How We Tailor Data Management Solutions to Specific Industry Needs
Mille Solutions has practical experience in 30+ domains, including healthcare, banking, lending, investment, insurance, retail, ecommerce, manufacturing, transportation and logistics, energy, professional services, and more.
Table of Contents
Data Management Components That We Cover Separately and in a Bundle
Data governance
- Drawing up data governance standards and policies to ensure data availability, integration, quality, security, proper usage, etc.
- Evaluating the existing data governance standards and policies.
Data architecture
- Designing data architecture to govern how data is captured, integrated, stored, analyzed, and used.
- Auditing data architecture to align it with the enterprise strategy.
Data integration
Consolidating data from disparate data sources with extract, transform, load (ETL) or extract, load, transform (ELT) processes and data virtualization.
Data quality management
Data cleansing activities, data enrichment and regular data quality assurance.
Data storage
Designing, implementing and supporting storage solutions for datasets of varying scale and format.
Reference and master data management
Enabling data consistency and quality across transactional and business intelligence systems with data profiling, data deduplication and standardization, etc.
Metadata management
Designing and populating metadata repositories with metadata to ensure localization of a data asset, data lineage, etc.
Data security
Setting up data security practices and regular BI and DWH risk assessment to prevent unauthorized data access and inappropriate data usage.
Data migration and backup
Moving your data from one system to another for ensured efficiency and security with preliminary data assessment, data migration automation, and data completeness evaluation.
Our Selected ITSM Projects
Centralized Data Repository for a Multinational Corporation
- Design a scalable data architecture integrating diverse data sources.
- Implement ETL (Extract, Transform, Load) pipelines for data cleaning and migration.
- Ensure secure access controls and compliance with data privacy regulations (e.g., GDPR, CCPA).
Cloud Data Migration for a Manufacturing Company
- Conduct a data audit to identify critical datasets and legacy systems.
- Plan and execute a phased migration strategy using platforms like AWS, Azure, or Google Cloud.
- Implement data encryption and monitoring tools to secure data during and after migration.