Data is one of, if not the most valuable asset for your company or organization. In today’s world, migrations of data for business reasons are commonplace as: Databases continue to grow exponentially that requires additional storage capacity, business switching to the cloud environment to minimize cost and system security and availability of clean and accurate data for consumption.

A well-defined data migration strategy should address the challenges of identifying source data, interacting with continuously changing targets, meeting data quality requirements, creating appropriate project methodologies, and developing general migration expertise. At Micdenlak, our data team has a combined over 200 years’ experience helping organizations in transforming legacy data system into new systems (both prem and cloud). With a multitude of storage options out there our Solution Architects at Micdenlak is here to help you make the best decision for your budget and data need.
Data is one of, if not the most valuable asset for your company or organization. In today’s world, migrations of data for business reasons are commonplace as: Databases continue to grow exponentially that requires additional storage capacity, business switching to the cloud environment to minimize cost and system security and availability of clean and accurate data for consumption.

A well-defined data migration strategy should address the challenges of identifying source data, interacting with continuously changing targets, meeting data quality requirements, creating appropriate project methodologies, and developing general migration expertise. At Micdenlak, our data team has a combined over 200 years’ experience helping organizations in transforming legacy data system into new systems (both prem and cloud). With a multitude of storage options out there our Solution Architects at Micdenlak is here to help you make the best decision for your budget and data need.

Services

Date Storage,
Migration and Security

Data
Visualization

Data as a Service
(DaaS)

Premise
Data Warehousing

Cloud
Data warehousing

Predictive
Modelling

Key Performance Indicator
(KPI) analysis