The Rise of Citizen Data Scientists in Finance: A Catalyst for Transformation

Unveiling the Three Pillars that Fuel Financial Innovation through Citizen Data Science

two humans- a banking expert and a scientist looking at each other

Data supreme in the finance industry, from trading algorithms to risk assessments. While data scientists have traditionally used these massive data lakes, a rising cadre of professionals are stepping up to share the load: Citizen Data Scientists. But what exactly are Citizen Data Scientists, and why might their rise be a boon for the financial sector?

Traditional Data Scientists: The Untapped Potential

Data science has been celebrated as the 'sexiest job of the 21st century,' a discipline essential for extracting business value from data. However, companies often bring in data scientists without clearly understanding their role or the importance they can provide. This can lead to a talent drain, as research from 365 Data Science suggests most data scientists will change jobs in less than two years.

Source: Number of Job Changes since 2017 by 365datascience

The finance sector, in particular, suffers from the mismatch between the rapid pace of technological change and traditional organizational structures. The existing data science teams are often mired in routine tasks rather than focusing on advanced analytical techniques that could significantly improve financial models or customer experiences.

Enter the Citizen Data Scientist

A Citizen Data Scientist (CDS) is not a replacement but instead a collaborator for the data science team. They are business experts willing to analyze data and discover new trends. According to Gartner, CDS are 'power users' capable of executing simple and moderately sophisticated analytical tasks, usually without coding. In the financial sector, the concept of CDS is far from theoretical. Many professionals using Excel or Tableau to analyze data can be transformed into Citizen Data Scientists with some training and the correct set of tools.

In a fast-paced financial environment, having a team of Citizen Data Scientists can address a significant bottleneck: turning data into actionable insights quickly enough to matter.

Let's consider the example of 'Mia,' a marketing expert in a bank. She's skilled at her job but is bottlenecked by the traditional way of generating reports and insights, waiting for the data science or IT team to respond to her requirements. With the tools and training to become a CDS, Mia can directly dive into the data, perform complex analysis, and develop actionable insights.

Citizen Data Scientists are not just data enthusiasts; they are domain experts armed with just enough data science skills to be dangerous. They fill a critical role in financial institutions, particularly those grappling with high staff turnover and a shortage of specialized skills. The Citizen Data Scientist can serve as a crucial bridge between the technological world of data science and the practical, outcome-driven realm of finance.

In finance, think about risk managers or traders who have been analyzing data for years. They already possess an intricate understanding of financial instruments, markets, and economic indicators. However, their analytical reach often ends with Excel spreadsheets. When empowered with the tools and skills to function as Citizen Data Scientists, these professionals can develop predictive models for market trends, customer behavior, or fraud detection. This democratically unlocks a new dimension of data science application, making the field accessible and beneficial for the entire organization.

Moreover, the CDS role has matured to a point where it's not just a gap-filler but a strategic component in finance. Data is not just numbers but also tied to compliance regulations, shareholder expectations, and business ethics. In the current digital climate, where data breaches and cyber risks threaten financial assets, the CDS can contribute to cybersecurity measures, risk assessments, and compliance checks.

In essence, the CDS role brings scalability to your data science operations in finance. They augment your data science team and get a practical perspective, often overlooked by technically-focused staff. This creates a richer, more balanced approach to extracting actionable insights from data.

The Three Pillars: Education, Tools, and Experimentation

three pillars

I. Education

While understanding derivatives or risk models might be crucial in finance, understanding basic analytics and machine learning concepts is vital for a CDS. Many platforms like Coursera offer professional certificates in data analytics tailored for people like Mia who don't come from a technical background but need to make sense of data.

The first pillar is education, more than just online courses or tutorials. In finance, this involves comprehending the regulatory landscape, understanding the nuances of market volatility, and having a finger on the pulse of economic indicators. Training modules could incorporate case studies that reflect real-life financial scenarios, making the learning curve easier and more relevant. Additionally, it's not just about analytics but contextual analytics. This contextual understanding elevates a CDS from being a data manipulator to a data strategist.

II. Tools

While Excel may have been the go-to tool for years, the second pillar necessitates an upgrade. In the fast-paced financial world, real-time data analysis is often crucial. Specialized software tools with features like AutoML (Automated Machine Learning), real-time analytics, and robust data governance mechanisms can make or break strategies. These platforms can offer functionalities ranging from No-code/Low-code interfaces to more complex environments, accommodating different skill levels and needs within the finance sector.

Numerous platforms are available on the market, and the right choice for you largely depends on your specific objectives. I can assist you if you want to select the ideal data platform for your business. I've successfully implemented such a solution and can share insights on achieving the same results.

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III. Experimentation

In the volatile world of finance, making a mistake can have far-reaching consequences. Therefore, having a safe environment to experiment is not just a feature but a necessity. These sandbox environments should host data sets for practice and simulate the financial market's intricacies. A natural or near-real scenario can help the CDS test predictive models or analytic algorithms in a risk-free setting, providing both confidence and competence.

The Future of Financial Analysis: A Symphony of Expertise

Imagine a world where your limited team of data scientists can focus on creating advanced algorithms for high-frequency trading or deep-dive into predictive analytics for assessing credit risks. Meanwhile, your fleet of Citizen Data Scientists is busily at work, fine-tuning customer segmentation models or optimizing the supply chain for your investment products.

The Citizen Data Scientist concept democratizes the analytics process. Giving data science tools to those closest to the business problems turns data into a resource anyone in the organization can use to generate value. In the context of the finance industry, this could be the key to unlocking more agile, responsive, and data-driven decision-making processes.

To capitalize on this, financial firms should invest in fostering a culture where Citizen Data Scientists can thrive alongside traditional data scientists. This is not merely a tactical shift but a strategic one that can redefine how financial institutions make decisions, allocate resources, and ultimately achieve a competitive edge.

If you're in the finance sector, now is the time to consider incorporating Citizen Data Scientists into your data strategy. It's not just about filling a gap; it's about creating a more resilient, agile, and data-driven organization. After all, in the high-stakes, ever-changing world of finance, being a step ahead can make all the difference.