Technology, media and entertainment (M&E), telecom, and public sector organizations are expected to deliver secure, real-time, AI-powered experiences while controlling cost and complexity. Zensar’s data engineering, analytics, and AI services — combined with Databricks Lakehouse and Zensar’s accelerator ZenseAI.Data— enable these industries to modernize platforms, operationalize AI safely, and deliver measurable value, fast. From network analytics and recommendation engines to R&D acceleration and citizen services, the joint approach reduces time-to-insight and TCO while improving governance and reliability.
Why this partnership works
Databricks Lakehouse unifies data engineering, analytics, and AI on a single governed platform, eliminating silos between data lakes and warehouses. Zensar amplifies Lakehouse outcomes with industry accelerators and delivery rigor.
ZenseAI.Data automates ingestion, quality control, schema conformance, PII minimization, and DataOps orchestration — so teams focus on outcomes, not plumbing. Together, we deliver:
A single, governed source of truth across structured, semi-structured, and unstructured data
Rapid model life cycle management with MLflow — training, versioning, promotion, and monitoring
Streamlined ingestion and transformation using Delta Live Tables and Spark, wrapped in Zensar’s CI/CD-ready DataOps patterns
Enterprise-grade governance with Unity Catalog — lineage, RBAC, audit, and data contracts
Cost control and elasticity via auto-scaling compute, job orchestration, and no-copy analytics on Delta
Industry focus and outcomes
1. Telecom: Real-time intelligence for resilient networks and superior CX
Telecom operators ingest massive telemetry from RAN, core, OSS/BSS, devices, and apps. The challenge is turning signals into proactive, customer-centric actions.
What we deliver:
Network performance and predictive maintenance: Streaming metrics via Delta Live Tables; time-series anomaly detection; automated triage to reduce MTTR and outage risk.
Customer experience and personalization: Customer 360 with identity resolution, churn propensity scoring, and next-best-action recommendations.
Revenue assurance and fraud detection: Graph analytics and behavioral models to detect SIM box fraud, roaming anomalies, and subscription misuse in near-real-time.
Gen AI for ops and care: RAG assistants on curated knowledge bases to support field technicians and contact center agents with governed responses.
Role of ZenseAI.Data
Pre-built connectors and templates ingest OSS/BSS, CDR, and telemetry; apply DQ scoring and PII masking; publish curated feature stores for churn, QoS anomaly detection, and NBA models —accelerating time-to-value by 40 – 60%.
Business impact
30 – 50% faster incident detection and triage
10 – 20% churn reduction via targeted retention strategies
Reduced fraud losses with real-time controls and automated workflows
2) Media and Entertainment: Audience insights, hyper-personalization, and smarter monetization
Streaming platforms, broadcasters, and publishers need deep audience understanding and agile content pipelines.
What we deliver:
Recommendation engines and content discovery: Hybrid recommenders (collaborative + content-based), tuned with offline/online A/B testing; boosts engagement and watch-time.
Churn and lifetime value modeling: Multi-signal propensity models using play events, dwell time, device, geography, and payment behavior; targeted interventions via promos and content surfacing.
Advertising and yield optimization: Cross-channel attribution, inventory forecasting, and real-time bidding analytics — driving higher fill rates and eCPM.
Content ops and Gen AI: Automated metadata enrichment, summarization, shot detection, and highlight generation for sports/news workflows.
Role of ZenseAI.Data
Accelerates ingestion of player logs and ad events; standardizes schemas; constructs audience segments and feature stores for recommenders; enforces governance for safe, compliant personalization.
Business impact
+10 – 30% lift in engagement for personalized experiences
Reduced CAC via precise audience segments and lookalike modeling
Higher ad yield through inventory and pricing optimization
3) Technology: Accelerate R&D, ship AI safely, and scale product analytics
ISVs and enterprise tech companies need to iterate quickly on AI/ML, observe product usage, and maintain governance.
What we deliver:
Unified feature stores and experimentation: Reusable features with lineage and online/offline consistency; MLflow tracking to compare experiments and promote models.
Product telemetry and behavioral analytics: Real-time insights into user flows, drop-offs, performance, and reliability; embedding analytics into customer-facing consoles.
Secure Gen AI enablement: Curate embeddings from product docs and code repositories with strict access controls and PII stripping; deploy RAG assistants for support and engineering productivity.
MLOps at scale: CI/CD for data pipelines and models, drift monitoring, shadow deployments, and rollback safety.
Role of ZenseAI.Data
Provides telemetry connectors, schema conformance, and templated ML pipelines; sets policy-driven data minimization to ensure experimentation stays compliant and reproducible.
Business impact
Faster release cycles and higher model acceptance
360° visibility into product usage and reliability
Reduced AI risk with governed data and model operations
4) Public Sector: Secure, governed analytics for mission outcomes and citizen services
Agencies need trusted data platforms that meet stringent security, privacy, and budget constraints.
What we deliver:
Citizen 360 and service analytics: Integrate case, benefits, healthcare, and education datasets; surface insights to improve service delivery and equity outcomes with lineage and audit.
Fraud, waste, and abuse detection: Graph and anomaly detection across tax, benefits, procurement, and licensing, backed by explainable models and transparent thresholds.
Policy impact and program evaluation: Scenario modeling and causal inference to evaluate policy options and interventions.
Secure Gen AI for workforce: RAG assistants for caseworkers, investigators, and analysts; strict role-based access and redaction pipelines.
Role of ZenseAI.Data
Embeds compliance guardrails (masking, minimization, and audit) and accelerates data mart creation for case management, benefits, and procurement analytics, ensuring agencies gain trusted insights quickly.
Business impact
Faster, fairer citizen services with measurable KPIs
Tangible savings from fraud detection and prevention
Clear, defensible analytics aligned to compliance mandates
How we deliver: Zensar DataOps on Databricks Lakehouse + ZenseAI.Data
Architecture overview
Ingestion: Batch/stream sources (network telemetry, clickstreams, OSS/BSS, ERP/CRM, public datasets) via ZenseAI.Data connectors.
Bronze → Silver → Gold: Automated schema mapping, PII masking, and quality scoring before landing in Delta tables.
Governance: Unity Catalog for data/AI governance, lineage, RBAC, and audit trails.
Transformation and orchestration: Spark + Delta Live Tables; Zensar’s CI/CD pipelines, automated tests, and reusable templates.
MLOps: MLflow for tracking, registry, staging/production promotion, drift monitoring, and explainability.
Serving: SQL warehousing, Lakehouse-connected BI, model endpoints, and vector search for RAG apps.
Observability and FinOps: Cost guardrails, reliability SLAs, and pipeline health monitors.
ZenseAI.Data accelerators
Industry data models for telecom, M&E, technology, and public sector to speed domain marts.
Reusable pipelines for identity resolution, churn, recommendation, anomaly detection, and RAG retrieval.
Compliance patterns for PII/PHI minimization, encryption, masking, and role-based policies.
Implementation blueprint (90–120 days)
Discovery and value mapping (weeks 1 – 2): Prioritize 2 – 3 high-impact use cases; define target KPIs (e.g., churn reduction, MTTR, fraud savings).
Landing zone and governance (weeks 1 – 3): Set up Unity Catalog, workspaces, access policies, data contracts, and secure connectivity.
Data foundation (weeks 2 – 6): Ingest priority datasets into Delta (Bronze/Silver), build data quality checks, and establish lineage.
Analytics and AI (weeks 5 – 10): Implement domain models (recommenders, anomaly detection, propensity scoring); integrate MLflow and feature stores.
Serving and adoption (weeks 8 – 12): Publish dashboards, endpoints, and Gen AI assistants; run A/B tests; embed into operations.
Operate and optimize (ongoing): FinOps guardrails, reliability SLAs, drift monitoring, and a backlog of incremental use cases.
Security, compliance, and cost control
Security by design: RBAC, workspace isolation, token policies, encryption at rest/in transit, secrets management.
Data minimization: Pseudonymization/redaction before model training; column- and row-level access.
Audit and lineage: Full traceability for regulatory needs and internal governance.
FinOps: Auto-scaling clusters, job scheduling, storage optimization, and right-sized SQL warehousing with cost dashboards.
Measuring success
Time-to-insight: Weeks instead of months for net-new analytics.
Operational KPIs: MTTR, incident volume, QoS, service wait times.
Business outcomes: Churn, ARPU, ad yield, fraud savings, policy effectiveness.
Trust: Data quality scores, governance coverage, and model reliability.
Call to action
Ready to turn your data into outcomes?
Telecom: Start with Network intelligence + Churn reduction.
M&E: Pilot a Recommendation engine + Ad yield optimization.
Technology: Launch Feature store + MLOps for faster releases.
Public sector: Stand up Citizen 360 + Fraud detection with governed access.
We’ll tailor a 2 – 3 use-case pilot plan and deliver a solution roadmap within two weeks.