In today’s fast-moving financial landscape, data-driven decision-making is no longer optional—it is essential. Financial analysts rely on massive amounts of structured and unstructured data to make sense of market trends, evaluate stock performance, and prepare investment strategies. Tableau, a leading data visualization tool, plays a crucial role in this process by enabling professionals to transform raw datasets into meaningful visual insights.
However, Tableau does not natively integrate with every third-party data provider. This is where Tableau’s Web Data Connector (WDC) becomes indispensable. By creating a custom WDC, organizations can fetch real-time data from external sources and integrate it seamlessly into Tableau dashboards.
This article explores how a Tableau Web Data Connector was developed for FactSet, a global financial data and software company. We will cover what a WDC is, how it works, the steps involved in this project, challenges faced, and the key lessons learned along the way.
FactSet is widely recognized as a trusted provider of financial market data, analytics, and research tools. For analysts, having access to FactSet’s API means access to accurate stock and ticker-level information in real-time. The challenge, however, is that Tableau does not provide direct out-of-the-box connectors for FactSet.
This created the need for a custom Web Data Connector that could:
Fetch data directly from FactSet’s servers using their API.
Handle user input dynamically (for example, entering a stock ticker like AAPL or MSFT).
Return clean, tabular data to Tableau in near real-time.
Support building dashboards for stock histories, financial performance, and analyst estimates.
By building this WDC, analysts could bypass manual downloads or static Excel sheets and instead work with live, interactive dashboards directly inside Tableau.
A Web Data Connector in Tableau is essentially a bridge between Tableau and external web-based data sources. Unlike Tableau’s built-in connectors (such as for SQL, Excel, or Salesforce), a WDC allows users to bring in virtually any data available through a web service.
Technically, a WDC is:
A web page built with HTML, CSS, and JavaScript.
A script that makes AJAX calls to fetch data from an API or other web resource.
A parser that transforms responses (usually JSON or XML) into a tabular format compatible with Tableau.
Here’s a simplified workflow:
A user enters input (such as a stock ticker).
The WDC sends requests to the API using AJAX.
The API checks for authentication and authorization.
If valid, the API returns data in JSON or XML.
The WDC parses and reshapes the data into a table.
Tableau ingests this table and populates dashboards.
This ability to pull external datasets dynamically makes WDCs highly valuable in domains like finance, marketing, and operations where real-time data is critical.
The first challenge was to develop a WDC capable of interacting with FactSet’s APIs. This involved a number of technical steps:
User Input & Interface:
The WDC needed a front-end interface where the user could enter a stock ticker. This was developed using HTML and CSS, keeping the interface simple but user-friendly.
AJAX Requests:
With every ticker input, the WDC had to send one or multiple AJAX calls to FactSet’s servers. Each request had to be correctly framed with query parameters for stock/ticker-specific data.
Authentication Handling:
FactSet APIs enforce strict authentication and authorization. The WDC was programmed to pass necessary credentials and validate responses securely.
Data Parsing & Transformation:
Since FactSet returned data in JSON/XML, the WDC had to parse this raw output and restructure it into a tabular dataset. For example, fields like "open price," "closing price," "earnings estimate," etc., were mapped into rows and columns recognizable by Tableau.
Overcoming CORS (Cross-Origin Resource Sharing):
One major roadblock was CORS restrictions, which often block client-side scripts from making direct API calls. To resolve this, a proxy server was implemented to route requests safely.
This stage of development was heavily JavaScript-intensive, as the logic for API calls, parsing, and transformations all resided in JavaScript.
Once the WDC was functional, the next step was building dashboards that financial analysts could use for decision-making.
Some of the dashboards developed included:
Stock Trading History Dashboard
Visualized the daily trading history of a selected ticker.
Included metrics like opening price, closing price, volume, and volatility.
Allowed analysts to spot patterns and detect unusual fluctuations.
Financial Performance Dashboard
Showcased company fundamentals such as revenue, net income, and earnings per share (EPS).
Enabled analysts to compare historical and quarterly results.
Broker Estimates Dashboard
Aggregated estimates from different brokers.
Helped analysts evaluate buy/hold/sell recommendations.
By connecting these dashboards to the live WDC, users could input any stock ticker and immediately see the relevant insights without waiting for manual data preparation.
Handling CORS Issues
Challenge: Direct API calls from the browser were blocked.
Solution: Implemented a proxy server to route requests securely.
Data Volume Management
Challenge: Financial data can be vast, especially when fetching years of historical trading data.
Solution: Limited queries to a reasonable timeframe and optimized parsing logic.
Dynamic Ticker Inputs
Challenge: Users could input any ticker, meaning the connector needed to handle unexpected cases (e.g., invalid tickers).
Solution: Added validation steps and error handling for invalid or missing data.
Performance Optimization
Challenge: Real-time data retrieval needed to be fast.
Solution: Optimized AJAX calls, minimized redundant queries, and cached certain results where applicable.
Technical Mastery of WDCs
Developing this project provided deep experience in how Tableau’s Web Data Connectors function and how to extend Tableau’s native capabilities using web technologies.
API-Driven Data Ecosystem
The project highlighted how critical APIs are in modern analytics pipelines. With APIs, data silos can be broken down, allowing analysts to access high-quality datasets dynamically.
User-Centric Dashboard Design
The dashboards were designed not just to show data but to answer key financial questions:
Is this stock overperforming or underperforming?
What do brokers recommend?
What trends are emerging in the trading history?
Real-World Constraints
Challenges like CORS limitations, data volume, and user variability taught important lessons about balancing technical implementation with user expectations.
For financial analysts, time is often the most critical resource. By automating data retrieval through a FactSet WDC, analysts could spend less time gathering data and more time analyzing it.
The dashboards delivered:
Efficiency: Instant access to updated stock information.
Accuracy: Data directly pulled from FactSet reduced errors common in manual processes.
Flexibility: Analysts could test multiple scenarios quickly by simply entering different tickers.
In the long term, this approach lays the foundation for more advanced analytics, such as integrating machine learning models into Tableau dashboards for predictive insights.
The development of a Tableau Web Data Connector for FactSet showcases how organizations can bridge gaps between data providers and visualization tools. By leveraging HTML, CSS, and JavaScript for the WDC, overcoming CORS challenges, and designing meaningful dashboards in Tableau, this project successfully enabled financial analysts to access, visualize, and act upon critical market data in real-time.
As the financial industry continues to demand speed, transparency, and insight, custom WDCs like this will become increasingly valuable. They empower decision-makers to look beyond static reports and embrace dynamic, interactive, and data-driven storytelling.
This article was originally published on Perceptive Analytics.
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