to Singapore to join a great venture capital firm where I focused on early-stage technology investments in Southeast Asia. 

Love, Bonito was one of my portfolio companies and I saw the opportunity for the company to start leveraging data to drive smarter decisions and accelerate its growth. After discussing further with the board and the team, I decided to join the team full-time to build the company's data and analytics function from scratch. 

What is Love, Bonito? 

Love, Bonito is the largest vertically integrated, omni-channel women’s fashion brand in the region today. The company currently comprises 150 people across the region, with headquarters in Singapore, teams in Indonesia and Malaysia, and a franchise in Cambodia. 

Designing for the key moments and milestones of a woman’s life, Love, Bonito’s comprehensive assortment features stylish, thoughtful and comfortable pieces for the modern Asian woman at home, work and play. 

How did you manage to build a high-performance data team from scratch in one year?

Building a data team is challenging, especially now when the demand for data professionals is high. However, for me, building a strong team doesn't necessarily mean hiring the most senior and experienced data scientists. We do look for the best but don't wait around for that one unicorn hire. I look at our team more holistically to ensure that members have different strengths and experiences that complement each other. That way, we can move a lot faster, build a strong and diverse team while up-skilling each other at the same time.

Some companies tend to hire more (or only) senior professionals when they get started, not necessarily because the work can only be done by seniors, but because there's no structure or culture in place to allow a junior to work efficiently. We decided to put in the extra time and effort early on to avoid that.

Having the right focus (and people) at the right time is also important. For instance, our initial focus was to put in place a strong foundation and data infrastructure because without accessible quality data our analytics efforts cannot scale. As we progress, our focus has shifted to analytics modelling and building up our data science capabilities.

How do you keep the team focused? 

From a practical perspective, establishing a robust and transparent framework from the get-go has been important. It's also critical to stay focused on what really matters by always anchoring my team's work and objectives in the context of our company's overall strategy and goals.

Clarity in purpose is what creates focus and motivation. Every piece of analysis or model we work on need to have a clear objective that’s directly related to making our business better. We collaborate with various departments and it's essential to be able to really understand and identify the actual business problem, help refine it, and then translate that to the right technical problems that we can logically solve.

What is important for driving a data culture across the business?

We were clear about the priority of promoting a strong data culture before even starting to build any infrastructure or data models. We focused on three areas to achieve this: key metrics and data vocabulary, data accessibility (infrastructure) and ongoing coaching.

It’s important to first align the company with the metrics to focus on, and develop the data vocabulary to work with data. Having transparent and aligned definitions also means that everyone is focused on the same things and comparing apples to apples across departments.

When we designed our analytics stack we knew it wasn’t only about powering our data science workflow but also enabling robust data reporting and exploration for non-technical stakeholders. Having seamless access to explore already aligned and clean data encourages data self-serve across the business, it also limits ad-hoc requests to my team so that we can really focus on the more complex analytics and most important projects.

While we are ambitious, we are also realistic. Data-culture doesn’t happen overnight it involves coaching the broader team on how to work with data. This requires a lot of patience and data storytelling; we need to deliver insights and recommendations that is clear and convincing to hopefully inspire action. Therefore, as we build trust in data and drive initial adoption, we try to limit “black box” solutions and use models that are simpler and easier to explain and understand.

Our business teams do great work every day and when that is objectively reflected in the numbers it creates validation and focus. Measurable outcome is a key ingredient for strategic long-term success. Data keeps our company honest, objective and performance-driven. Intuition is always important and often inspires new directions for data analysis as well, but data validates our intuition objectively and keeps us in check when our intuition fails.

How is Love, Bonito utilising data analytics and what problems are they solving?

Data is important in every corner of the business. Broadly speaking, we work on two key topics. The first one is about understanding our customers so that we can serve them better and the second part is driving automation and efficiency across our business so that we can do things smarter, faster and better.

We research topics ranging from what attributes make a winning design, to optimising stock levels at each store and deciding what products we should show to our customers on our website and when. Ultimately, by combining everything we know about our customers, from the information they explicitly tell us to their digital interaction and shopping behaviour, we’re able to customise our products and services, launch the most relevant products and deliver delightful experiences to our customers.

The beauty of it is that it has a compounding effect. As our models and analytics capabilities become stronger, we can create more meaningful and tailored experiences for our customers which in turn gives us more feedback and data to further strengthen our models and analytics - so we keep getting better and better.

What has been the most challenging time in your role so far?

Building out our data foundation and analytics workflow has been challenging but also rewarding. What’s challenging is not so much the actual technical implementation, but defining the strategy and action plan and then coordinating the workflow.

Before we built our data infrastructure, we were still in the spreadsheet era and all of our data was fragmented and kept in isolation in different source systems. This wasn’t scalable from an analytics perspective. So, we decided to rethink our analytics workflow including building our own enterprise data warehouse. The data warehouse is our own comprehensive analytical database that combines data from all our different business processes in a cohesive way with aligned definition and structure that powers our analytics workflow.

A big part of making that possible was to take a step back and think about what are the fundamental business questions that matter and defining KPIs before starting to build anything. In the process of doing that, we gained clarity on how to optimally design our data warehouse, prioritised what data needs to be tracked and uncovered any data quality problems that had to be resolved first. All of this was important groundwork that happened before any ETL or ELT process.

It takes time to build trust in data and the habit of leveraging it. Having clean, consistent and accessible data is an important step that our new data infrastructure has enabled. It all takes time as data is a long-term investment, but we’ve already made great progress. 

What advice would you give to a retail company looking to invest in data analytics?

Take a step back and identify clear objectives first. Don't build something for the sake of building it. Be smart and flexible. This is especially important for smaller companies as you're dealing with very limited resources and budget. Invest your team's time on areas where you will develop that competitive advantage and be open to leverage existing solutions in areas where you really don't need to reinvent the wheel. There are great open source, off-the-shelf and cloud solutions out there which have all levelled the playing field for smaller companies and also allow larger companies to become more agile.

In terms of hiring, how did you convince people to join you?

I joined Love, Bonito because I really believe in the company's vision and potential. We’re building something special at Love, Bonito and that’s reflected in our people (both our internal team culture as well as our loyal customer base). It's my job to translate that vision, excitement and potential to new hires as well.

My team offers a unique opportunity for data candidates because in addition to having the growth environment and flexibility of a start-up, we also have a strong brand with a loyal customer base to further scale with. Love, Bonito has actually been around for 10 years, which means we have a lot of data and experience to learn from. Given that we’re also a vertically integrated omni-channel business, this also means our team gets the opportunity to work on diverse problems with data points from online to offline channels and from projects ranging from assortment strategy, design generation to supply chain and customer growth.

I think it’s important to always be very honest and upfront about what a specific role entails so that when candidates join, expectations are aligned and they are set up for success. If you join our team, even as a junior member, you’re expected to wear multiple hats and you’ll have the opportunity to make significant contributions from the get-go. We look for people who are eager to take on that extra ownership and who can bring something new to the table that makes our team better; if that excites you, then this may be the right opportunity.

Posted almost 4 years ago
About the author:
Paris Herard

Our blogs and insights help keep you up to date with market developments and regional news