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Data Strategy Implementation
Objective
Implement a common data layer in EDL following the best practices of AWS and Databricks to ensure that current and future business needs for data and analytics are met across the various enterprise teams.
Industry: Agriculture and Construction Machinery Manufacturing
Services: Transformation Enablement
Technology: Data Architecture and Data Management
Increased sales volume by reporting credit exposure on a deal-by-deal basis thereby identifying customers for upsell.
Reduced risk by predicting the likelihood of credit default and making better credit decisions via efficient risk scoring models and better Loss Modeling models.
Improved revenue and profit margin by leveraging Cost Modeling/ Forecasting models.
Our Client
Our client is a Fortune 100 financial services leader who specializes in agricultural financing and equipment leasing, with a strong history of supporting farmers and rural communities worldwide.
Challenge
The financing unit of a global agricultural manufacturing company is on a digital transformation journey, with a data architecture moving from on-prem to cloud, and is facing several challenges leveraging the potential of their data assets
- Data Architecture not optimized for reporting and advanced analytics needs – practitioners spend 80% of time on data prep and blending vs analytics
- Analytics is limited, poor data quality, availability and usability
- Multiple legacy data architectures result in high data latency, significant costs, and poor data searchability and understanding
Approach
- Identify data / process requirements and gaps in analytics capabilities and technical performance.
- Implement frameworks, processes and standards to optimize data ingestion, discovery, and orchestrate data across multiple layers.
- Setup up multiple data centric product teams using agile methodology to deliver incremental value.
Outcomes
- Established 3 product centric teams covering Customer application, Account agreement and Dealer and Sales & Marketing.
- Built Ingestion frameworks to ingest and transform data from various source systems to Bronze, Silver and Gold layers. Frameworks include time-based and event-based triggers.
- Implemented automated data quality validation processes via Collibra DQ.
- Executed on 2-year roadmap using Agile methodology, adjusting as needed to meet business priorities and delivering value incrementally.
Impact
- Increased sales volume by reporting credit exposure on a deal-by-deal basis thereby identifying customers for upsell.
- Reduced risk by predicting the likelihood of credit default and making better credit decisions via efficient risk scoring models and better Loss Modeling models.
- Improved revenue and profit margin by leveraging Cost Modeling/Forecasting models.
- Enable market growth by identifying new customers, provide attractive financial incentives to them, and retaining valuable customers.
- Improved customer experience by significantly reducing the time taken to target existing customers for a credit limit increase.
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