The Great Resignation: How Software Is Plugging The Finance Industry’s Data Skills Gap

Data scientists, developers, and engineers are all on the move.

A recent study by 365 Data Science revealed that sought-after data scientists switch employers every 1.7 years on average. And developers are quick to change jobs also. A February 2022 survey conducted by Salesforce’s Mulesoft found that 93% of 600 IT leaders from enterprises across the US, UK, France, Germany, and Australia were finding it more difficult to retain skilled developers and 86% were also finding it more difficult to recruit since the start of the pandemic.

That’s a big problem for financial services firms as business processes are becoming more data-intensive. To avoid drowning in data, firms need to digitalise and become more data-driven and they depend on expert technology and data specialists to provide that capability.

Delivering technology and tools

 Every data scientist and developer wants to be paid well for what they do but they also need a challenging work environment and to be able to work on interesting problems using cutting-edge technology. In addition, they want their job to allow them to hone their skills and advance their career. Critically too, every working environment should be supported by appropriate cloud infrastructure allowing for easy and fast data onboarding and data sharing as well as the use of the latest open source technologies, that give data experts the creative freedom and processing power to experiment with different scenarios.

Many financial services organisations are still failing to hit the mark when it comes to making the latest technologies available to these employees. Recent research by Alveo in the UK, US, and Asia that focused on ESG data analysis specifically reveals that just 37% of data scientists in financial services firms currently use AI, machine learning and other advanced technologies in their key analysis and investment processes and workflows. This underlines the fact that, despite the potential of AI and its rapid growth, harnessing its capability for practical benefit can be more challenging.

Why data scientists and developers need to be stretched

Many data experts working for financial services firms are weighed down by basic administrative, and/or routine tasks. Two-thirds (66%) of respondents to the Alveo survey say quants and data analysts in their organisation have to spend between 25% and 50% of their time collecting, preparing and quality-controlling data. 

Even if they are getting paid well for their efforts, most data scientists will not want to work in roles where they spend their time dealing with basic data aggregation and quality control issues rather than inferring new insights and conducting statistical analysis on large data sets.  Getting mired in clerical work will prevent staff from developing the skills and capabilities they need to advance their careers and keep them motivated. Integrated data management and analytics solutions together with data modelling, data quality and data onboarding capabilities will ensure staff hit the ground running.

Training and the wider working environment

From the financial services firm’s perspective, this enhanced technological capability has to be accompanied by relevant training. It is often a key way of engaging these important workers and keeping them loyal.  

That also plays into the need to build a flexible environment for developers and data scientists to work in. This may include offering hybrid work arrangements and giving staff the opportunity to work remotely if needed. Developers, in particular, value working in a calm, quiet location that allows them to focus their attention on their creative work. It is important here to ensure that the home-work environment is secure and that staff have what they need to work as efficiently as possible.

Providing a challenge

It is also key to ensure that these staff are supported in using popular, widely leveraged technologies like Cassandra and Apache Spark: technologies that once learned by data scientists can become portable skills. For data scientists, the equivalent is likely to be the latest productivity tool, that allows them to get to complex analytics tasks as fast as possible, skipping over the more mundane data collection mastering and aggregation jobs

Highly skilled and trained developers and data scientists need to be given the latitude to work in a way that suits them best. That should be a collaborative process, likely involving brainstorming sessions with colleagues but there should always be an element of freeing up the creativity of your analysts and developer experts.

Financial services firms can further accelerate data scientist and developer engagement by ensuring these employees are able to work on challenging projects with interesting, ground-breaking clients: the sort that pushes them to work hard and deliver to their full potential.  

Enabling staff to hone these skills working for demanding high-end clients from central banks to leading hedge funds will help drive their development. As a result, firms can counter The Great Resignation, keep data scientists and developers engaged for longer, build their skills to position them well in their long-term careers and reap further rewards in terms of efficient project execution and enhanced client engagement. 

About the author: Martijn Groot is VP Marketing and Strategy at Alveo.

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