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Title: Fighting Hunger with Analytics (thanks Alteryx!)
Company: World Food Programme
Collaborators: Information Lab
Overview of Use Case
According to Bill: “Hi. I’m Bill, an Information Management Specialist at the Vulnerability Analysis & Mapping Unit at the World Food Programme’s (WFP) Regional Office of West & Central Africa. That’s a mouthful but basically my job is to collect, process, and analyse data to examine a couple questions: 1) Who are the food insecure or vulnerable people? 2) How many are there? 3) Where do they live? 4) Why are they food insecure or vulnerable? 5) How is the situation likely to evolve and what are the risks threatening them?
Like many of my colleagues, I’m data literate and can use some old-school tools like Excel, SPSS and ArcGIS to do analyses but when a new project came up, combining historical food insecurity figures from the Cadre Harmonise (http://www.cilss.int/#) with operational data, I realised I needed more firepower. So I called up Benedetta…”
According to Benedetta: “I’ve been using Alteryx for more than 2 years being part of the Information Lab UK and going through the Data School program, which teaches you Alteryx & Tableau in a four-month long training. After becoming familiar with the software, I signed up for Alteryx for Good and Tableau Foundation as I want to share my skills for free to organisations which don’t have the same resources as corporates do.
I had a call from Bill Olander, working at the World Food Programme, United Nations, from the Dakar office in Senegal. He needed some help with visualising his data and while talking we realised that a big data cleaning and automation process had to be done first. I suggested to get Alteryx as they offer free licenses for charity. It turned out he had it installed already in his pc, but he’s never used it before!”
Describe the business challenge or problem you needed to solve
Every year, WFP assists 80 million people in nearly 80 countries. WFP is the leading humanitarian agency fighting hunger worldwide, providing humanitarian assistance in emergencies and supporting vulnerable people and communities to improve nutrition and resilience.
Currently, in the region of West & Central Africa WFP faces three humanitarian crises: the Lake Chad Basin region, the Central African Republic and, more broadly, drought and unrest in Mali. In all three cases, WFP distributes food and remittances to refugees, displaced persons and host communities, as well as food supplements to the most vulnerable.
With the scale and complexity of the crisis, there was a need for managers to understand the burden of food insecurity along with operational data the progress of the response. This information needed to be processed as quickly as possible to facilitate timely decision making.
WFP’s staff had some old-school skills with Excel but they realized that a brute-force manual solution would be too error-prone and time consuming to work. The challenge called for a little data jiu-jitsu: creating an automated data-pipeline that could clean, transform, and merge two very different data sources.
Describe your working solution
Step 1 – Alteryx data prep: Input of served data by country excel file – cleaning, dynamically filtering columns, eliminating empty columns/rows, populating empty cells, unioning with a second data source, looking at the need by country, imputing missing values and much more! See attached workflow!
Step 2 – Tableau: The output is a TDE used directly in Tableau. This process is repeated each month and the Tableau dashboard, which is used by managers, automatically updates.
Describe the benefits you have achieved
This solution impacted WFP and the humanitarian community at a couple of levels:
Decision making and advocacy—most importantly, it allowed decision makers to quickly and intuitively view information surrounding a complex situation. By overlaying two data sources that had before been siloed, users were able to assess bottle-necks, and re-allocate resources based on need. Users were also able to use this information for advocacy, illustrating areas in need of increased food assistance.
Alteryx helped to break down silos and increase data literacy. This solution also opened a lot of eyes on what is possible. Unfortunately, data is often siloed between units, but by seeing the potential and ease of working with automated data, many data users and consumers have started to work together and think about how they can better harness the power of their data.
No unfortunately - We can’t share the Tableau link for data protection reasons.