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.
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.
In the past decade, there has been over $7 billion worth of private-sector investments into Nigeria’s agriculture sector. Why, and what is the impact of these investments? What does it mean for Nigerian farmers? In this blog, we explore these and other changes happening in the Nigerian fertilizer market.
Our objective in the DAI Farmer-Centric Data Governance Project is to demonstrate opportunities of emerging user-centric models and actions required to create an enabling environment of socio-technical factors needed for implementation.