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Gaston-Portfolio

BI & Data Analytics Portfolio

BI & Data Analytics Portfolio

Data Product Manager · Business Intelligence & Analytics Solutions
🧑‍💼 Freelance · Available for remote & onsite engagements

👋 Hi, I’m Gaston Lucca. I’m a Product Manager with over 10 years of experience in Business Intelligence and Data Analytics. I specialize in designing, delivering, and scaling analytics platforms that transform data into actionable insights—from building pipelines and dashboards to shaping stakeholder strategy and driving end-to-end delivery.

🧰 Skills & Tech Stack

✅ Selected Projects

Project 1: DataBase & BI reporting: Project Overview

  1. Definition of reporting tool
  2. Definitions of KPIs
  3. Definition and elaboration of the dashboard, regular reporting, and ad-hoc reporting
  1. Data gathering process and normalization
  2. ETL process
  3. Database structure: variables – dimensions, define transactional tables, schemas (star)- data types
  4. Database Warehouse – site – back up - testing
  5. Data Visualization on Qlik View and PowerPoint

Project 2: Analysis of trends and metrics for La.Radio.live

For this project, I have selected and defined a series of metrics relevant to analyzing the performance of the Facebook site for the company, LaRadio.live

Gathering all this information, I was able to summarize the performance of the Facebook site, and estimate by linear regression the future like level. Through this analysis, I have generated actionable conclusions and extracted actionable insights in order to calibrate the marketing strategy of the company.

Broadcasting Performance:

Broadcasting Performance

Project 3.1 (Tableau): Support Function Cost Automation: Objetives, Estructure & Dashboards

  1. To improve the collection, preparation, and display of data to promote a change in the financial analysis department.
  2. Automatization of the process that is built from different sources (SAP ERP, ORACLE, manual settings, local files)

Project 3.2 (PowerBI): Support Function Cost Automation + Sales: Objetives, Estructure & Dashboards

  1. To improve the collection, preparation, and display of data to promote a change inthe financial analysis department.
  2. Automatization of the process that it is been built from different sources (SAP ERP, ORACLE, manual settings, local files)

Mobile expenses by Business Unit.

Sales by Business Unit

Project 4: Finances BI Report: Project Overview

  1. Provide the finance department with a BI solution to improve the collection and display of data
  2. Improve data visualization and analytics through Tableau

Note: WIP

Project 5: Deploying BI solution & Advanced analytics with IDS - AWS + Tableau

  1. Definition of Use case (Agile / Kanban methodology)
  2. Business/Data Analysis + prioritization: How to solve the use case and how we prioritize the backlog
  3. Data Architecture: What is the data architecture I need to resolve this problem
  4. Definitions of KPIs, business rules, data quality rules, and Critical data elements
  5. Deploy data governance: to guarantee that the technical solution is in compliance with the company data strategy (Data quality, tools, data security)
  6. Development part: Coding (Scrum master methodology) + UAT + delivering the model
  7. Definition of the dashboard, regular reporting, and ad-hoc reporting
  1. Data gathering process and normalization
  2. ETL process + data ingestion
  3. Database structure: variables – dimensions, define transactional tables, schemas (star)- data types
  4. Database Warehouse – site – back up - testing

Note: WIP

Project 6: Deploying Data Governance based on DCAM approach

Note: WIP

Project 7: Data Architecture for quotation model

  1. Data research to create a quotation model based on SAP
  2. Data ingestion and analysis
  3. JOIN STATEMENT identification on SQL code
  4. Data architecture to be programmed on an ETL software (Power Center/ Informatica Cloud /SnapLogic)
  5. Designing the data schema and architecture

Target Tables - Source tables - SQL JOIN STATEMENT

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Project 8: Machine Learning model- recommendation system for the sales team

  1. Scoping and frameworking the problem statement: use case elaboration
  2. Data Research and exploration: exploring the data available on the database level and data sources
  3. IT architecture for scalable machine learning models: What is the right IT architecture to scale models

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Note: WIP

Project 9: Business Intelligence Hackathon

Goal: Working on a managerial reporting solution that will improve the company’s strategic plan follow-up and the KPIs tracking, through the improvement of the KPI definitions, process automation on data gathering, and data visualization.

Software used:

1) Business data analyst & technical approach- Top down

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2) Business data analyst & technical approach- From data to KPI (bottom up) image

3) Example of KPI measurement: reduce the technical debt by 30 % image

Note: Due to data security and privacy, I can not show the final dashboards Note: WIP

Project 10: Artificial Intelligence Hackathon (Winner)

Problem statement: ​Digital Customer Support has identified duplications in Articles and Documents, which hinder customers from easily finding specific solutions to their issues or searches.

Goal: Our objective is to develop a sustainable solution aligned with Schneider data standards that compares Articles, Documents, and Links, providing accurate similarity scores based on title and response comparisons. Additionally, use case-oriented visualizations will highlight potential duplicates, assisting agents in the analysis and optimization of the content.

SaasS Plataform: Dataiku

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Librari: Faiss library

Data source analysis: Analysis of the Data Source reveal that Data source is unstructured and risks of Word-to-Word comparisons because of the dependency of the Content and Technical Specific.

Solution: Steps of the solution includes the following actions: Data Preparation, Data Validation, LMM, Reporting, and Visualisation. (Dummy Data)

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Visualization: Developed on Tableau (Dummy Data)

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Benefits of the model : Gives a detailed matrix after one iteration of SS calculation

Using LLM, we save the Context impact.

Flexible Threshold defined by the user

Give the pool of similar FAQs biased SS and on FAQ related.

Extract the FAQ if they are connected and allow fast access to the online check

Risk Avoided: Word-to-word comparison loses the Context.

All Experts to use it as an interface to make a final validation.

Scale up & Next steps image

Note: WIP

Project 11: Digital Data Analytics Sales Products

Product 1: Greenberry

What is: Greenberry+ is a Business Intelligence solution developed in a Tableau Dashboard that showcases Sales-related Data collected from Salesforce CRM into advanced analytics.

Objective: to empower the Sales teams to effortlessly convert Sales data into valuable insights, focusing on the Opportunity Pipeline analysis: a powerful support to adjust their commercial strategy, ensuring flexible responses to market dynamics.

Solution:

Key KPIs: Sales volume, business pipeline, won/lost/ cancelled opportunities Visuals: Regional/segment filters, waterfall analysis, drill-through by account and opportunity. Integration: CRM (Opportunities) + client data, with governance and data quality standards.

Key Features​​​​​​​

Home page example (dummy data used for data protection purposes)

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Product 2: Platforming and Coverage

What is Platforming & Coverage BI? Platforming and Coverage are vital components supporting the new E2E Growth Path strategy to improve our Digital Customer Journey.​

Platforming & Coverage BI is the new tool of reference to positively influence your platforming and coverage. It will enable Sales Excellence, Sales Leaders, and Sales Managers to:​​

Key Features

PLATFORMING​:

COVERAGE​: Am I covering my accounts as per their attractiveness​ …as per my plan?​ …as per their revenue or potential revenue?​ Are there customers engaging with Schneider that I’m unaware of?​

BENCHMARK​:

How am I performing in relation to the official KPIs?​ What is my country’s ranking?

Solution:

Key KPIs: active coverage Visuals: Regional/segment filters, drill-through by account Integration: CRM + ERP + clientes

Home page example (dummy data used for data protection purposes)

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