Objective

To contain revenue leakage, predict demand, and optimize transportation costs by leveraging data insights, ultimately leading to healthier margins.

Value addition

Analysing the company’s data was critical for informed decision-making. The following steps were recommended:

Assessment and documentation

1. Conduct a thorough assessment and document requirements from business stakeholders, including the current technical landscape—source systems, datasets, metadata, KPIs/metrics, and current reporting.
2. Based on the discussions, recommend a future-proof technical architecture using industry best practices such as a Cloud Data Lakehouse. This architecture can:
   • Automatically collate data from source systems and store in a Data Lake
   • Transform and process data using standard and company-specific business rules
   • Create a data model and department specific datamarts
   • Generate insights through analytics (using data science techniques like Prophet, XGBoost, ARIMA, etc.) and reporting (using BI tools like Power BI, Tableau, etc.)
3. Convert analytical insights into executive summaries for decision-makers to implement changes. Monitor and track the results to refine the implementation further.

Impact

Implementing the Cloud Data Lakehouse brings a paradigm shift, enabling regular delivery of actionable insights to senior management. Other impacts included:

1. A single repository for data consolidation led to cross-functional analysis and efficient reporting with data slicing capabilities.
2. High data quality after transformation and application of business rules allows for the standardization of KPIs and metrics
3. Automatically refreshed data models optimizes the operational needs in conjunction with demand, leading to stronger margins