Every piece of wealth-management software that has a front-end needs tools to present data in a visual way. In today’s article, I want to share some means and tips on visualizing wealth-management data.
What is displayed visually?
As a rule, WealthTech projects use various and sundry kinds of data. Almost any data may be used for reports—this is where visualization is most often required.
Visual imagery may be used to show the following data:
- Current status (for example, asset allocation, number of clients, assets under management):
Source: https://portfolios.insart.com/ - Results of analysis (for example, risk profiling):
Source: https://www.riskalyze.com/ - Statistics (for example, historical returns, investment changes for different groups of investors):
Source: https://portfolios.insart.com/ - Forecasts (for example, possible portfolio returns):
Source: INSART’s internal project - Dependencies and correlations (for example, similar historical performance, dependencies between stock returns):
Source: https://correlate.pro/ - Comparison (past returns of different assets, asset classes, types of portfolios):
Source: https://portfolios.insart.com/
Depending on the purpose, you may choose to use different types of visuals, such as pie, timeline, bar, tree, or bubble charts.
How should it be designed?
The main factor that you should consider is the competency of the target audience. Charts created for financial advisors may be much more complicated compared to those created for end investors, especially if the latter are new to investing.
Most investors will understand pie and line charts if you show no more than two or three segments/lines in each chart. This may be a pie chart that shows their portfolio allocation or a line graph with expected or past returns.
On the contrary, advisors prefer to see much more information on the screen without the need to request additional data. For example, they may need all data about a particular client or household, such as:
- Their full risk profile, including individual risk tolerance and mutual risk capacity, and their behavioral analytics;
- Current portfolio allocation, its value, and how it has changed over time;
- Statistics about rebalancing, including realized and unrealized gains, etc.
All this data will require various visuals—tables, charts, and graphs of different kinds depending on what they show. For example, to compare the growth of two portfolios over the years it’s better to use timelines. To show asset allocation in a portfolio, pies or bars are mostly used.
Source: INSART’s internal project
For visualization, we most often use such tools as D3.js, Highcharts, ChartIQ, Qlikview, FusionCharts, etc.
What about data?
Because data is the basis for any visualization, it’s crucial to prepare it thoroughly. Here, the coordination of all groups of people working with the data becomes vital:
- Business analysts and product owners explore what types of data are required and why they may be useful.
- UI/UX experts provide the design according to business goals—what data and changes should be displayed and how this should help financial advisors and investors.
- Database engineers and big data engineers process the data to convert it into a consistent format that is best suited for storing and rendering.
- Back-end and/or front-end software engineers work on visualization.
Data transfer between database (or warehouse), back-end, and front-end is mostly provided via APIs. The API documentation enables precise coordination.
I would say that the following data-preparation tools are the most popular in our teams: Alteryx Analytics, IBM Watson Analytics, TIBCO Spotfire, etc.