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Portfolio & Case Studies / Capacity building in data management & analytics
Case Study

Capacity building in data management & analytics

Practical data management and analytics training designed to strengthen staff capacity in data handling, analysis, visualization, and evidence-based decision-making.

Capacity building in data management & analytics
Industry Non-Governmental Organizations (NGOs)
Industry Non-Governmental Organizations (NGOs)
Overview

The work at a glance.

MRT IT Peaks Limited delivered targeted capacity-building training to an NGO team whose existing data management and analytical skills were limiting the effective use of organizational data. The training covered data management, Excel, Power BI, and data analysis best practices, combining practical exercises with real datasets to help participants apply their skills to real-world reporting and decision-making.
01 The challenge

What needed to change?

Staff had limited skills in data management and analysis. As a result, organizational data was not being fully utilized, and reporting outputs were weaker than required to effectively support planning, performance monitoring, and decision-making.
02 The solution

A solution built for purpose.

MRT IT Peaks Limited delivered practical, targeted training in data management, Excel, Power BI, and data analysis. Rather than relying solely on theoretical instruction, the training used real datasets and practical exercises to strengthen participants' ability to work with and interpret data.
03 Our approach

How we made it happen.

The project assessed staff capacity and data-related skills gaps, then designed training around practical organizational needs. Hands-on sessions covered data management and analytics, including Excel, Power BI, and data analysis best practices, using real datasets and practical exercises to reinforce learning. Throughout, the focus was on enabling staff to independently apply the skills they gained.
04 Results

What changed in the end?

The training improved staff capacity and confidence in handling data, which in turn improved data quality and reporting. It increased the use of analytics in planning and decision-making, strengthened internal data management capabilities, and created more sustainable internal capacity for data analysis and reporting going forward.