Practical data structure design and management

Session Number: 1322
Track: Quantitative Methods: Theory and Design
Session Type: Poster
Tags: Data Capture and Management (DCM), data cleaning, Data Quality
First Author or Discussion Group Leader: Christopher Huey [Monitoring, Evaluation, and Learning Manager - Chemonics International]
Time: Oct 31, 2018 (06:30 PM - 08:00 PM)
Room: Poster 220

Audience Level: Intermediate

Session Abstract (150 words): 

Project adaptation and learning requires providing the right good-quality data to the right people at the right time. However, even the best of data, when disorganized or poorly cleaned, can lead to headaches and time wasted while attempting to clean, merge, and reorganize the data to make it usable. To help practitioners avoid such headaches and inefficiencies, this session will explore proactive considerations when designing data structures and will provide participants with practical Excel functions for cleaning and reorganizing data in a way that facilitates efficient use, saving practitioners more time for analysis and learning. The ultimate goal is to provide quality and timely data in formats that decision makers need in order to manage, adapt, and improve programming.