Who is the Client

A US-based Fortune 500 departmental store chain with more than 1000 stores across the states, they are bringing stylish clothing for the entire family since decades now.

The Challenge

The client runs a huge e-commerce portal and constantly requires sales data analysis to optimize its business strategy. It was using Tableau-based dashboards to analyze the incremental data (streaming data) and historical sales data of over ten years.

However, the dashboards had several performance issues, such as frequent app crashes, slow report generation (40 min), and more. A lot of unwanted columns containing large amounts of data were getting generated in the reports. Since the client’s leadership team was able to choose only one filter at a time, they were unable to see the statistics for a specific time interval by selecting the start or end date and vendor name in one go.

Additionally, several fields were showing errors due to incorrect data structure of the tables, whose data was merged using blending logic to populate together in the reports. As a result, the client was unable to get real-time sales insights, which could help in making well-informed business decisions.

The Solution

GSPANN’s advanced analytics team examined the requirement and developed a POC first to overcome these issues. Following were the key tasks that we undertook:

  • Decoded the blending logic from Tableau and implemented it at the database level.
  • Removed unwanted historical columns from the reports.
  • Created a database-level architecture flow.
  • Displayed daily comparison and customer sales comparison in the reports using the data from different channels, such as channel marketing partner’s CRM, Marketo, Oracle database, and excel sheets.
  • Created a landing page where the users can select the start or end date for a transaction or event along with the vendor name (alphabetically) to generate the reports.

We redefined the data model, built the dashboards from scratch, and filtered data in the data source to avoid errors in the reports. We performed reverse engineering of the existing Tableau architecture and implemented a new architecture to improve the performance. The model was built in the Teradata database and ETL tasks were performed on the data source. We created ‘Views’ for the target tables and created reports by fetching data from the database.

Business Impact

  • The client’s leadership team can now visualize the data in real-time that helps them in taking better business decisions.
  • The users can populate reports utilizing any combination of filters without any errors.
  • The option of choosing all filters in one go has provided the much-needed convenience to the users.

Technologies Used

Tableau. A visual analytics platform transforming the way you use data to solve problems, empowering organizations to make the most of their data
Teradata. Provides database and analytics-related software, products, and services
Tableau server. An online hosting platform to hold all your tableau workbook, data sources, and more
MySQL. It works with an operating system to implement a relational database in a computer's storage system, manages users, and allows for network access

Related Capabilities

Utilize Actionable Insights from Multiple Data Hubs to Gain More Customers and Boost Sales

Unlock the power of data insights buried deep within your diverse systems across the organization. We empower businesses to effectively collect, beautifully visualize, critically analyze, and intelligently interpret data to support organizational goals. Our team ensures good returns on the big data technology investment with effective use of the latest data and analytics tools.

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