Seko Kibona

Case Study: School Analytics

An interactive tool offering the ability for users to more deeply explore data directly within the dashboard to uncover insights into school rankings across countries, cities and regions.

Africa Tech Schools

Along with the website, , the client wants a dashboard for users to freely explore data to uncover insights into the African tech ecosystem. This information can be extracted from the tool, to be used for reporting and analysis.

Project Requirements

  • Users want data-driven insights and reports from the same source of truth in an interactive dashboard.
  • Users should be able to see key perfromance indicatiors such as tech ecosystem rank, # of schools by country, countries with 5+ coding bootcamps by region/country/city, etc.
  • Client wants a serverless tool that automatically scales to tens of thousands of users, without any infrastructure to manage or capacity to plan for.

Project Overview

Africa < Tech /> Schools (ATS) is a catalog of technology colleges and universities, incubators, accelerators, innovation hubs, coding boot camps, and coworking spaces in Africa. Users can discover and learn about formal and informal technology spaces in the continent. They can search and view analytics and metrics to understand tech trends and perform comparative analysis of the tech ecosystem.

The Challenge

Create a dashboard that will give 100+ premium website users quick access to interactive analytics and reports showing technology ecosystem trends in Africa.

The Approach & Solution

80% of Africa Tech School users were located in the African continent, so we had to create a dashboard that could lender data quickly, whenever the dashboard was opened, or when users queried data. We wanted to pick a tool that was scalable, accessible, and cost-effective. After reviewing different business intelligence products, we chose Amazon QuickSight to build our dashboard. We chose Amazon QuickSight, because the website was tightly intergrated with the AWS ecosystem. In addition, the integration with the AWS data analytics stack, scalability, performance, and a competitive pricing model were key reasons for choosing Amazon QuickSight. We stored our data in Amazon RDS PostgreSQL database, we used AWS Glue to prepare, move, and integrate data from the database to Amazon QuickSight for user consumption.

image This is a representative of the data flow pipeline only, not actual data flow.
image Due to client privacy we can't show the actual dashboard, this is a representative photo only, not actual dashboard.

The Results

Total Number of Users
100+
Number of users who have subscribed to the dashboard.
Views per Month
700+
Number of pages/views that were opened.
Total Data Extracts per Month
1,000+
Number of times data was extracted from the dashboard to excel/pdf.
Average Engagement Time per Month
1min
How long a user spent in the dashboard.

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