More than once, we learn how banks and other financial organizations are asked to get better at “storytelling” by distilling key insights about plans, profits, and prospects, in ways that make sense to non-finance professionals. This ask depends on two things – one is the availability of quality data (metrics, KPIs, and other critical business health parameters), and two, the appropriate tools to access (and represent) both structured and unstructured data culled from across internal and external sources.
The real value of Data Visualization
In this scenario, data visualization unites analytics and data-processing tools to churn out user-friendly reports and bespoke presentations for select audiences. However, the real value for banks is unlocked when a few preliminary questions are used to unravel the core.
- Who is the audience, the level of their data expertise, and where would the data be used (precisely, the decision-making it will enable)?
- Regarding the device and its designs, interface, and visual experience, what are the data representation requirements?
- Finally, what is the outcome desired – enhance holistic decision–making, facilitate deeper conversation, or end-user education?
After discussing the essential goals (audience composition and user purposes) for data visualization, it is natural to look at the various available tools.
Data Visualization Tool Categories
While the data visualization field evolves at a fast clip, there are three broad categories.
Beginner or DIY Tools
There are products like Tableau and Qlik where the tools are easy to set up, access data from multiple sources, and allow for easy familiarization. Along with extensive product demos, online user communities are associated with powerful tips for getting started, troubleshooting, and advanced features.
Next-Gen Analytics
The next swathe of products comes from IBM, Oracle, SAP, and Microsoft, offering a broader palette of analytics, reporting capabilities, business intelligence, and visualization capabilities. From addressing complex data platform needs to wide-ranging powers, this category asks for more profound expertise from its users.
Open-source tools
Tools like D3.js (D3 stands for ‘data-driven documents’) use a JavaScript library to develop interactive visualizations, So interactive maps within websites (for, say, the election results and other data-driven journalism) are created with such tools. This category works best when extensive customization and large-scale.
To leverage its full potential, these tools require a modest level of JavaScript coding expertise and some proficiency in HTML and other languages. Interactivity is needed. An additional benefit comes when a framework has to be developed, allowing for code to be reused.
Even though data visualization is a way to drive reporting, analytics, and other data representation, it is powerful to tell a story that amplifies the metrics, factors, and variables for both finance and non-finance professionals.
And the outcome? The ability for banks and other finance departments to effectively partner across departments.
Another fertile space for publishing data visualization outputs is social media. After all, for datasets to gain a competitive edge is often closely linked to the number of people that study them and comment on their accuracy and efficacy. Banking teams can often progress beyond pilot projects to command ambitious projects with senior sponsors – thanks to data visualization.
Beyond the standards – the world of advanced data visualization
Once the teams master the regular visuals, reports, and dashboards, there is a wide emerging area of ADV where banks can create curated and complex, interactive forms of data visualization. Often web-based, as well as VR, MR, and AR-based, these intuitive visualizations are the future.
Conclusion – A picture can write a 1,000 words
Helping banks make timely and prescient decisions with mountains of data is at the core of the financial industry. More than ever, there are solutions beyond the traditional BI tools that process and analyze massive data volumes with real-time velocity.
So, be it for risk modeling tasks, meeting regulatory requirements, or operating BAU activities, for banks, data visualization tools have come a long way – from being a nice-to-have to a must-have.
