About Client

The client is a designer, marketer, and distributor of upscale collections of women’s attire, sportswear, and other products. Originally established in the year 1950, it has emerged as an affluent brand with a heritage and aesthetic based on the Palm Beach resort lifestyle.

The brand has demonstrated multi-generational appeal. Its products can be found in the company’s owned signature stores, e-commerce site, certain department stores, and a variety of independent specialty stores. (PIO) is supporting this fashion retail client in the field of Data analysis using business intelligence, EDI transactions and IBM i projects.


The Challenge

The client being a large scale retail company of garments and fashionable outfits, it had to focus on delightful CX and offer various discounts and promos. For this the retail client had to analyze real time data but it was facing the following challenges:

  • The client had a huge amount of promo and sales data which became extremely difficult to retrieve from the database.
  • The existing data warehouse had complex components and queries that took a lot of time to fetch the required data from several data tables. This delay made them analyze old data instead of real time data.
  • The inability to drill down to the exact information with cumbersome manual process was frustrating for the data management team.
  • Huge amount of data overload and retrieval even led to bugs and security issues.

Looking at the complexity and delays the client wanted a simplified Centralized Data warehouse that contained all the promo/sales data at one place from which any information could be retrieved within no time. The retail company was also concerned about the security risks and bugs of the overloaded data in the tables.

The Solution

map-data has a team of Data Engineers with in-depth experience in enabling the clients to extract actionable insights. We helped the client to analyze to perform data analytics on huge amount of data and transform it into readable visual reports.


Our team analyzed the severity of the situation and immediately created a data flow plan to simplify the backend data tables. Data Lake/Datawarehouse was created using Matillion and Snowflake (Datawarehouse).

02. developed the data flow with the metadata approach as it reduces development time and development assets, hence making the process hassle free.


Our team created the QA job suites which eliminated the QA manual activity, hence saving the QA time and reducing the timeline from months to minutes.

04. team also integrated the main data flow built in Matillion with AWS service. This made the real time execution robust in nature for future maintenance with the tracking of real time issues.


Cloud Data Warehouse using Snowflake was built to have more control over structural and semi-structural data with extremely fast retrieving process with graphical data representation. Matillion ETL was used by our team to unleash the benefits of the cloud data warehouse and integrate it with Data warehouse/Data Lake.


Our team followed the shared model development throughout the process that minimized the number of resources as single development model can be used across the board.


Maintenance and ad-hoc changes could now be incorporated by any new authorized Developer easily. It was not possible earlier and consumed a lot of time of the data management team.