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Enterprise Data Management

Strategic Roadmap 2025-2029

From Legacy Risk to AI-Enabled Innovation

Obsolescence Risk

  • Informatica PowerCenter (End-of-Support March 2026)
  • Informatica TDM (End-of-Support March 2026)
  • MicroStrategy Enterprise Platform (End-of-Support December 2028)

Technical Risks

  • High latency, brittle performance with on-prem SQL Server
  • No horizontal scaling capability
  • Zero native AI/ML capabilities
  • Observability predicated mostly on email alerts
  • File-based ingestion only, no API support

Organizational Status

  • Reactive firefighting, chasing DQ issues in source systems
  • Legacy Dev/Ops separation model
  • Data retention execution pending
  • No self-service analytics for business users
  • Manual deployments, no AI-accelerated development

Legacy Architecture Constraints

Platform

On-premises SQL Server VMs, not AI-enabled

ETL

Informatica PowerCenter, proprietary & EOL

Scaling

Tightly coupled compute & storage

Data Types

Structured only, no semi/unstructured

Quality

No DQ metrics or dashboards, manual resolution

Lineage

Azure Purview not operationalized, blind spots, manual workarounds

Governance & Compliance Gaps

Data Retention

Level 1/2 maturity - regulatory risk

Test Data Management

Informatica TDM EOL March 2026

Reference Data

Managed in Excel workbooks

Data Stewardship

Mostly focuses on retention today

The Imperative

Transform from operational firefighting to AI/ML enablement

Migrate from proprietary legacy to cloud-native lakehouse

Evolve from reactive support to strategic innovation

Three Strategic Pillars

P1

Platform Modernization

Scalable, compliant foundation

P2

Advanced Capabilities

AI/ML tools for competitive advantage

P3

Enterprise Governance

Stewardship, security, modern ops

Platform Modernization

Databricks Pilot Phase

  • Environments deployed, CI/CD scaffolding in place
  • Most complex PowerCenter workflows converted in Pilot
  • Transformation logic in open languages (SQL, Python)
  • Target: Full migration by April 2026

Databricks Lakehouse

Open standards (Delta Lake, Apache Spark)

Industry-standard open formats and frameworks

Cloud-native, horizontal scalability

Scale compute and storage independently

Native AI/ML capabilities

Built-in machine learning and AI tools

Unified governance (Unity Catalog)

Centralized data governance and security

Data Lineage

Track lineage to ensure data provenance and traceability

Data analysis and reporting

Optimized engine for data warehousing workloads.

Lakehouse Architecture

Bronze Layer

Raw ingestion - all source data

Silver Layer

Cleansed, validated, conformed

Gold Layer

Business-ready aggregations

Unity Catalog

Unified governance & lineage

Delta Lake

ACID transactions, time travel

Auto-Scaling

Independent compute & storage

Future State: AI/ML Enabled

MLOps Scaffolding

Enterprise governance for predictive models

Unified Platform

Single platform for data & AI (replace AzureML)

Databricks One

Democratize data and AI for business users

Pervasive AI Assistance

Context aware AI assistance for development, data wrangling, and analytics

AI/BI Genie

Enabling users to ask data questions in natural language and receive instant insights

Agent Bricks, Databricks Apps, Databricks Lakebase

Build apps and production AI agents optimized on Foresters data

Future State: Governance Maturity

Unity Catalog

Centralized metadata, RBAC, automated lineage

Data Quality

PySpark-based data validation

Data Retention

Semi-automated disposition

Reference Data

GenAI and spec-driven development of replacement solution

TDM Replacement

ConfiData for masking & subsetting

Data Council

Cross-functional governance oversight

Future State: Agile POD Model

Current State

  • Traditional Dev/Ops split
  • 4-person external support, heavy reliance on runbooks
  • No real 24/7 coverage, EST hours on-call only
  • Reactive support mode, limited continuous improvement
  • 4-person internal team, heavy EST hours support engagement
  • SoD is human-based, relies on human-to-human handoffs

Future State

  • Integrated Agile POD model
  • DevOps automation (CI/CD), path to production via pipeline
  • 24/7 core team (5 FTE), floater team for additional development support
  • Proactive innovation focus, continuous improvement and cost optimization
  • Internal team focused on strategic transformation
  • SoD is system-based, automated, auditable, and immutable

Organizational Evolution

Current

Data Warehouse and ETL focused

Near-Term

Expanded influence across enterprise data strategy

Target

Unified Data Strategy and Ethical Data Stewardship across the Enterprise

Roadmap Phases (2025-2029)

Phase 1: Transition & Foundation

2025-2026

  • Databricks migration
  • TDM replacement
  • Agile POD model launch
  • Manual Data Disposition Implementation
  • Transitory Data Disposition
  • MLOps and Predictive Model Governance
  • Refactor Operational .NET Apps
  • Develop GenAI/Spec-Driven Reference Data Solution
  • MicroStrategy Cloud Migration
  • Document data models in Unity Catalog

Phase 2: Acceleration & Maturity

2027

  • Establish Data Governance Council
  • Evaluate DQX Data Quality Framework
  • Partially Automated Data Disposition
  • Implement Reference Data Service for centralized consumption
  • Unstructured Data Disposition
  • DQ Dashboard Implementation
  • Automate data scanning and classification
  • Advanced Analytics Democratization
  • Introduce Delta Live Tables Expectations for DQ

Phase 3: Innovation & Leadership

2028-2029

  • Approval workflow to data owners for access to specific data sets
  • Automate the process for auditing access to Enterprise Data Environment
  • Implement automated metadata-driven ingestion framework
  • On-Demand Data Extraction Service
  • Implement automated metadata-driven ingestion framework
  • Fully Automated Data Disposition
  • Streaming Data Ingestion/Micro-batch capabilities
  • Enterprise DQ Dashboard and continuous data profiling
  • Document Management Integration
  • Monitor user actions to suggest improvements and, with user approval, create user stories
  • Central Metadata Application for democratized understanding
  • Implement attribute level data masking using Unity Catalog

The Journey Ahead

From Legacy Risk to AI-Enabled Innovation

A modern, cloud-native lakehouse foundation
enabling AI/ML capabilities, advanced governance,
and organizational transformation

Space/P: Pause/Resume • Arrows: Navigate • R: Restart • O: Overview (zoom out to see all slides)