Overcoming FinTech Data Challenges

09 June 2021

Using data to make informed business decisions


Back in April, we brought together key players in the FinTech start-up space for a closed roundtable. CTOs, Heads of Data and Heads of Engineering spoke to Netpremacy and our partners at Looker and Google about data areas they wish to improve in their businesses. 

We discussed the challenges that arise when using data to drive smarter business outcomes and how to address them. Here’s what we learnt. 

What data challenges do leaders in FinTech face? 

Despite a universal understanding amongst the group of data’s value and an appetite to use it to its full potential, several barriers are getting in the way of making this happen. 

A lack of data transparency. Thanks to a wide range of data sources, environments and a reluctance to open up data to everyone, it can be challenging to centralise data in an accessible, meaningful way. 

Getting started in making more data-driven decisions. Many companies have large amounts of data but are unsure how to capitalise on it and make educated business decisions based on their findings. There is pressure to do more with data, but the myriad of potential can be overwhelming. 

Creating a data-driven culture. Even where there are data analytics tools in place, they are just part of the picture. How do companies make sure that end-users not only understand how to use the tools at their disposal but use data to drive what they do? How do they encourage everyone in the organisation to take a more data-centric approach to understand the business better and use insights to improve customer experience?

Driving adoption of self-service data. Data queries and requests to the technical team can cause bottlenecks and block gaining valuable insights in customer activity, product opportunities and more. How can businesses encourage non-technical users to discover these insights for themselves?

Modernising reporting across teams. Many companies in the FinTech space are struggling with updating the way that their teams are creating reports. The spreadsheet culture is still predominant in many workplaces. It causes a loss of data and leaves room for human error. It also slows down the reporting process due to the manual importation of data. 

So, how to solve them?

Here are just some of the ideas we discussed with our panel, Looker specialist Colin Murphy and Matthew Yeager, EMEA Technical Programme Lead, Startup & VC Ecosystem at Google. 

Get buy-in by solving the right problems and speaking the right language. As well as being Google’s EMEA Technical Programme Lead, Matthew Yeager has plenty of experience in the start-up arena, with multiple start-up successes to his name. His advice, when at the start of a business journey is to ‘ask yourself, what problem am I trying to solve here? For example, how can I manage my cash flow better? How would knowing more about my data help fix this problem? Use language that resonates across the business. You need to be able to describe problems and possible solutions in a way that appeals to the right people at the right time, from investors to the C-Suite, and everyone in between.’

Do your research. Don’t just rely on your data, use other research data sets to learn more. Google has a free open data set for you to go and explore. Finding out what people are searching for will help you produce a product that fits a gap in the market. Read what others, such as challenger bank Monzo, are doing with their data to get ahead. 

There is no such thing as too much data, but stop waiting for ‘perfect data’ to get started. It is vital to have a working theory with a data set. The more data you have, the better and more valuable insights you are going to get. Throwing away data can harm your analysis and outcomes. However, waiting for the ‘right’ or ‘perfect’ amount is fruitless. Use the data you have right now, and keep collecting more to ingest and improve your model.

Let the platform do the work for you. Another piece of advice from Matthew was to stop trying to write ‘pretty code’. No investor is interested in machine learning code or how good it looks. Investors only want to know what problem you are solving. Save time by using tools to analyse data quickly. 

Create a model that allows anyone in the organisation to gain data insights. Move away from a model where the technical team is writing a requested query to answer one question at a time by empowering the end-user and reducing bottlenecks with tools like Looker. For example, Monzo’s non-technical staff self-serve 85% of BI queries without consulting the data team with their BI solution built on Google BigQuery and Google Cloud Platform. 

Bring non-technical colleagues on the data journey with you. Ease colleagues gently and at their own pace. Start an end-user with a dashboard, then go beyond that dashboard to build out their own visuals, insights and more. Train your new starters on tools like Looker so they understand data is as much a part of their role as their technical colleagues’. Be open to and appreciate their feedback and ideas, don’t be overly prescriptive. They will use data in a way a data engineer/scientist wouldn’t, but that can be where the real insights happen. 

Integrate technical and end-user teams. Encourage a spirit of data-driven working and a feedback loop by bringing together technical and end-user colleagues. Technical colleagues can upskill and help their non-technical colleagues, and non-technical staff can give insight into the practical application of data insights. By working side-by-side, teams can work together to get better results for customers and the business, truly driving innovation. 

What next?

As FinTech companies grow, so does the amount of data they are storing and collecting. This valuable, often untapped resource can be the difference between just another failed startup and a massive success. Using data to understand customer behaviour, predict trends and future demands is already the norm for forward-thinking companies.

Wherever you are on your data journey, there are plenty of powerful tools like Looker and Google BigQuery to support your company in accessing and gaining insights into data. However, as many are learning, the real challenge is company culture. Giving non-technical colleagues the technology they need is one thing, but pivoting to a data-led approach is a must if your business is seriously committed to being one step ahead of the competition.  

Speak with the Experts

At Netpremacy, we have a plethora of experience working with hyper-growth companies, including Just Eat, Monzo and Deliveroo. If you’d like to get more from your data, our team of data engineers and infrastructure specialists can help. Our decade of experience in change management means that you’re in safe hands to make the cultural shift needed to change your organisation. Contact us for an initial conversation on your data strategy to start doing things differently. 


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Monzo Case Study 

Monzo refines and optimizes its fast-developing product with BI analytics based on Google BigQuery and Google Cloud Platform.



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