Cloud platform for data analytics

Cloud migration service enabled the company to streamline data analytics and automation within their corporate platform.

Customer

Industry

eCommerce

Region

Germany

Client since

2019

Our client is one of the largest multi-channel retailers for specialized clothing, tools, and accessories.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Challenge

The client came to us with a corporate platform characterized by non-scalability and lack of automation. It made our client turn to a cloud solution based on Power BI, which would enable secure data storage, greater opportunities for analytics, and optimization of business processes.

Solution

Innowise has migrated an existing on-premise solution to the cloud, building data marts and enhanced dashboards with analytics.

DATA MARTS

Our engineers have developed data marts: Operative, HR Management, Finance, Logistics, and E-commerce data, which refers to uploading data from different sources like internal APIs, Salesforce, and Google Analytics, transforming, and loading it into the final data storage. Initially, there was a massive piece of data to transfer. More to that, all this data was scattered. Besides, there were a lot of inconsistencies and so-called dirty data. Despite this, we have succeeded in making the transferring process as painlessly as possible.

UPDATED DASHBOARDS WITH ANALYTICS

The current solution enables data storing and keeping it consistent; the data is refreshed daily by default. The data analysis results are displayed on actionable dashboards with analytics of internal operations, HR management, finances, logistics, and marketing campaigns. Each of the users can customize the dashboards according to their needs, choose a preferred view and add required parameters. As a result, it helps to constantly monitor the situation and more quickly and effectively respond to changes.

AUTOMATION

We have implemented full-cycle workflow automation starting from data extracting and ending with data marts and dashboards creating, covering filtering, and mapping. Although data transfer was hindered by data inconsistency and some peculiarities concerning data representation (Germanic umlaut in the spelling), we arranged everything to make all data coming from different sources available within a Power BI platform.It allows our client to track the shopper’s path from the first appearing at the site (thanks to Google Analytics data) to the purchase history (thanks to the Salesforce data). This is valuable for getting more targeted campaigns based on the characteristics of the shopper’s behavior.Also, it’s beneficial for the delivery process that covers placing an order on the site, sending the notification to the logistics department to collect the order and send it to the shopper, shopper’s automatic notification, the delivery itself, and sending a form for leaving feedback on the service/ product provided.What is more, thanks to the automation works we have done, the information on goods, articles, prices, current balances, and availability in stock are synchronized between internal accounting systems and the website in real-time.

Technologies & tools

Main programming languages

Python, Scala, SQL

Library

DAX

Cloud services

Azure Data Factory, SSAS, Azure DevOps, Power BI, Salesforce Cloud

Web applications

Google Analytics

Process

TECHNOLOGY CHOICE

We recommended the technologies and services that best met the client’s needs based on the specifications and requirements. Thus, Power BI is a powerful tool for quick analysis and dashboard creation. Databricks is an enhanced Spark that enables swift and flexible data analysis and transformation with Python, Scala, R, and SQL. Azure Data Factory effectively creates pipelines from pre-built operations, reduces the number of pipeline errors, and speeds up the entire development process.

METHODOLOGY

As a methodology for the software development lifecycle, we chose Scrum daily rallies in the morning and evening, but no retro and sprints as such. The releases were made immediately after the implementation/fix of the feature. During the project, all communication between our development team and the customer was handled via Teams. Time tracking was carried out in BCS.

Each phase of development was completed with the unit and manual testing so that we could detect and fix even the most minor bugs as early as possible to prevent them from becoming problems.

Team

Team Lead

Solution Architect

Data Engineers

Business Intelligence Developers

Business Analyst

Project Manager

Results

We have created a fault-tolerant automated system for swift collecting, storing, processing, and analyzing data. In order to guarantee streamlined system operation, we did not skimp on the resources and applied extremely powerful clusters. In order to ensure system fault-tolerance, we provided maximum code cleanness with logs prominently written to immediately understand what is wrong.
The client has gained a handy cloud-powered platform with data analysis and forecasts displayed in dashboards to use this information for efficient data-driven decisions.

Project duration

October 2021 - April 2022

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