Success Story | Intelligence

Getting rid of Data Silos and Re-Engineering the Business Model of a Healthcare Provider

Success Story | Intelligence

Getting rid of Data Silos and Re-Engineering the Business Model of a Healthcare Provider

Our client provides healthcare services that encompass education, prevention, early intervention, comprehensive treatment options, rehabilitation, and wellness maintenance for people of all ages.

How Did It All Start

As a healthcare provider, the client constantly stores and processes medical data of various sizes, types, and frequencies. Their transaction database was built on IBM DB2 and their analytical database used SQL Server. They also used multiple 3rd party database tool providers.

The client’s data science team faced multiple problems, such as data unavailability, data inconsistency, and data fragmentation. Multiple data sources also led to data duplication. All the aforementioned issues, plus the fact that there was no documentation or data dictionary, made it extremely difficult for the client to maintain data quality.

The client approached TVS Next with a requirement of migrating and modernizing their current data warehouse and BI platform to Google Cloud Platform.

What Did We Do

TVS Next supported the client’s data warehouse requirements by assigning DB experts to develop, maintain, and support their systems. The client’s tech stack consisted of SQL Server, SSIS, SSRS, SSAS, Alteryx, and Tableau. As an alternate, Google Cloud Platform’s Cloud Storage, Big Query, and Looker were identified for data lake, data processing, and BI.

Then, TVS Next formulated a six-step plan to execute the migration efficiently. The six steps were:

Document the existing solution

Determine the modernization approach

Capture the development & deployment processes

Prioritize datasets for migration

Define the timelines and budget

Determine the outcomes

Architecture

The Business Outcome

The modernization project brought in multiple benefits, such as

80%

reduction in computing costs

35%

reduction in the time required to produce reports

35%

reduction in implementation time

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