Our data centralization methodology
A step-by-step approach to centralizing data sources for cost savings, improved decision-making, and leveraging enhanced platform capabilities.
Discover & Plan
Establish a clear roadmap for data centralization, ensuring alignment with business needs and data quality.
Step 1: Define Data Strategy & Business Objectives
Actions:
Identify key business objectives (e.g., reducing storage costs, improving analytics, enhancing AI capabilities).
Assess current data sources (CRMs, ERPs, marketing platforms, IoT, financial systems, etc).
Establish KPIs to measure success (e.g., storage cost savings, data retrieval speed, AI model accuracy).
Outcome:
A clear roadmap for data centralization tailored to business goals.
Step 2: Assess and Cleanse Existing Data
Actions:
Conduct a data audit to identify redundant, outdated, or inconsistent records.
Apply data deduplication techniques using Databricks Auto Loader & Delta Lake.
Standardize naming conventions, data formats, and schemas across sources.
Outcome:
High-quality, structured data ready for consolidation.
Step 3: Choose the Right Data Architecture (Lakehouse Approach)
Actions:
Adopt a Data Lakehouse Model (Databricks) to merge structured and unstructured data.
Implement Delta Lake for versioning, ACID transactions, and schema enforcement.
Select a cloud provider (AWS, Azure, GCP) to host the centralized data warehouse.
Outcome:
A scalable, cloud-based lakehouse architecture optimized for analytics.
Build & Integrate
Implement scalable pipelines, optimize data storage, and enable analytics.
Step 4: Implement a Scalable Data Ingestion Pipeline
Actions:
Use Databricks Auto Loader to ingest data from multiple sources (databases, APIs, streaming data, IoT).
Enable real-time data streaming using Apache Kafka or Delta Live Tables.
Establish batch processing pipelines for periodic data ingestion.
Outcome:
Automated data flow from disparate sources into a single unified system.
Step 5: Optimize Data Storage to Reduce Costs
Actions:
Tiered Storage: Store frequently accessed data in high-performance tiers and move historical data to lower-cost options (AWS S3, Azure
Blob Storage).
Blob Storage).
Data Compression: Use Parquet format to reduce storage footprint.
Lifecycle Policies: Automate archiving and deletion of outdated data.
Outcome:
Significant cost savings with intelligent storage management.
Step 6: Ensure Security, Compliance & Governance
Actions:
Implement RBAC (Role-Based Access Control) and attribute-based security.
Use Unity Catalog for centralized data governance and audit tracking.
Ensure GDPR, CCPA, and SOC 2 compliance with automated policy enforcement.
Outcome:
Secure and compliant data infrastructure with controlled access.
Optimize & Scale
Strengthen security, governance, and scalability for long-term success.
Step 7: Enable Data Analytics & AI for Better Decision-Making
Actions:
Use Databricks SQL for business intelligence and reporting.
Implement AI/ML models for customer analytics, predictive forecasting, and anomaly detection.
Provide real-time dashboards with Power BI, Tableau, or Looker.
Outcome:
Data-driven decision-making with AI-powered insights.
Step 8: Train Teams & Continuously Optimize Workflows
Actions:
Conduct Databricks training sessions for analysts, engineers, and decision-makers.
Establish data governance policies to maintain high data integrity.
Continuously monitor and optimize performance using Databricks performance tuning tools.
Outcome:
A well-adopted, optimized, and scalable data ecosystem.
Step 9: Scale Data for Use Case Development & Implementation
Actions:
Enable real-time and historical data availability for AI/ML and business use cases.
Optimize compute resources for large-scale model training and data analytics.
Implement multi-region and multi-cloud scalability for enterprise-wide data accessibility.
Outcome:
A fully scalable data ecosystem ready for enterprise AI, advanced analytics, and operational efficiencies.
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