Almost all of the report's 2,500 survey respondents plan on learning and using advanced information and communications technology or ICT in the next 10 years.
Here are four of the top technologies predicted to influence the development sector and how to pursue a career in them, with advice from experts.
Geographical Information systems
Geographical information system software is used to present and analyze project data and create maps of project locations to visualize findings and results; it's an incredibly sought-after skill in development.
"There are multiple ways to learn about GIS [geographical information systems]," said Carries Stokes, chief geographer and director of USAID's GeoCenter. If you are currently studying at university, "run — do not walk — run to your geography department," she advised. Geography departments are the key place to learn GIS skills.
"It's not hard to get the skills, it does require some time and discipline, however," Stokes said. "It's not just something you can pick up by playing around because there are some concepts that go along with the technology."
Stokes strongly recommends taking a course in the basics of human geography in addition to learning GIS software. This will help you to "better understand the relationship between people and their environment that is extremely important."
"They go hand in hand: To just focus on the technology alone is only part of the equation, the other part is understanding the concepts that drive human behavior," she explained.
If you're not at university, there are also online courses — some are even free along with courses at community college.
Big data requires deep statistical and analytical skills. A university degree in mathematics, information technology, computer science, economics, electrical engineering, or bioinformatics, will all provide you with relevant skills to pursue a career in big data, explained Robert Bakyayita, software developer at ATemp Consulting. Continuing with a master's degree and specializing or majoring in big data is also advisable.
Coding and programming languages, such as Python, are also essential for big data scientists. These, too, are learned in an academic setting or can be gained through online courses.
Paula Hidalgo-Sanchis, manager of Pulse Lab Kampala, advised that the best way to hone big data skills is on-the-job. Additional reading on the topic and attending conferences will also help to broaden knowledge.
"Learning about these things, it's not like you do a course and you're done. It really takes time, it's really different stuff," she said. "This topic is complicated, so it takes longer to get the knowledge and the expertise."
Cloud computing is the use of online and remote servers to store, manage, and process data. Cloud computing offers interesting possibilities for geographically vulnerable areas such as small island developing states who may be subject to hurricanes, tectonic hazards, or other climatic changes to physically backup data elsewhere.
Jerry Hensel, managing partner and e-government consultant at Precipice Development International, started out as a programmer, going on to become a database architect and then moving into international development.
Henzel explained that a degree in management information systems — the study of people, technology, and organizations is advisable for a more research-based path, whereas pure computer science can be more theoretical.
Many cloud providers offer a free tier, such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, where users can learn the basics and experiment, Henzel said. Many now also offer their own training and certification courses. A number of cloud providers offer reduced prices, or even free versions, of their services for nonprofits.
If you are just starting out, volunteering for development organizations is one way to begin building a portfolio. "You help somebody in the community that's doing meaningful work and with the same token, you're also building up your own cloud capabilities," Henzel said.
Continuing education is very important in cloud computing. "This field moves extremely fast," he said. "What I learned and knew in the '90s and early 2000s is obsolete today."
There's been a buzz recently surrounding artificial intelligence and machine learning, but it's still "early days" and there's plenty more to learn and explore around its potential and uses, said Aubra Anthony, strategy and research lead at the Center for Digital Development of USAID's Global Development Lab.
To work in AI for development, you can either become a highly tech-skilled data scientist — critical in developing AI and machine learning products or, alternatively, gain a deeper understanding of the uses of AI for development through additional learning.
To become an expert in AI requires a background in data science. However, Anthony stressed the need for greater understanding and research into biased machine learning, to ensure the magnifying involvement of tech in development doesn't cause more harm than good. Part of the Global Development Lab's work involves partnering with Massachusetts Institute of Technology as part of the Higher Education Solutions Network to address the gap on bias and fairness in machine learning in the developing country context.
While more data scientists working in development is sought after, "we don't need a hoard of data scientists," Anthony said, adding that development practitioners who then become AI experts would be a great nexus of skill sets.
Becoming more familiar with AI and machine learning would be beneficial for all international development professionals, Anthony said, because many USAID partners are turning to machine learning to make operations more efficient and effective. Even if you aren't developing the tools yourself, understanding them and being able to investigate and evaluate their impact is important.
Google AI offers an array of useful guides, workshops, and resources, Anthony said, and can be filtered to suit your experience level. Additionally, there are various massive open online courses such as those run by Udacity, Coursera, and fastai.
In the coming years, Anthony predicts there will be an increasing number of specialized university courses in AI and machine learning for development.