Overview
With data complexity rising, our client in the financial research field needed a solution to efficiently analyze and visualize extensive datasets on a weekly basis. This would enable timely insights into company and industry trends, enhancing decision-making and operational efficiency.
- Managing high data volumes: Node.js and MySQL pipeline for scalable weekly uploads.
- Customization limits in Tableau: Enhanced functionality for a responsive experience on web and mobile.
- Web and mobile app with React frontend, Node.js backend, and Tableau dashboards.
- Admin panel for tracking uploads, user permissions, and weekly KPI updates.
- Automated workflows, predictive analytics, improved data access, and boosted productivity.
Case
As organizations and businesses generate huge amounts of data, the need for effective techniques to interpret and act on this data has grown. Data visualization has become crucial here, where large amounts of data and its complexity are unprecedented. Visualization tools can help in translating these vast data, often complex datasets into intuitive visual representations. This process helps decision makers to respond quickly to emerging opportunities, identify potential risks, and optimize operations based on real-time insights.
One of our clients needed a powerful yet streamlined solution to analyze and visualize vast datasets on a weekly basis. The intention was to provide timely insights into company and industry performance and trends, providing users with actionable insights. With a focus on quarterly analysis, the platform would help to identify how companies performed against benchmarks and also deliver predictive insights. Additionally, the client needed a responsive, user-friendly dashboard for both web and mobile, where data visualization could help users assess and explore company details quickly.
Challenges
The development team faced two major challenges while developing the solution. First challenge was to come up with a way to manage high data volumes. The client’s requirement to process and upload excel files weekly introduced challenges in terms of both scale and consistency. We implemented a pipeline that ingests data powered by Node.js and MySQL, which required careful design. We had to ensure that each dataset was split into relevant tables and accurately mapped to maintain data integrity. This data was uploaded into an S3 bucket, from where data could be retrieved easily by Tableau for visualization.Another major challenge we faced was the customisation constraints in Tableau. Tableau was selected for its powerful visualization capabilities, yet its built-in UI posed few limitations in meeting specific client needs, especially in creating a consistent experience across both web and mobile platforms. The client requested some custom features Tableau’s default setup couldn’t provide, so we dedicated some time to research and development to extend Tableau’s functionality. We created tailored views and added a responsive, user-friendly interface which satisfied the client’s needs.
Solution
To meet client’s requirements, we built a web-based and mobile application integrating a robust data processing backend, interactive Tableau-based visualizations, and a predictive analytics model. The platform’s frontend is developed using React and backend is developed using Node.js. A key feature is the admin panel, which can manage user permissions and tracks the status of data uploads. Each week the admin can see whether excel sheets have been successfully uploaded, ensuring data consistency and accuracy to users via the admin panel. Another provision to upload the excel sheets via shell commands has also been provided. User management mainly includes two user roles; admin and user. Admin can add customers to the Tableau dashboard via email authentication. Admin sends invites to the customer via mail and when the customer accepts the invite dashboards made on Tableau can be accessed.
Tableau creates dashboards that enable clients to explore trends easily. These visualizations are updated weekly with new data and are capable of providing a comprehensive quarterly analysis. Within these dashboards, key performance indicators highlight whether companies “beat” or “missed” expected benchmarks, and are displayed in green and red respectively. When users click on these indicators, they are redirected to a customer specific dashboard that aligns with their subscription level, offering detailed analytics into company performance. The platform allows users to drill down into specific company metrics. For example, by clicking on a company name, users are redirected into a secondary dashboard. This dashboard shows primary reasons for a company’s success or shortfall, industry specific factors contributing to the company’s performance, historical data for the past three years, enabling users to spot trends and identify patterns.
To enhance the platform’s value, we developed a data model that makes use of historical data to predict growth trends for the upcoming quarter. This model is also capable of providing investment recommendations based on anticipated growth, giving users valuable insights for future planning.
Impact
The platform has transformed the client’s data analysis process, allowing users to access, interpret, and act on data with unprecedented ease. Data visualizations that are updated weekly and quarterly analytics can give users timely insights into company and industry performance. The platform’s drill down functionality helps users gain a granular understanding of factors influencing a company’s success or shortfall.
By automating data ingestion, simplifying visualization, and by adding predictive modeling, we have enabled our client to deliver a product that can provide significant value to their end-users.
Tasks that once required manual effort such as data uploads, file validation are now automated. This has not only led to financial savings but has also enhanced team productivity and overall morale.
In areas of finance, healthcare, marketing and more data visualization aids in making predictions, tracking performance, and displaying findings in ways that are easily understood by both technical and non-technical audiences. This process plays an important role in enhancing communication as visual data is easier for people to understand than raw facts and numbers.