The explosion in data variety, velocity, and volume in recent years has brought with it new methods of tracking and analytics for banks and financial institutions. The modern-day market for data visualization tools offers a range of price points, types of visualization, and functionalities.
That said, choosing the right tool is only one piece of the data insights puzzle for CIOs and CTOs in 2023. My latest report on data visualization tools provides both a market overview and a checklist for IT leaders on optimizing their overall data ecosystem to make the most of their visualizations.
Open-source data visualization tools are tempting, as many of them are free to use. Well-known open-source tools include Google Charts and D3.js. Options like this can seem like a great way for smaller community banks and credit unions to save money.
But tools like these can open the door to more potential pitfalls. Financial services institutions must adhere to steeper privacy regulations than other industries. There is no vendor or central governing body to liaise with about security risks. As the source code is available for anyone to edit, it can be a target for hackers.
Not only that, but the source code sometimes includes snippets of code from vended solutions. That means that, depending on the open-source licensing, users may owe money to a vendor or individual even though they think they are using a free, open-source tool. While financial institutions should always ensure that a process is in place for legal and operational management prior to implementing any data tool, this is especially important when it comes to open-source software.
The Existing Data Ecosystem
The ideal data environment includes a data staging area, an operational data store, a data warehouse, an optional data lake (for big data and unstructured data), and data marts. Among these key components, data analytics and visualization tools are usually directly connected to the data marts.
Not all financial institutions have achieved this ideal environment in 2023. Individual financial institutions are at different stages of the data journey; furthermore, no two institutions’ data ecosystems are exactly alike. Data visualizations frequently offer a range of connectivity options and APIs, which community banks and credit unions must compare not only to their existing architecture, but also to the future IT roadmap to facilitate long-term success.
Types of Visualization Tools
In today’s marketplace, there are a variety of visualization tools with capabilities that are leverageable for problem-solving. Some of these, such as code tools, require highly skilled resources whereas others have a business-friendly interface. Different types of tools offer different levels of customization, types of visualizations, and interactive components.
Visual reporting and business intelligence tools can often connect to multiple data sources for processing visualizations, dashboards, and reports. Network graphs are useful comparison tools and are frequently used in the financial services industry for fraud/AML monitoring. Common banking use cases for data visualization tools also include forecasting and risk management, among others. Community banks and credit unions wishing to achieve a data-driven model can derive significant value from combining different tools depending on the issues at hand.