AZ
Back to Projects

Telkom Indonesia – Enterprise Dashboard Visualization System

Looker StudioGoogle Sheets APIFirebaseNode.jsExpress.jsGoogle CloudJavaScriptHTML/CSS

Developed enterprise-grade operational dashboards for the Regional Enterprise and Government Service (REGS) division during an internship at Telkom Indonesia. Replaced manual spreadsheet workflows with a Looker Studio LOP dashboard and an interactive web-based LOB dashboard integrated with Google Sheets API, Firebase, and Google Cloud — significantly improving data visibility, collaboration, and monitoring efficiency.

Internship Project — Telkom Indonesia

From Spreadsheets to Enterprise Dashboards

During my internship at Telkom Indonesia, I was embedded in the Regional Enterprise and Government Service (REGS) division — a unit responsible for managing operational and project data for enterprise and government clients across the region.

The division's existing workflow relied heavily on spreadsheets. While this worked at small scale, it had become a significant operational bottleneck: data was scattered, visualizations were limited, and collaboration was fragile. I was brought in to help design and build better tooling.

Over the course of the internship, I contributed to two major initiatives: a Looker Studio dashboard for List of Project (LOP) data, and an interactive web dashboard for List of Billcom (LOB) data integrated with Google Sheets API, Firebase, and Google Cloud.

Division

REGS — Regional Enterprise & Government Service

Company

Telkom Indonesia (Internship)

Output

2 Production Dashboard Systems

Problem Statement

What Was Broken Before

The division's reliance on manual spreadsheet workflows created four compounding operational problems that affected data accuracy, visibility, and security.

01

Manual Data Entry Errors

Spreadsheet-based workflows introduced consistent human errors during data input and updates, degrading data reliability over time and making reports untrustworthy.

02

No Effective Visualization

Traditional spreadsheets lacked the visualization capabilities needed for fast interpretation of project and billing data, making trend analysis slow and imprecise.

03

Weak Version Control and Collaboration

Multiple team members editing separate files created version conflicts, data loss risk, and no clear audit trail — collaboration was brittle.

04

Limited Data Security

Spreadsheet files shared via email or local drives offered no meaningful access control or audit logging, exposing sensitive operational data.

My Role

Responsibilities Across the Full Stack

I was involved across the entire delivery cycle, from understanding the business problem to implementing and iterating on the solution.

Stakeholder Requirement Gathering
Operational Pain Point Identification
Dashboard UI Concept Design
Looker Studio Development
Data Visualization and Charting
Frontend Implementation
Backend and Data Integration Support
Iterative Testing and Improvements
Tech Stack

Tools and Technologies

Visualization and Data

Looker StudioGoogle Sheets APIGoogle Cloud ConsoleFirebase

Backend and Frontend

Node.jsExpress.jsHTMLCSSJavaScript
Solution Overview

Two-Track Dashboard Strategy

Each initiative addressed a different data domain with a purpose-built approach.

Initiative 1 — LOP Dashboard

List of Project Dashboard (Looker Studio)

Built a fully interactive reporting dashboard in Looker Studio connected to live Google Sheets data. The LOP dashboard gave the REGS team a single-pane view of all active and historical projects, with visual KPIs, status filters, and trend charts that previously required manual report generation.

Easier Project Monitoring

Real-time project status visible to all stakeholders without manual aggregation.

Visual Reporting

Charts, KPIs, and trend lines replaced static tables, dramatically improving data interpretation speed.

Faster Operational Decisions

Managers could act on data within seconds rather than waiting for a report to be compiled.

Initiative 2 — LOB Dashboard

Interactive Web Dashboard for List of Billcom (LOB)

Designed and built a multi-page web application backed by Google Sheets API and Firebase. The LOB dashboard replaced the most fragile part of the team's workflow — billing status tracking — with a secure, structured, cloud-integrated interface.

Summary Dashboard
Detailed Monitoring Page
Status Tracking Page
Filtering and Search
Charts and Data Visualization
Improved UI/UX
Cloud Integration
Enhanced Security
Architecture

Data Flow and System Architecture

The LOB web dashboard was built on a lightweight but scalable architecture connecting Google Sheets as a data source to a Node.js backend and Firebase for security and auth.

Google Sheets

Primary data source

Sheets API

Data retrieval layer

Node.js / Express

Backend API server

Firebase

Auth and security

Web Dashboard

Frontend interface

Data flows from Google Sheets through the API layer, processed by Express, secured via Firebase, and rendered in the web interface.

Dashboard UI Previews

Interface Showcase

The actual LOB web dashboard views built for the REGS division.

Summary Dashboard
Telkom LOB Summary Dashboard

Top-level KPI view showing the status breakdown of projects via responsive pie charts.

Detailed Monitoring Page
Telkom LOB Detailed Monitoring Page

Granular project-level data with dynamic search, parameter filtering, and export capabilities.

Status Tracking Page
Telkom LOB Status Tracking Page

Dedicated interface for tracking billing progress and statuses across different mitigation stages.

Before vs After

What Changed for the Team

Before — Spreadsheet Workflow

  • Manual data entry into shared Excel/Sheets files
  • Emailing reports to stakeholders as static attachments
  • No central source of truth — version conflicts common
  • Charts built manually each report cycle
  • No access control or audit trail

After — Dashboard System

  • Live data synced automatically via Google Sheets API
  • Dashboards accessible in-browser with role-based access
  • Single source of truth with Firebase-backed security
  • Auto-generated charts and KPIs updated in real time
  • Structured audit trail and collaboration support
Impact

Outcomes and Business Impact

The dashboard systems delivered measurable improvements across the division's data operations.

Improved Operational Efficiency

Project and billing monitoring no longer required manual compilation — teams accessed current data instantly.

Reduced Manual Errors

Automated data flow from source to dashboard eliminated the data entry step that caused the most recurring errors.

Better Data Interpretation

Visual charts and KPIs made it significantly easier for managers to interpret trends and make faster decisions.

Improved Team Collaboration

Browser-based dashboards replaced file-sharing workflows, enabling simultaneous access with proper access control.

Enhanced Data Visibility

All stakeholders had a consistent, real-time view of project and billing status without requesting reports.

Stronger Data Security

Firebase authentication replaced open file sharing with role-based access and an audit trail.

Project Accessibility Note

The original dashboards and systems built during this internship operated on internal Telkom Indonesia company data within restricted internal environments. They are no longer publicly accessible. This case study documents the project through architecture descriptions, workflow explanations, and representative UI mockups. No proprietary or confidential data is included or referenced.

Reflection

The most useful thing I built wasn't the dashboard — it was the habit of asking why a team was doing something manually before assuming they needed automation.

Early in the internship, I assumed the problem was a tooling gap. It turned out the real issue was process fragmentation — data existed, but it lived in the wrong places and in the wrong format. The dashboard only worked well because we spent the first phase understanding how the team actually operated day-to-day. That requirement gathering phase shaped every design decision downstream. It's what I'd prioritize first in any similar engagement.

Explore More Projects

Continue exploring selected data, analytics, and machine learning projects.

Chili Quality Classification
Data Science (Thesis)

Chili Quality Classification

Built a Two-Stage XGBoost classification pipeline to automate agricultural quality control. Explored thorough Exploratory Data Analysis (EDA) and compared decision trees, RF, SVM, and MLP models before isolating XGBoost due to a superior ~83% accuracy and ~82% macro F1 score. Emphasized end-to-end data science workflows over simple model APIs.

PythonXGBoostScikit-learnPandas+1 more
View Project
View Chili Quality Classification
Retail Inventory Optimization & Demand Analysis
SQL Data Analytics

Retail Inventory Optimization & Demand Analysis

A SQL-based exploration into how inventory, demand forecasting, and sales interact, with a focus on identifying inefficiencies and turning data into actionable business insights.

SQLPostgreSQLData AnalysisInventory+1 more
View Project
View Retail Inventory Optimization & Demand Analysis
AI Assistant for Data Issue Troubleshooting
Exploratory / Concept

AI Assistant for Data Issue Troubleshooting

Mapped historical issue occurrences spanning SAP BW/BPC environments directly to logical root causes. This exploratory project explores how AI logic can improve analyst efficiency and institutional knowledge reuse within complex ETL layers.

ConceptAI LogicSAP BW/BPCRoot Cause Analysis
View Project
View AI Assistant for Data Issue Troubleshooting