we established a centralized data lake for one of the largest courier delivery services which helped construct interactive decision boards to decrease time to truth and increase actionability

Challenge

INDUSTRY OVERVIEW

The client is one of a largest Indian courier delivery services company which handles 12 - 15 million shipments every month. wanted to fully utilize the enormous information they owned to make educated judgements through focused analysis since they recognize the value of data and how important a part it plays in the decision-making process.

CHALLENGE

To construct automated, interactive decision boards that decrease the time to truth and increase actionability, as well as to establish a central data lake and warehouse to do away with manually produced MIS reports.

Gaps in the current system:

Significant Manual Effort Involved: Most reports were manually generated and refreshed making them vulnerable to human errors.
Unoptimized Architecture: The architecture In place, which was serving well at the moment, was not in line with the rapid growth in data volume that was expected by the company. AWS best practices were not being followed in many cases.
Slow Report Generation: Before Ganit came, they automated some of the reports with existing vendors, which took approximately 5 hours daily for data movement and backend processing

Why were we brought in?

Ganit has made a significant dent in various industries using data science and analysis. Ganit partners with clients to translate their data into a tangible, insightful plan of action that delivers on a measurable impact to the clients’ topline & bottom-line growth.

Our approach

Methodology

For this particular solution, we have successfully helped the client to migrate from their SAP on-premises/SAP cloud Datawarehouse system to a robust cloud-based architecture with AWS Redshift as the core data warehousing tool with integrations to S3, Glue for data ingestion part and BI tools like tableau, quick sight for data consumption/visualization part.

We have achieved efficiency through a simple yet powerful architecture, which requires minimal manual input from the client’s end. As an overview of the process, we used S3 as our source repository, created glue jobs and lambda functions for ETL and then stored the data in redshift which is then used to create multiple views and reports facilitating decision making for the client.

Features of the tool

Multiple decision board views to enhance decisions.
Reduced data load for daily migration.
Faster availability of refreshed dashboards.

A valuable difference

Impact

Several decisions take place by daily analysis of the reports which help them to increase sales and revenue across several regions.
As process was automated the data will be up to date in the dashboards without any human intervention
Better visibility of daily reports and more reliable dashboards