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

• Product & Strategy: Product roadmap, stakeholder management, Business Analysis, agile/scrum, data governance
• Data & Analytics: Data warehouse, data modelling, ETL/ELT
• BI & Visualisation: Power BI, Tableau, Qlik
• Programming & Tools: SQL, Snowflake, Azure/AWS data services
• Soft Skills: Cross-functional leadership, client consultancy, translating business to data requirements

✅ Selected Projects

Project 1: DataBase & BI reporting: Project Overview

• BI REPORTING STRUCTURE: Create a system of BI reporting that allows a display of data and a reporting system to track the performance of the pricing and supply program.

1. Definition of reporting tool
2. Definitions of KPIs
3. Definition and elaboration of the dashboard, regular reporting, and ad-hoc reporting

• DATABASE STRUCTURE: Design and create a solid database that contains structural data, easy to retrieve, which allows answering simple queries.

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, using linear regression, the future like level. Through this analysis, I have generated actionable conclusions and extracted key insights to calibrate the company’s marketing strategy.

Broadcasting Performance:

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

• OBJECTIVES:

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)

• ESTRUCTURE:

• DASHBOARDS:

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

• OBJECTIVES:

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

• ESTRUCTURE:

• DASHBOARDS:

Mobile expenses by Business Unit.

Sales by Business Unit

Project 4: Finances BI Report: Project Overview

• OBJECTIVES:

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

• BI REPORTING ESTRUCTURE: Create a system of BI & advanced analytical reporting that allows covering Business use cases (business analytics demands)

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 complies 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

• DATABASE STRUCTURE: Design and create a solid database that contains structural data, easy to retrieve, which allows answering simple queries + live data connection

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

• Process automation by DDBB deployment: Data flow

• Data Architecture + data ingestion for data model customization

Note: WIP

Project 6: Deploying Data Governance based on DCAM framework

• Deploying a strategy for solid data analytics environment in an organization
• Customizing DCAM based on the particularities of the organization and data ofices and data domains
• Active data quality: implemneting the data quality circle
• Active data security: Data attack surface - Data breach map - Deployment of the control point of access
• Contribute to roadmap planning and value realization for the Data Maturity Program and the Data Quality platform

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

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

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:

• AWS S3 bucket for data Ingestion
• AWS Redshift for DDBB
• Tableau: For data visualization

1) Business data analyst & technical approach- Top down

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

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

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 analyzing and optimizing the content.

SaasS Plataform: Dataiku

Librari: Faiss library

Data source analysis: Analysis of the Data Source reveals that the 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 include the following actions: Data Preparation, Data Validation, LMM, Reporting, and Visualisation. (Dummy Data)

Visualization: Developed on Tableau (Dummy Data)

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 for final validation.

Scale up & Next steps

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.

• Target audience: Sales manager by region /country, Account Manger, Strategic accounts manager, Finances
• Active Users: 3000
• Adoption 85% over the target
• Platform: Saleforce (CRM) + AWS + Tableau Web Analitic/Tableau Site
• Data Source: Saleforce (CRM) (Objects: Opportunities) + CHITU (CRM China) + RELTIO MDM + Other master data (sharepoint)
• Architecture: five flow of data (pipeline) allocated in Sales Data Mart + shadow squema + DP in ADL (golden layer) + DP in UDL (silver layer)
• Size: Tableau Site 20 GB - Data Mart on AWS 0.8 TB

Key Features​​​​​​

• Monitor segments and Targeted Accounts performance.
• Pipeline evolution by month + waterfall explanation YoY, MoM + Pipeline Forecast 1- 2 yeras
• Data-driven business insights & actionable intelligence
• Data consistency across countries, Business Units, and segments
• Targeted view: Performance, pipeline & forecast analysis
• Secure Power & Energy Management QBR analysis
• Strategic customer & Segment performance
• Deep dive abilities: by geography, segment, services
• Quarterly Business Reviews preparation »> Last Capability Launched
• Desing firm feature (under development)

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

Product 2: Platforming and Coverage

Platforming & Coverage BI is the BI tool supporting the new E2E Growth Path strategy to improve our Digital Customer Journey. This tool is the reference to positively influence sales account mnager platforming and coverage. It will enable Sales Excellence, Sales Leaders, and Sales Managers to undertan better the interaction between client - company and to imporve the coverage of the diferrents accounts. ​​

Key Features

• Understand their gaps in platforming ​& coverage
• Identify potential ways for improvement
• Optimize their commercial resource allocation

PLATFORMING​:

• View total accounts for my team/country in the 9-box view​
• Customize the data view based on the info the user wish to know.​ -View performance by bFO Sales, Net orders, Digital Engagement, etc…

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 the company 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: Total accounts - % of active accounts wiht platfoming, % Interactions Visuals: Account »> Regional/segment filters, drill-through by account, country, sale manager, account manager Metrics: PAM. Sales, Orders, Pipeline (Opps), Visits, Interactions Integration: CRM + ERP + clientes

• Target audience: Sales manager by region /country
• Active Users: 500
• Adoption 85% over the target
• Platform: Saleforce (CRM) + AWS + Tableau Web Analitic/Tableau Site
• Data Source: Saleforce (CRM) (Objects: Opportunities, Cases & activities)+ RELTIO MDM + SAP (ERP) Object: Orders)
• Architecture: one flow of data (pipilin) allocated in XX Data Mart + shadow squema + DP in ADL (golden layer) + DP in UDL (silver layer)
• Size: Tableau Site 2 GB - Data Mart on AWS 0.1 TB

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

Adoption - Monitoring Dashboard

Architecture and Data Flow

```mermaid flowchart LR A[Sources: CRM, ERP, Product Catalog, Reltio MDM] –> B[Ingestion: ETL] B –> C[Semantic Model: Sales, Customers, Products] C –> D[Dashboards: Greenberry] C –> E[Dashboards: Platforming & Coverage] subgraph Governance F[KPI Glossary, Metrics & Business Rules] –> C end C –> G[Adoption & usage metrics]

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