making data meaningful
TRANSCRIPT
6 MAY 2015AMANDA MAKULECJSI CENTER FOR HEALTH INFORMATION, MONITORING & EVALUATIONPhoto credit: Robin Hammond
TECH CHANGE | TECHNOLOGY FOR MONITORING & EVALUATION
MAKING DATA MEANINGFUL
Amanda MakulecProgram Manager & RME AssociateJohn Snow, Inc.
Passionate about how visualizing data effectively can empower people to make decisions.
Some people think design means how it looks. But of course,
if you dig deeper, design is how it
works. -Steve Jobs, Apple
Developing data visualizations as part of international development programs presents unique challenges.
Despite these challenges, using visualizations to analyze and use data is huge in the development community.
There are some simple principles worth considering when designing visualizations in development programs.
Consider whose expertise would be useful.
M&E Advisor
Graphic Designer
Technical Expert
Communications Expert
For data analysis tools like dashboards, engage your end user to understand their needs.
Image credit: Beth Kanter
STATIC IMAGES: COMMUNICATING A MESSAGE
THE USER EXPLORES YOUR DATA AND CAN DRAW THEIR OWN CONCLUSIONS.
YOU DECIDE THE STORY AND THE MESSAGE, GUIDING YOUR READER.
iNTERACTIVE: PROMOTING EXPLORATION & USE
*Normal as defined by standard BMI measures and women aged 20-49 years.
Data table from: Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R. Maternal and child undernutrition and overweight in low-income and middle-income countries” The Lancet 2013; (06 June 2013) DOI: 10.1016/S0140-6736(13)60937-X.
underweight normal overweight obese1980 2008 1980 2008 1980 2008 1980 2008
Africa 18 12 64 58 14 19 4 11LAC 4 2 66 43 22 31 8 24Asia 19 17 68 62 11 17 2 4Europe 4 4 61 55 25 28 10 13Oceania 6 2 69 45 19 32 6 21
Working from a simple table, using Excel, you can design meaningful, visually appealing graphs and charts.
The challenge of visualizing qualitative data requires thoughtful consideration of design and layout.
Create a framework or diagram to explain a complex relationship
From Pilot to Practice | SC4CCM
Nov
2013
Dec
Jan 2014
Feb MarApril
May
June
July
Aug
Sept Oct
Nov
Dec
Jan
2015
Feb MarApril
May
June
July
April
Sept Oct
Nov
Baseline data collection
Follow-on data collection starts & is continuous to Nov 2015
Budget data validation(July-Aug)Documentation
of Yr1 initial findings (Aug-Oct)
Yr2 budget data collection; Yr1 information sharing
Yr2 budget data validation
Global dissemination starts
Data collection concludes
District baseline data collection
District data validation
Documentation of Yr1 findings
District follow up period
District information sharing (Jan-Feb)
District budget data validation
Final round of data collection
nati
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Develop simple timelines