When data is fragmented, analytics becomes a slow, manual exercise: extract files, reconcile definitions, resolve duplicates, and rebuild the same reports repeatedly. Teams spend more time preparing data than using it. The result is predictable—insights arrive late, decisions rely on gut feel, and AI initiatives stall because the foundation isn’t reliable.
A multi-business organization had data across ERP, CRM, finance tools, production systems, and customer platforms. Every request for analysis required manual effort: data pulls from multiple sources, spreadsheet joins, inconsistent metric definitions, and heavy dependency on a small data team.
Key problems included:
Siloed systems and inconsistent data definitions
Different teams reported different numbers for the same metric (revenue, active customer, churn, production output) because sources and definitions were not aligned.Slow analytics turnaround time
Business queries took days because data had to be extracted, cleaned, reconciled, and validated manually—often repeating work done in previous reports.Limited trust and adoption
Without consistent governance and quality controls, stakeholders questioned the accuracy of dashboards, leading to parallel “shadow reporting.”Data pipeline fragility
ETL processes were not standardized, lacked monitoring, and broke when upstream systems changed. Data refreshes were unpredictable.AI readiness gaps
Data was not curated in a way that supported ML feature creation, experiment tracking, or scalable model deployment. Governance and access controls were also insufficient for sensitive datasets.
The organization needed a platform that enabled real-time or near-real-time insights, governed access, and created a strong foundation for AI/ML workloads.
Maayan Technologies implemented an end-to-end data platform modernization program that combined ingestion, transformation, governance, and analytics enablement—built for speed, reliability, and AI readiness.
1) Unified Data Architecture
We defined a scalable architecture that consolidated key systems into a governed, centralized platform. The design supported both batch and streaming ingestion to ensure freshness where it mattered (operations, sales performance, inventory, customer behavior).
Key components included:
Source integrations (ERP, CRM, finance, operations, logs)
Data lake / lakehouse foundation for scalable storage and compute
Curated layers for standardized analytics datasets
Secure access controls and data segmentation by domain
2) Automated Data Ingestion and Pipeline Standardization
We built robust ingestion pipelines with:
Automated scheduling and incremental loads
Schema validation and change detection
Monitoring and alerting for pipeline health
Standard patterns for onboarding new data sources quickly
This reduced pipeline fragility and ensured reliable refreshes.
3) Data Quality, Governance, and Master Data Alignment
To build trust and consistency, we implemented:
Canonical metric definitions (“single source of truth”)
Master data alignment for customers, products, vendors, locations
Quality checks (completeness, duplicates, anomalies, reconciliation rules)
Data cataloging and lineage tracking for transparency
Role-based access and policy enforcement for sensitive data
Governance made data reliable and audit-friendly without slowing access.
4) Semantic Layer and Self-Service Enablement
We created a semantic layer that exposed business-ready datasets and standardized dimensions/measures. This enabled:
Self-service dashboards without repeated manual joins
Consistent definitions across teams
Faster ad-hoc queries and drill-downs
Easier onboarding for analysts and business users
5) Performance Optimization for “Minutes, Not Days”
To cut time-to-insight, we optimized performance through:
Partitioning and clustering for high-volume tables
Caching strategies and optimized query patterns
Pre-aggregated “gold” datasets for common KPI views
Workload isolation so heavy queries didn’t affect refresh SLAs
The platform was tuned for high concurrency and rapid queries.
6) AI/ML Readiness and Feature Foundations
To support AI initiatives, we enabled:
Feature-ready datasets and repeatable transformation pipelines
Controlled access for training data
Experiment tracking and reproducibility practices
Support for deploying models into workflows (where applicable)
This ensured analytics modernization also created future AI capability.
The platform delivered measurable improvements in speed, reliability, and adoption:
Analytics time-to-insight reduced from days to minutes, enabling faster operational and strategic decisions.
Higher trust in data and dashboards through governance, quality checks, and standardized definitions.
Reduced dependency on manual reporting as self-service datasets replaced spreadsheet-driven workflows.
More reliable data refreshes with monitored pipelines and standardized ingestion patterns.
AI-ready foundation supporting feature creation, model training, and scalable ML adoption.
Improved cross-team alignment as metrics and master data became consistent across functions.
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