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Data Modernization

It’s all about easy access, manage, analyze and deploy

Data Modernization

These days organizations need a modern data architecture to apply the acumen that drives insights and action. By streamlining your approach to data and data management, you can unravel the potential of artificial intelligence to monetize data.

Data modernization means moving data from legacy databases to modern databases. It is particularly critical for any organization that needs to store unstructured data—images, customer voice audio, social media com- ments, clinical notes in health care, and so forth. Data modernization offers substan- tial cost advantages over previously used data management technologies.


Grow with the transformation process

Let you organization be agile by eliminating inefficiencies and unrequired complexities around the legacy systems.

Itelenet helps in accelerating decision-making to deliver better customer experiences, drive top-line growth, reduce costs and gain a competitive advantage. Modern data architecture ensures that your organization’s data scales and is accessible, cloud-based, and trusted. It also ensures compliance with data security and privacy regulations.

Data Modernization Methods

Data Conversion

Data conversion is the procedure of interpreting data from one format to another. As the idea itself may seem easy, data conversion is an acute step in the process of data integration. We do it easily as we guarantee the destination format supports the same feature and data structures.

Database Upgrade

Database upgrade is significant as the bugs, internal errors, and other technical issues can be recognized and resolved as soon as possible. With the use of a database upgrade, you can reduce downtime.

Data Migration

Data migration is the greatest way of transmitting data from one location to other, one format to other, or one application to other. Whenever you are in need of data migration, you can connect with us to optimize or transform the company.

Use Multiple Environments

With the right technology, it becomes easy to control the application behavior such development, staging and production. Environment variables are used to specify which application is running in, one can allow the app to be configured.

Reuse and containerize

The data analytic team can change at a lightning speed and use extremely optimized tools and processes. One of the most essential tools is the capability to reuse and containerize codes. When we talk about reusing code, we mean reuse data analytics components.

Parameterize your processing

A model is defined or represented by the model parameters. The process of training a model includes selecting the optimal parameters that the learning algorithm will use to learn the optimal parameters that correctly map the input features.