From Data Collection to Data Use: Advancing a Data-Centric ICT4D Strategy

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July 10, 2018

From Data Collection to Data Use: Advancing a Data-Centric ICT4D Strategy

By Steve Hellen

Director of ICT4D & GIS Catholic Relief Services

Data Driven Decision Making. . . Analyze Data. . . Take Action on Data. . . Adaptive Management with Data. . . One does not have to look far to find buzz-phrases that link data with a verb that emphasizes use – and that ridicules those who might be so callous as to act without data (because we all should have comprehensive and timely data, right?). In fact, “Be Data Driven” appears at the center of the Principles for Digital Development. At Catholic Relief Services, we similarly highlight data for evidence-based decision making as the headline of our digital development or ICT4D efforts.

Allow me to back up and offer some history. Spawned by the mobile revolution, hundreds of our programs use digital tools to capture information about our fieldwork – typically to register people and track delivery of goods and services. This yields many fairly obvious positive results such as improved data quality, timeliness, and reliability. At the risk of sounding pompous, we got quite good at mobile data collection. But we saw the data being used largely to aggregate for reporting to institutional donors – data for compliance. This is certainly important for accountability but misses a huge opportunity to use these rich datasets to improve program operations and efficacy. For example, it was not uncommon to find programs that planned their activities using hand-drawn maps despite having digital data available. So, in 2016 we crafted an ICT4D strategy that aims to up our game in leveraging data through innovative analytics to improve our programs. There are five pillars to this initiative:

1. Elevate the Conversation

We have strong leadership support to focus on more impactful use of data. This aligns with our agency strategy that called out as core competencies: ICT4D; and Monitoring, Evaluation, Accountability and Learning. We further unpacked this idea into several objectives: informing program decision-making through data analysis; enabling data aggregation; safeguarding data privacy; and enabling data sharing.

2. Rules, Tools & Schools

With more data that is often highly sensitive, we need stronger guardrails to protect it. As a result, we created data privacy and protection guidelines, implemented an obligatory organization-wide information security awareness program, and drafted responsible data principles.

Aiming to democratize access to data analytics, we put enterprise license agreements in place to make best-of-breed data analytic tools available to all staff who need to publish or access dashboards, visualization, mapping and spatial analysis. 

To ensure there is capacity to use analytic tools as well as general data literacy, we curated the best online resources into a training program that is available to take in a self-paced format. We also deliver a series of instructor-led trainings when and where it makes sense.

3. Support & Partnerships

Despite the best training resources, access to expertise is often necessary to get a data analytics effort off the ground. We offer human resource options that range from tapping into volunteer corps to university partnerships to in-house staff (both locally and a central team) to professional services. These span spectrums of cost, predictability and suitability for complex efforts. We found it is important to have options to meet various needs – for example, from basic map-making to complex predictive analytics or machine learning.

4. Embed into Standard Practices

Data standardization are often dirty words in our sector. We are finding, however, that to do any meaningful data analysis beyond the scope of a single program requires a fair amount of common data definition. Finding ways to balance organization-wide data desires with what is useful to our field programs remains a delicate balance. The current iteration is to capture high level common data about our programs globally while more detailed and sector-specific indicators are defined and tracked locally.

5. Art of the Possible

We shine a spotlight on examples of programs that use data in innovative ways to inspire others to look at data as an asset that can improve the efficacy of their own interventions. Some of my favorite examples are making food distribution sites more accessible in Madagascarprotecting refugees from floods in Bangladesh, ensuring everyone receives a mosquito net in Nigeria, and mitigating risk of food loss in Ethiopia

It’s All About Results

Each of the examples above and all our ICT4D investments are laser-focused on using technology and data to reach the most isolated and vulnerable communities – going to the last mile, going to scale, and achieving sustainable use. Data helps to increase our reach, effectiveness, and efficiency with evidence that we are improving the lives of people we serve

I will explore these topics in more detail at Humentum’s Annual Conference later this month in Washington during the session Advancing a Data-Centric ICT4D Strategy.

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