Modern Enterprise Data Storage & Analytics Transformation
Business Challenges
Key Business Challenges in the Storage landscape:
The client a leading global cloud services and data management company faced slow performance in executive dashboards, primarily due to challenges in how data was stored, structured, and accessed for analytics.
Inefficient data retrieval and processing resulted in long load and refresh times (~90 seconds), reducing decision-making agility at the CXO level.
Legacy data handling and security dependencies (e.g., reliance on external access platforms like Immuta) complicated storage governance and increased latency.
The client a leading global cloud services and data management company faced slow performance in executive dashboards, primarily due to challenges in how data was stored, structured, and accessed for analytics.
Inefficient data retrieval and processing resulted in long load and refresh times (~90 seconds), reducing decision-making agility at the CXO level.
Legacy data handling and security dependencies (e.g., reliance on external access platforms like Immuta) complicated storage governance and increased latency.
Digital Transformation
Key Digital Transformation practices:
A key part of the transformation was modernizing data storage infrastructure by integrating Snowflake, a cloud-native data storage and warehouse platform, to replace or augment legacy storage and access layers.
Snowflake was used to create optimized queries and enforce security directly at the storage layer, enabling seamless, secure data delivery to analytics tools like Tableau.
The transformation included simplifying security logic using SQL, reducing unnecessary records, and expanding storage capacity to improve performance and analytics readiness.
A key part of the transformation was modernizing data storage infrastructure by integrating Snowflake, a cloud-native data storage and warehouse platform, to replace or augment legacy storage and access layers.
Snowflake was used to create optimized queries and enforce security directly at the storage layer, enabling seamless, secure data delivery to analytics tools like Tableau.
The transformation included simplifying security logic using SQL, reducing unnecessary records, and expanding storage capacity to improve performance and analytics readiness.
Key Solutions
Cloud-Native Storage & Data Warehouse
Deployment and optimization of Snowflake as the central storage solution for large-scale data sets, improving access, performance, and governance.
Data Modeling & Security Integration
Security policies and filters were embedded at the storage query level, removing reliance on supplementary security tools and reducing latency.
Unified Data Access for Analytics
Created a single, shared storage layer that served Tableau dashboards, enabling faster, consistent, and secure access to the stored data across departments.
Deployment and optimization of Snowflake as the central storage solution for large-scale data sets, improving access, performance, and governance.
Data Modeling & Security Integration
Security policies and filters were embedded at the storage query level, removing reliance on supplementary security tools and reducing latency.
Unified Data Access for Analytics
Created a single, shared storage layer that served Tableau dashboards, enabling faster, consistent, and secure access to the stored data across departments.
Business Benefits
Key Business benefits of the practices & approaches:
Improved Storage Performance & Efficiency: Data refresh and dashboard load times dropped from ~90 seconds to ~30 seconds, demonstrating faster storage access and processing.
Simplified Data Governance: By embedding security at the storage query level, the solution reduced complexity and improved compliance without slowing performance.
Scalable Data Platform: Snowflake’s scalable storage enabled growth of data volumes without performance degradation, better supporting enterprise analytics and business intelligence needs.
Better Decision-Making: Faster and more reliable access to storage-backed insights empowered executives to make real-time strategic decisions.
Improved Storage Performance & Efficiency: Data refresh and dashboard load times dropped from ~90 seconds to ~30 seconds, demonstrating faster storage access and processing.
Simplified Data Governance: By embedding security at the storage query level, the solution reduced complexity and improved compliance without slowing performance.
Scalable Data Platform: Snowflake’s scalable storage enabled growth of data volumes without performance degradation, better supporting enterprise analytics and business intelligence needs.
Better Decision-Making: Faster and more reliable access to storage-backed insights empowered executives to make real-time strategic decisions.
Best Practices
Key best practices for the Storage domain – concise, actionable, and aligned to modern enterprise storage transformation:
Architect for performance and scale – Use elastic, cloud-native storage that scales compute & capacity independently.
Unify storage to eliminate silos – Consolidate data sources into a central, governed data store.
Enforce security at the storage layer – Apply RBAC, encryption, masking, and policy controls closest to the data.
Optimize structure & data modeling – Partition, cluster, index, and compress data for faster access and lower cost.
Automate data lifecycle & tiering – Classify hot/warm/cold data and automate archiving, retention, and purging.
Adopt metadata & catalog governance – Maintain standardized metadata, schema management, and lineage tracking.
Instrument continuous monitoring – Track latency, throughput, storage usage, cost, and access patterns in real time.
Design for interoperability – Ensure storage works seamlessly with BI, ETL, AI/ML, and analytics tools.
Implement zero-trust data access – Minimize unnecessary exposure and enforce least-privilege read/write access.
Enable controlled self-service access – Provide governed, role-based access for analysts and business units to improve agility.
Architect for performance and scale – Use elastic, cloud-native storage that scales compute & capacity independently.
Unify storage to eliminate silos – Consolidate data sources into a central, governed data store.
Enforce security at the storage layer – Apply RBAC, encryption, masking, and policy controls closest to the data.
Optimize structure & data modeling – Partition, cluster, index, and compress data for faster access and lower cost.
Automate data lifecycle & tiering – Classify hot/warm/cold data and automate archiving, retention, and purging.
Adopt metadata & catalog governance – Maintain standardized metadata, schema management, and lineage tracking.
Instrument continuous monitoring – Track latency, throughput, storage usage, cost, and access patterns in real time.
Design for interoperability – Ensure storage works seamlessly with BI, ETL, AI/ML, and analytics tools.
Implement zero-trust data access – Minimize unnecessary exposure and enforce least-privilege read/write access.
Enable controlled self-service access – Provide governed, role-based access for analysts and business units to improve agility.
Client
A leading global cloud services and data management company
Project
Modernizing Enterprise Storage for AnalyticsA strategic initiative to overhaul the client’s data storage and retrieval framework by adopting Snowflake for scalable cloud storage, optimizing data flows for performance, embedding security at the storage layer, and enabling cross-departmental analytics readiness. This project balanced storage performance, governance, and business decision-support needs.
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