Sencinet — Enterprise Data Architecture and Data Lake

The challenge

Sencinet needed to unify its data sources to build management KPIs for its projects area. Teams faced long wait times to access key information and relied on manual processes to integrate data, which led to inconsistencies, rework, and high operating costs in their dashboards. The lack of data governance limited the strategic use of information in a large-scale telecommunications operation.

The solution

We implemented a data architecture for automated data ingestion, transformation, cleansing, and loading into a data lake. We built the reports in Power BI and Superset, reducing operating costs and speeding up access to strategic information for both project managers and clients. The new environment made it possible to scale new use cases and quickly add new sources without affecting the performance of the analytics layer.

Why does a well-designed data architecture transform how a company is managed?

Many organizations accumulate information across different systems, but without an architecture that integrates it, each area ends up building its own reports through manual processes. The Sencinet case shows how that fragmentation turns into long access times, inconsistencies, and operating costs that recur month after month.

By implementing automated ingestion, a transformation and cleansing layer, and a data lake as the central point, information no longer depends on who integrates it and becomes available to whoever needs it. This also lays the data governance foundations whose absence, according to the case, limited the strategic use of information.

With that foundation, the Power BI and Superset reports can grow without redoing previous work. The company thus gains the agility to add new sources and new use cases as its operation demands.

Technologies used

• Sharepoint
• Pentaho
• Superset
• Power BI
• MySQL
• Python
• Power Automate

Results