The Bioscore project aimed to increase client engagement by developing a personalized Customer Portal for each of its hotel clients, enabling them to interact with real-time data and monitor their sustainability certification process. This initiative not only improved the efficiency of data delivery but also positioned Bioscore as an innovative brand in the market.
The implementation significantly reduced manual tasks related to file management, report creation, and chart assembly by 90%. By centralizing data and automating previously manual processes, Bioscore enhanced its service offerings, reduced human errors, and provided clients with a seamless, interactive reporting experience, adding substantial value compared to competitors.
Before Implementation
Data analysts had to manually download, copy, and paste data between files, leading to errors and inefficiencies.
Reports had to be manually created, causing delays and inconsistencies in visual presentation.
Data was scattered across multiple Excel files, making it difficult to manage and analyze.
Key Improvements
Automating the extraction, transformation, and loading (ETL) process using Python and SQL, eliminating manual data movement.
Building a customer portal for automated, consistent, and faster report generation.
Creating a centralized data warehouse using, enabling efficient data management and future scalability for advanced data analysis.
Project Overview
1.
Data Analysis
The team conducted a thorough analysis of existing data sources and file structures. Google Cloud and BigQuery were selected as the technology stack for scalable and secure data storage.
2.
Data Transformation
Python functions were created to automate the extraction, enrichment, and transformation of raw data, then ingested into BigQuery, where created structured views for key metrics.
3.
Customer Portal
A proprietary brand and domain portal was implemented for Bioscore, granting external clients secure, personalized access to their performance data. Automated dashboards deliver real-time insights, eliminating the need for manual formatting in other applications.
4.
Data Automation
Automation was established through Cloud Scheduler to regularly run Python functions for data updates. Cloud Functions were used to maintain these workflows, facilitating the easy replication of dashboards for new clients and ensuring the system’s scalability and efficiency.
Python
BigQuery
Google Cloud Functions
Looker
Technologies used
Víctor Monzón
CEO - Bioscore Sustainability
"Working with Datakimia has revolutionized our data management. The Customer Portal provides real-time insights and automated reports that enhance our decision-making. The secure, personalized access to our performance data has improved collaboration within our team. We appreciate their innovative approach and look forward to continuing our partnership."
