Tackling The Data Skills Gap In The Payments Industry


Payments generate roughly 90% of banks useful customer data, and global cashless payment volumes are set to increase to almost 1.9tn by 2025.

Beyond throwaway statistics, the indisputable truth is that data is now critical to the way the Payments Industry operates. How it’s collected, stored, analyzed, understood, and actioned is fundamental to the delivery of a vast array of products and services. 


Hopefully though, none of this should come as a surprise.

We know that the Payments Industry is one of the leading generators of big data. 

We know big data is a fuel that will drive the entire industry towards better customer service and higher revenue.

And yes, over the course of time big data has proved its high value and efficiency.

But, this is only possible if organizations have a workforce with the data skills required to extract this value and create efficiencies, and the simplification that digital payments provides millions of customers and merchants has created unprecedented demand for high-end tech talent to build cutting-edge payments products and solutions.



Here’s the rub, though: it’s a candidate’s market. 

Finding employees with the requisite skills and experience to work in specialist data roles, or at least handle data within their wider remit, is becoming increasingly challenging.

Reporting commissioned by the UK Government has found that nearly half (48%) of businesses are trying to recruit for data roles, but a similar number (46%) were struggling to find candidates. 

It’s become abundantly clear that the current demand for individuals with these skills far outweighs the supply, which has seen the cost of ‘buying in’ this talent rise dramatically.

“There are very few data scientists out there passing out their resumes. Data scientists are almost all already employed, because they’re so much in demand.”

Allen Blue, LinkedIn co-founder 

This means that upskilling and reskilling existing talent pools will prove a cost-effective and future-forward way to counter this market imbalance.


Data is now interwoven into all business departments, therefore if you truly want to extract every ounce of value from your data, the skills to be able to do this should not be siloed in a single department, team or individual. 


This doesn’t mean every employee needs to be a data scientist or that you need a data scientist in the room to inform every decision. You just need to be able to scale that skillset at varying degrees throughout your organization.


Data skills are just as likely to be required among someone working in marketing, customer insight, product development or risk analysis. In fact, almost every decision-making function within a company is reliant on crunching high volumes of data.

You need to empower employees across all departments to be able to apply varying levels of data skills, to ensure that data-informed decisions are happening at every level of your organization.



In the last 5 years, we’ve been engaged by clients spanning the full breadth of the Financial Services industry to help reduce the data skills gap of their organizations by building and implementing multiple in-house Data Academy programs.


We’ve enabled their employees to make a break with Excel and build even smarter models and perform deeper data analysis. Moving them away from traditional spreadsheets has made it easier to handle large data sets, including real-time data, and unlock insights that would otherwise remain hidden.


The examples below evidence the impact Payments Industry learners are able to make across their organization when applying the skills taught through one of our Data Academies.

#1 Reducing customer churn

By using predictive analytics to detect changes in consumers usage habits and Natural Language Processing to extract user feedback from social media platforms, you’re able to proactively reach out to high-value customers who have experienced a series of quality issues or reported negative experiences regarding the service on social media. 


#2 Preemptive Diagnostics

Building learning models to identify patterns of customer behavior that precede the loss of an account. Through the automation of internal processes in identifying and segmenting clients with similar risk profiles, and letting mathematics determine the clusters rather than relying on the human eye and arbitrary filters. This early diagnosis helps to create strategies to reduce future losses.


#3 Price Optimization

By analyzing customers’ reactions to different pricing strategies, purchase history, and competitor pricing, you’re able to gain accurate data insights and create optimal pricing strategies for products and services to retain customers and attract new accounts.



It’s evident that data analytics and data science provide numerous opportunities for the Payments Industry to smartly utilize the vast amount of data being generated daily, and that companies need qualified professionals to continue shaping the future of payments. 

However, the demand for such professionals far outweighs the supply therefore the time is ripe for organizations to invest in the data analytics and data science competences within their existing workforce.

We appreciate that time away from the ‘day job’ to upskill an employee is an opportunity cost to any business. There is no “one size fits all” solution.


Therefore, we design the duration of our Academies around our clients needs, whether this is an ongoing year-long program, an intensive bootcamp or a burst of micro-masters modules.

To find out more about Decoded; how we partner with organizations from the Boardroom to the shop floor transforming their digital capabilities; or how our programs could help you with your digital transformation, please email me at patrick@decoded.com


Get in touch.

Want to find out more? Leave your details and we’ll be in touch.