Prior Situation / Scenario:

• Business decisions were not based on data
• Many data sources were dispersed and not fully integrated
• Different reports measure the same business KPIs (Key Performance Indicators) across the departments
• Complexity to implement predictive ML (Machine Learning) models

Client Challenges:

• Develop a data centric organization strategy
• Define and prioritize Analytics use cases in order to improve business decisions, processes and reduce operation costs

Strata Solution/ Key Enablers:

• Workshops to prioritize use cases, knowledge sharing to educate on importance of data and capabilities of advanced analytics
• Set up a cross functional team to develop data driven use cases
• Design and implementation of ML models for collection, fraud prevention, preventive maintenance and demand planning

Outcome:

• Data availability for advanced analytics and reporting
• Business decision-making based on KPI results
• ML models for collection and fraud prevention, demand planning and preventive maintenance
• Data driven corporate culture improvement