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Tunisia Telecom · CVM · Reporting

SQL Ad-hoc Requests & Dashboard Development

Operational reporting and dashboarding for Customer Value Management (CVM) at Tunisia Telecom (June 2016 – Dec 2017).

Tunisia TelecomCVM OperationsSQL · ETLDashboards

TL;DR: Reliable ETL and clear KPI definitions unlock fast, repeatable reporting — prioritize automation and documented verification checks before scaling dashboards.

Context

I worked in the CVM and Data-Mining department as an SQL developer and SAS user. Business stakeholders required rapid ad-hoc analyses, weekly and monthly dashboards, and reliable data pipelines that could feed PowerPoint reports and operational decision-making.

What I built

  • SQL ETL and analytical base tables to join billing, CRM and network sources for CVM KPIs.
  • Operational dashboards tracking CVM performance, network-quality KPIs, data-service penetration and market growth.
  • Customer segmentation profiles and sales analytics used for targeting and product planning.
  • VBA automation to export and format PowerPoint reports from analysis outputs.
  • Data quality verification scripts and anomaly checks to catch source problems early.

Business impact

Dashboards and automated reports reduced reporting latency from days to hours, improved campaign targeting with segment-level insights, and supported Try & Buy offer frameworks and retention actions recommended to marketing teams.

Deliverables

Weekly & monthly dashboard templates, SQL stored procedures for ETL, VBA report automation scripts, and a set of documented KPI definitions and verification checks for handover to the BI team.

Key Takeaway

Design insight: Reliable ETL and clear KPI definitions unlock fast, repeatable reporting — prioritize automation and documented verification checks before scaling dashboards.

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FAQ

What is the key takeaway from "SQL Ad-hoc Requests & Dashboard Development"?

Reliable ETL and clear KPI definitions unlock fast, repeatable reporting — prioritize automation and documented verification checks before scaling dashboards.

Who wrote this and what is it about?

This was written by Mahmoud Trigui, Senior Data Scientist. CVM SQL reporting, dashboard development, automation and data quality for Tunisia Telecom (2016-2017).

Comments

Machine Learning Feature Engineering MLForecast Time Series Decomposition Forecasting LightGBM XGBoost Catboost Clustering Segmentation NLP LLMs Web App R Markdown SQL Oracle DB SAS-Guide SAS E-Miner Dataiku BigQuery GCP Python R CRISP-DM Hypothesis Testing ANOVA Data Analytics Dimensionality Reduction Recommendation System Network Analysis Geospace Analysis Spatial Data Embedding Sampling Techniques Decision Rules Data Storytelling CVM Churn Fraud Detection Sentiment Analysis Topic Modeling IBM Watson PowerBI Looker Studio VBA Statistical Learning Ensemble Modeling Stacking Cross-Validation Profiling ABT Construction Plumber Tidyverse Shiny Prophet Deep Learning Scikit-Learn JSON SAS Programming Git VS Code CSS Styling Automated Reporting Outlier Detection Temporal Clustering Startup Survival Pre-Valuation Modeling K-Means Decision Trees Data Science Predictive Modeling SVM LDA Text Classification Weight Prediction Pattern Recognition Real-Time Detection Community Detection Pipeline Automation Data Quality Checks Data Reliability Specification Mapping Business Strategy Marketing Campaigns Try & Buy Frameworks KPI Dashboards Network Quality Sales Analytics Mentoring Statistics Lecturer Remote Work Hybrid Work Consulting Contract Full-Time Freelance Sofrecom Orange Group Tunisia Telecom Kiota Intelligence VC Analytics Series A Prediction Production ML Applied AI Prompt Engineering Business Forecasting Decision Systems Graph Analytics Household Detection Multi-SIM Detection FTTH Forecasting Audit Extraction Infrastructure Classification Pydantic GPT-4 OpenAI API Base64 Classification Zindi Codementor LAAS-CNRS ESSAI MIT xPRO Tunisia ML Competition Cell Tower Analysis Uber Logistics Uber Cape Town Necessary Condition Analysis Behavioral Signals Spike Smoothing Observation Unit Design Dendrogram Ward Clustering VIF Target Encoding Machine Learning Feature Engineering MLForecast Time Series Decomposition Forecasting LightGBM XGBoost Catboost Clustering Segmentation NLP LLMs Web App R Markdown SQL Oracle DB SAS-Guide SAS E-Miner Dataiku BigQuery GCP Python R CRISP-DM Hypothesis Testing ANOVA Data Analytics Dimensionality Reduction Recommendation System Network Analysis Geospace Analysis Spatial Data Embedding Sampling Techniques Decision Rules Data Storytelling CVM Churn Fraud Detection Sentiment Analysis Topic Modeling IBM Watson PowerBI Looker Studio VBA Statistical Learning Ensemble Modeling Stacking Cross-Validation Profiling ABT Construction Plumber Tidyverse Shiny Prophet Deep Learning Scikit-Learn JSON SAS Programming Git VS Code CSS Styling Automated Reporting Outlier Detection Temporal Clustering Startup Survival Pre-Valuation Modeling K-Means Decision Trees Data Science Predictive Modeling SVM LDA Text Classification Weight Prediction Pattern Recognition Real-Time Detection Community Detection Pipeline Automation Data Quality Checks Data Reliability Specification Mapping Business Strategy Marketing Campaigns Try & Buy Frameworks KPI Dashboards Network Quality Sales Analytics Mentoring Statistics Lecturer Remote Work Hybrid Work Consulting Contract Full-Time Freelance Sofrecom Orange Group Tunisia Telecom Kiota Intelligence VC Analytics Series A Prediction Production ML Applied AI Prompt Engineering Business Forecasting Decision Systems Graph Analytics Household Detection Multi-SIM Detection FTTH Forecasting Audit Extraction Infrastructure Classification Pydantic GPT-4 OpenAI API Base64 Classification Zindi Codementor LAAS-CNRS ESSAI MIT xPRO Tunisia ML Competition Cell Tower Analysis Uber Logistics Uber Cape Town Necessary Condition Analysis Behavioral Signals Spike Smoothing Observation Unit Design Dendrogram Ward Clustering VIF Target Encoding