Case Study·

Accelerating Digital Transformation through Enterprise Data Lake Solution

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Enabling high value extraction from insights for a top cosmetic client through automation and machine learning

About the customer

The customer is one of the Top 10 cosmetic companies in the world and engages in the manufacturing, marketing, sale, and distribution of branded beauty products.The company is focused on deploying Machine learning techniques to improve their cash flow prediction process. One such use case is of Building Enterprise wide Data Lake solution for automating the need of predicting future cash flows and making insights more consumable. They contacted Brillio to get benefits of our expertise in Machine learning and Data Visualization space to execute the task.

Business Challenge

In the consumer products manufacturing business one has to deal with lots of suppliers, vendors and specialists for certain process. The situation demands accuracy in terms of predicting the future cash flow because it gives one the current cash position of the company. Devising an AI-based solution for predicting future cash flow would thus enable an automated and reliable way. The problem is ripe for machine learning disruption. The key challenge lies in designing a solution which is efficient, consistent, accurate and adaptable in predicting future cash flow.


Step 1: Data Ingestion

  • Ingesting data from SAP BW4HANA into Azure using MDX queries
  • Setting up complete environment, from Azure DLS, Databricks, SQL DB, VM, Logic Apps, DevOps, ADF following security and folder structures, as per guidelines
  • Used Azure Databricks for enabling Data Transformation and Machine Learning usage
  • Setup Monitoring and Logging across the ETL process to detect any failures
  • Transform hierarchical information of various dimensions into consumable format for users
  • Provided dynamic table updates using SFTP with instant auto refresh in PowerBI

Step 2: Storage, Compute & Analyze – Building Machine learning model

  • Prepared ML model which predicts future payments date
  • Provides cash flow forecast using Random Forest algorithm, with 79% accuracy.
  • Used Pyspark, Azure Databricks, MLib package, and pipeline feature to execute repeatable codes
  • Provided outputs to PowerBI in a consumable format to further enable the easy to digest visualizations on the output

Step 3: Making Data ready for consumption

  • Transformed basic dashboards into interactive valuable dashboards
  • Provided frontend homepage like SAP for ease of use to users
  • PowerBI refreshes is triggered by Azure
  • Added features such as bookmarks, toggle, date scrollers etc in the dashboard
  • Prepared dashboards which transform ML outputs in consumable visualization
  • Provided users with PowerBI App, and trained users on its

Business Benefits & Impact

Achieved 81% optimization

Transformed a completely manual effort process (8 hours) and refreshed it into a completely automatic process (1.5 hours)

Real time Insights

Auto-refresh helps in extraction of latest data for discussion with client customers. The dashboard is always updated.

79% Forecast model accuracy

Cash Forecasted numbers are now based on statistical insights rather than the judgement of the business user.

Improved Dashboards

Effective PowerBI dashboards enabling better business decisions and augmented user experience

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