Applied research · Data infrastructure · Seeking funding
AI-Ready Enterprise Data Lake
Transforming fragmented legacy data into a secure, centralized, AI-ready data lake with governance, privacy, and analytics foundations.
- Data engineering
- Governance
- Seeking funding
Transforming fragmented legacy data into a secure, centralized, AI-ready data lake with governance, privacy, and analytics foundations.
From silos to AI-ready data
- Step 1
Collect & ingest
Pull structured and unstructured data from databases, documents, spreadsheets, and archives.
- Step 2
Clean & classify
Standardize schemas, enrich metadata, and enforce quality and access policies.
- Step 3
Analyze & build
Enable reporting, machine learning, and generative AI on a unified governed platform.
Bangladesh context
Bangladesh context
Bangladesh's public and enterprise sectors operate across 50+ ministries/divisions, many state-owned enterprises, and a wide mix of siloed databases, spreadsheets, paper archives, and legacy applications. Initiatives such as Digital Bangladesh, the National Data Centre, and a2i have accelerated digitization, while mobile connectivity has surpassed 180 million connections and digital services continue to expand rapidly. Institutions increasingly need architectures that align with national cybersecurity expectations and emerging data-protection requirements while making historical records usable for analytics and AI.
Research objectives
- Ingest heterogeneous legacy sources into a governed, searchable data lake.
- Standardize schemas, metadata, and classification for analytics and ML readiness.
- Enforce role-based access, audit trails, and privacy-preserving data handling.
- Deliver reference pipelines for reporting, machine learning, and generative AI workloads.
Methodology
- 01
Assess source systems and define ingestion, cleansing, and lineage requirements.
- 02
Implement batch and incremental pipelines for structured and unstructured data.
- 03
Build a metadata catalog, data-quality checks, and access-control policies.
- 04
Validate downstream analytics and ML use cases on unified datasets.
Expected outputs
- A reference enterprise data-lake architecture tailored to Bangladeshi institutional constraints.
- Ingestion and governance tooling for mixed legacy sources.
- Pilot dashboards, analytics workflows, and ML-ready datasets.
- Documentation for security, privacy, and operational handover.
Seeking funding
MIRAI Lab is seeking funding to build a reference architecture and run a first institutional pilot that demonstrates how fragmented legacy data can become a secure, AI-ready foundation.
Overview
This project will transform an organization's fragmented legacy data into a secure, centralized, and AI-ready data lake. Data from databases, documents, spreadsheets, applications, and historical archives will be collected, cleaned, standardized, classified, and integrated into a unified platform. The data lake will support advanced analytics, reporting, machine learning, and generative AI applications while maintaining strong data governance, privacy, access control, and regulatory compliance.
The project enables organizations to unlock valuable insights from existing data and build a reliable foundation for future AI initiatives.
Team
Project lead: MIRAI Lab
Publications and datasets
Research papers, models, and open datasets will be shared here as they are released.
Project updates
Milestones, fieldwork notes, and lab announcements will appear on our notices page.
View notices →