The eight Millennium Development Goals are targeted for completion in 2015, and the Sustainable Development Goals (SDGs) will be the world’s new targets. These SDGs necessitate a “Data Revolution,” and every organization needs to start laying the groundwork now for a successful future.
If you do a Google Search of “SDG data revolution”, articles like Funding the Data Revolution, Igniting the Data Revolution, and the UN Data Revolution Group will appear in your search results. But all of these overlook a major question: Do organizations working with the Sustainable Development Goals have the capacity to work with this data revolution?
According to McKinsey & Company, probably not. The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise, and a shortfall of 1.5 million managers and analysts able understand and make decisions based on big data. This is especially worrying as organizations involved in the Data Revolution – from top to bottom – will need to analyze and interpret data to ensure SDG success.
Instead of focusing on external data revolution projects, organizations should be focusing on their internal capacity to do analysis. This can be done in part by investing more in the field of data science.
A hot topic in the public and private sectors, data science is the discipline of extracting knowledge from data. Thanks to recently developed techniques, organizations can now look at old problems and get new insights and more informative results. What’s more, we’re in a Golden Age of resources for learning data science inside and outside of the classroom. There are several Massive Open Online Courses (MOOCs) related to data science, guides for using Excel (which can conduct most statistical analyses), as well as open source tools like R and Python (with accompanying active communities and advanced statistical packages).
Who in your organization should care about data science? Everyone. Project Managers (PMs) need to be comfortable with conducting analyses – gone is the time when individuals can just to pass information to another person to analyze. While dedicated data scientists and analysts could crunch numbers all day, they’re not domain experts – PMs are the ones who know where to look for useful insights. As Ram Charan covers in this article, data science is changing the way end users (or program beneficiaries) interact with programs, and understanding this feedback is key to organizational success. Basic numeric literacy is fast becoming as important as textual literacy.
For the data revolution to become a meaningful contributor to the SDGs, organizations need to ensure their staff understand basic statistics and analysis – otherwise, the investment in data will have a reduced payoff, programs will underperform, and we will fall short of our goals for the next 15 years.
In their second blog exploring Nigeria’s changing fertilizer market, Vinisha Bhatia-Murdach and Scott Wallace dive deep into the details of the NPK fertilizer market and explore how Nigeria became a leader in this space. They also discuss why the VIFAA Nigeria dashboard is an important part of ensuring access to information at each point in fertilizer supply chain.
Many factors must be considered before investing in a data ecosystem for sustainable development, including the objective of the assessment, the focus level, and specific goals for data outputs. IREX’s Jesus Melendez Vicente and DG’s Carmen Cañas would like to share a few insights and questions to help you get started, including a curated list of tools for data ecosystem assessments.
Our Visualizing Insights for Fertilizer in African Agriculture (VIFAA) Program launched an Innovation Fund to address data shortages relating to total crop production. Building on its track record of mapping land cover, Quantitative Engineering Design (QED) was selected to tackle the challenge of mapping Nigeria’s croplands. Through the Innovation Fund, DG and partners will use the resulting data and maps to answer two questions: (a) what is the total cropland under production in Nigeria and (b) what is the cropland under production by crop type.