Transforming Data Challenges into Strategic Insights

Data Management for Sustainable Growth 

THE CHALLENGE

In the face of rapid growth, our client encountered a significant data management challenge, with diverse data models impeding effective analytics. Custom SQL for each model led to time-intensive development and unreliable results due to the complexity of managing numerous models.

In the face of rapid expansion, our client encountered a substantial influx of data organized across diverse models. This data structuring variation necessitated the creation of custom SQL queries for each model to conduct comprehensive company analytics. However, this approach became unsustainable over time. Basic analytics required excessive development efforts, and the complexity of multiple models often resulted in unreliable outcomes.

THE SOLUTION

To address these issues, our team at SEQTEK performed a comprehensive assessment and proposed a data warehouse as the key solution. We designed a unified data model by integrating requirements from over a hundred distinct models. Through an Extract, Transform, Load (ETL) process, we consolidated these models into a centralized data warehouse, providing a tailored storage solution.

TECHNICAL APPROACH 

Utilizing Draw.io, SQL Server Integration Studios (SSIS), SQL Server, and Management Studios (SSMS):

• Collaboration: We engaged with business stakeholders to create an Entity-Relationship Diagram (ERD) as the blueprint for our comprehensive data model.
• Integration Planning: Collaborating with relevant parties, we determined integration methods for each unique data model.
• ETL Development: Using SSIS, we implemented an ETL process based on the ERD to integrate diverse data models into the centralized warehouse.
• Business Rules: We refined and defined business rules for reporting in collaboration with business users.Reporting System Enhancement: We re-engineered SQL for the client's reporting system, drawing data directly from the centralized data warehouse.
• Automation: Leveraging SSMS, we implemented automated jobs to enhance the efficiency of the reporting solution.

THE OUTCOME

Centralizing data storage streamlined development efforts, resulting in a reliable and accurate reporting solution. Embraced by business users, this enhanced efficiency and productivity across the organization. Furthermore, the new data infrastructure positioned the client for future integration of Artificial Intelligence (AI) on a singular data model, anticipating long-term advancements and maintaining a competitive edge.