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Data Strategy
Objective
Develop a data strategy and 2-year phased roadmap with a central vision and foundation to ensure that future business needs for data and analytics are met
Industry: Manufacturing
Services: Strategic Advisory
Technology: Data Architecture and Data Governance
Increased revenue and profit margins due to better conversion rates, dealer effectiveness, probability to finance/ return, and merchant analytics.
Reduced Risk due to better application risk analytics, credit scorecards, equipment analytics, and accurate machinery valuation.
Reduced costs via predictive maintenance, staffing optimization, demand forecasting and faster asset remarketing.
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
Assess gaps in analytics capabilities, architecture, data delivery processes, and technical performance. Recommend and implemented new technology frameworks, processes and standards to optimize data ingestion, discovery, and speed-to-market of reporting and analytics.
Outcomes
- Established roadmap that delivers the future state of data
- Modern architecture allows the business to accelerate transforming into a “smart industrial” enterprise with advanced data and analytics capabilities
- Mature data governance and management processes enable analytics at industrial scale
Impact
New architecture and frameworks enable a wealth of new analytic opportunities.
- Increased revenue and profit margins due to better conversion rates, dealer effectiveness, lease / fleet / merchant analytics, probability to finance / return, advanced “next best offer”, and merchant analytics.
- Improved customer experience from more granular segmentation, lifetime value analytics, and optimal customer contact analytics.
- Reduced Risk due to better application risk analytics, credit scorecards, equipment analytics, and accurate machinery valuation.
- Reduced costs via predictive maintenance, staffing optimization, demand forecasting and faster asset remarketing.
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