#016 10 Ways to use SAFe, Scrum & Kanban to STREAMLINE TOOL usage
Focus on Deluge of Tools in Data Platforms related to FinTech Domain
1️⃣ SAFe Portfolio Alignment → Remove Redundant AI Use Cases Across Platforms
Problem:
Each LOB (front office, risk, compliance) builds separate AI models on Databricks using the same client data but different MDM feeds.
Agile Practice:
🧭 SAFe Portfolio Kanban + Lean Business Case + Value Stream Mapping
Platform Application:
Informatica MDM: Standardize Legal Entity Master across programs.
Databricks: Single feature store for ML pipelines.
Snowflake: Centralized reference data.
Cuts Clutter:
Eliminates duplicate pipelines and datasets.
Outcome: Reduced 5 redundant AI projects → 1 unified “Client Intelligence Platform.”
2️⃣ Scrum Backlog Refinement → Stop Tool & Library Overload
Problem:
Data teams experiment with multiple AI libraries (TensorFlow, PyTorch, HuggingFace) and DBs (Milvus, Pinecone, Neo4j) in parallel, creating chaos.
Agile Practice:
📋 Scrum Backlog Refinement + Definition of Ready (DoR) + Architectural Owner input
Platform Application:
Databricks MLflow: Enforce a single model tracking platform.
Kafka: Streamlined ingestion schema versioning via Schema Registry.
Cuts Clutter:
Prioritizes features that deliver measurable value, not hype.
Outcome: 30% reduction in DevOps maintenance overhead.
3️⃣ Kanban Flow Visualization → Unblock Invisible Data Bottlenecks
Problem:
Kafka → Databricks ETL → Snowflake chain hides blocked transformations.
Agile Practice:
📊 Kanban Board with WIP limits (Raw, Staged, Curated, Published).
Platform Application:
Kafka Topics: Visualize message lag by partition.
Databricks Jobs: Map stuck notebooks to backlog items.
Snowflake Views: Mark dependency failures.
Cuts Clutter:
Reveals where latency or manual handoffs occur.
Outcome: Reduced average data delivery time from 8 hrs → 1 hr.
4️⃣ SAFe System Demos → Prevent Big-Bang AI Integration Failures
Problem:
ML model built on Databricks fails when integrated with downstream Informatica MDM APIs or Snowflake dashboards.
Agile Practice:
🧩 SAFe System Demo after every PI (Program Increment).
Platform Application:
MDM APIs + Databricks MLflow + Power BI: Integrate demos end-to-end.
Cuts Clutter:
Early integration testing → removes “last sprint” chaos.
Outcome: 60% fewer post-deployment integration issues.
5️⃣ Scrum Definition of Done (DoD) → Control Data & AI Technical Debt
Problem:
Incomplete pipelines — no lineage, documentation, or validation in production.
Agile Practice:
✅ DoD includes: Data profiling, lineage tracking, schema validation, MLOps versioning.
Platform Application:
Informatica EDC (Enterprise Data Catalog): Lineage enforcement.
Databricks MLflow: Version + audit trail.
Snowflake: Validation scripts tied to DoD checklists.
Cuts Clutter:
Every increment is production-grade.
Outcome: Predictable, auditable deliveries → compliance-ready AI.
6️⃣ Kanban Metrics → Quantify and Eliminate “Dead Data”
Problem:
Massive pipelines generate terabytes of unused or orphaned data tables.
Agile Practice:
📈 Kanban flow analytics + Cumulative Flow Diagrams on data product lifecycles.
Platform Application:
Databricks + Unity Catalog: Track access frequency.
Snowflake Usage Stats: Identify dormant tables.
Cuts Clutter:
Decommission low-value datasets → reclaim cloud spend.
Outcome: 25% cost reduction on cloud storage.
7️⃣ SAFe Inspect & Adapt → Simplify Toolchain Sprawl
Problem:
Teams use Databricks, Snowflake, Informatica, and AWS Glue redundantly.
Agile Practice:
🔍 Inspect & Adapt (PI-level retrospective) + Enabler Epics for rationalization.
Platform Application:
Consolidate Glue ETL → Databricks Delta pipelines.
Move static feeds → Kafka Streams where real-time needed.
Cuts Clutter:
Rationalizes overlapping tools based on ROI & latency needs.
Outcome: Cleaner architecture diagram; 20% fewer tool licenses.
8️⃣ Scrum of Scrums → Synchronize Data, AI, and DevOps Streams
Problem:
Data pipeline, model training, and deployment teams operate in isolation → version mismatches.
Agile Practice:
🤝 Scrum of Scrums with Data, AI, DevOps leads weekly.
Platform Application:
Informatica MDM: Define canonical master keys.
Databricks MLflow: Align model registry versions.
Kafka: Sync event schema evolution across teams.
Cuts Clutter:
Creates a “single version of data truth.”
Outcome: Model reproducibility and traceability improved 50%.
9️⃣ Kanban Continuous Flow → Reduce Manual AI Retraining Delays
Problem:
AI models drift silently; retraining requires manual trigger via ticketing.
Agile Practice:
⚙️ Continuous Flow with automated gates triggered by drift metrics.
Platform Application:
Databricks Jobs + MLflow: Monitor model accuracy.
Kafka: Event-driven retraining triggers.
Cuts Clutter:
Removes human dependency and redundant revalidation.
Outcome: Continuous learning → stable and current AI predictions.
🔟 SAFe Architectural Runway → Stop Over-Engineering in Data Platforms
Problem:
Overdesigned data lakes with 200 unused tables “for future AI.”
Agile Practice:
🛠️ SAFe Architectural Runway + Enabler Epics.
Platform Application:
Snowflake + Databricks Delta: Build only required zones for near-term epics.
Kafka: Add topics on demand, not “just in case.”
Cuts Clutter:
Encourages incremental, value-based architecture evolution.
Outcome: Agile, lean data architecture → faster time-to-market
.