case study

Business impact

Detailed Use Cases & Benefits

Explore how centralized data platforms drive efficiency and cost savings across your organization's critical business functions.

Optimizing Storage Costs with Data Deduplication & Tiering

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Use Case:

Many organizations store duplicate, outdated, and redundant data across multiple databases, increasing storage costs.

Without automated lifecycle management, businesses retain unnecessary historical data, incurring extra cloud storage expenses.

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How Centralization Helps:

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Data Deduplication & Compression – Removes redundant data, reducing overall storage footprint.

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Tiered Storage Strategy – Stores frequently accessed data in high- performance storage and archives old data in low-cost storage.

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Automated Lifecycle Management – Deletes or archives unused data, optimizing storage efficiency.

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Potential Cost Savings:

30-50% reduction in cloud storage costs by eliminating redundant data and optimizing storage tiers.

20-30% savings in data retrieval costs by using optimized query execution.

Improved Decision-Making with AI-Driven Business Intelligence

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Use Case:

Decision-makers lack real-time access to unified data, leading to delayed and inaccurate business insights.

BI teams spend 40%+ of their time manually consolidating reports across different systems.

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How Centralization Helps:

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Single Source of Truth (SSOT) – Real- time, unified dashboards for sales, marketing, and operations.

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Automated AI-Powered Insights – Uses Databricks ML for predictive analytics, reducing reliance on manual analysis.

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Self-Service Analytics – Business users access up-to-date reports without IT intervention.

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Potential Cost Savings:

30-40% reduction in manual reporting efforts, leading to increased productivity.

20-25% savings in decision-making efficiency, optimizing business strategy execution.

Enhanced Customer Personalization & Increased Revenue

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Use Case:

Disjointed customer data across CRM, eCommerce, and marketing platforms limits personalization.

Companies lose 10-20% in potential revenue due to ineffective customer targeting.

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How Centralization Helps:

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360-Degree Customer View – Unified customer profiles integrating transaction, behavioral, and demographic data.

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AI-Driven Personalization – Recommender systems enhance targeted marketing and product recommendations.

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Real-Time Customer Insights – Faster response to customer trends and purchasing behavior.

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Potential Cost Savings:

15-25% higher conversion rates from personalized marketing.

10-20% increase in customer lifetime value (CLV) through targeted engagement.

Optimized Supply Chain & Inventory Management

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Use Case:

Companies face 10-30% excess inventory costs due to inaccurate demand forecasting.

Supply chain inefficiencies lead to delays and increased logistics costs.

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How Centralization Helps:

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AI-Powered Demand Forecasting – Uses historical data and real-time sales trends to predict stock needs.

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Inventory Optimization – Reduces overstock and understock situations.

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Real-Time Logistics Tracking – Provides up-to-date shipment and supplier analytics.

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Potential Cost Savings:

20-30% reduction in inventory carrying costs.

10-15% savings in logistics expenses through optimized routing and warehouse management.

Fraud Detection & Compliance Cost Reduction

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Use Case:

Financial fraud costs businesses 3-5% of annual revenue.

Compliance fines and security breaches add unexpected expenses.

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How Centralization Helps:

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Real-Time Anomaly Detection – AI-driven fraud detection models flag suspicious transactions.

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Automated Compliance Auditing – Ensures data security policies align with GDPR, CCPA, and SOC 2.

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Role-Based Access Controls (RBAC) – Reduces unauthorized data access risks.

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Potential Cost Savings:

30-50% reduction in fraud losses through early detection.

25-40% savings on compliance audit costs via automation.

IT & Infrastructure Cost Reduction

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Use Case:

Maintaining fragmented IT systems leads to high operational costs and inefficiencies.

Companies pay 20-40% extra on data processing due to inefficient pipelines.

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How Centralization Helps:

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Eliminates Redundant Infrastructure – Consolidates multiple data platforms into a single cloud-based solution.

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Auto-Scaling Cloud Computing – Dynamically adjusts compute resources based on demand.

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Serverless Processing & Cost Optimization – Uses cost-efficient Databricks clusters.

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Potential Cost Savings:

30-40% savings on cloud and IT infrastructure costs.

20-30% reduction in data processing expenses via efficient workload execution.