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Quote of the Week: Philip Stephens

Sina Odugbemi's picture

So what follows, Snowden? On the evidence so far, not a great deal. The biggest heist in the history of intelligence was supposed to change the world – to expose the calumnies of the secret state and redraw the boundaries in favour of individual liberty. For all the sound and fury, little has changed.”

- Philip Stephens, associate editor and chief political commentator of  the Financial Times.

Data and Development

Mahmoud Mohieldin's picture

WASHINGTON, DC – Since the turn of the century, the international development community has rallied behind the Millennium Development Goals, which set specific targets in eight key areas, including poverty, child mortality, and disease, to be achieved by 2015. In formulating the post-2015 development agenda, measuring the MDGs’ successes – and identifying where progress has lagged – is critically important. And that demands more and better data.

To be sure, international institutions and many developing countries have invested significantly in improving data collection to track better their performance against MDG targets. In 2003, only four countries had two data points for 16 or more of the 22 principal MDG indicators; by last year, that figure had soared to 118 countries.

But development data remain a scarce resource in the developing world. Given their value in measuring – and propelling – social and economic progress, this shortage must be addressed urgently. A catalyst is needed to expand the production and use of development data. With this in mind, the high-level panel on the post-2015 development agenda is right to call for a global “data revolution.”

EdStats: Big Data, Better Policies, Learning for All

Husein Abdul-Hamid's picture

Are we effective in presenting education data to help tackle the real issues that developing countries are facing? The education community continues to be puzzled by two realities: (1) crucial data is often not available and (2) available data is often hard to digest.

Anecdotes and Simple Observations are Dangerous; Words and Narratives are Not.

Heather Lanthorn's picture

In a recent blog post on stories, and following some themes from an earlier talk by Tyler Cowen, David Evans ends by suggesting: “Vivid and touching tales move us more than statistics. So let’s listen to some stories… then let’s look at some hard data and rigorous analysis before we make any big decisions.” Stories, in this sense, are potentially idiosyncratic and over-simplified and, therefore, may be misleading as well as moving. I acknowledge that this is a dangerous situation.

However, there are a couple things that are frustrating about the above quote, intentional or not.

  • First, it equates ‘hard data’ with ‘statistics,’ as though qualitative (text/word) data cannot be hard (or, by implication, rigorously analysed). Qualitative twork – even when producing ‘stories’ – should move beyond mere anecdote (or even journalistic inquiry).
  • Second, it suggests that the main role of stories (words) is to dress up and humanize statistics – or, at best, to generate hypotheses for future research. This seems both unfair and out-of-step with increasing calls for mixed-methods to take our understanding beyond ‘what works’ (average treatment effects) to ‘why’ (causal mechanisms) – with ‘why’ probably being fairly crucial to ‘decision-making’ (Paluck’s piece worth checking out in this regard).

Weekly Wire: The Global Forum

Roxanne Bauer's picture

These are some of the views and reports relevant to our readers that caught our attention this week.

Big data: 4 predictions for 2014
The Guardian
"One could look back at 2013 and consider it the breakthrough year for big data, not in terms of innovation but rather in awareness. The increasing interest in big data meant it received more mainstream attention than ever before. Indeed, the likes of Google, IBM, Facebook and Twitter all acquired companies in the big data space. Documents leaked by Edward Snowden also revealed that intelligence agencies have been collecting big data in the form of metadata and, amongst other things, information from social media profiles for a decade." READ MORE

The rise of civil society groups in Africa
Africa Renewal
"Under the glaring sun of a recent Monday, an unusual group of protesters marched on the streets of Kampala, Uganda’s capital, all dressed in black “to mourn the loss of Uganda’s public money through corruption,” as some of them pointedly explained to reporters. “Return our money and resign,” read one of the slogans they brandished. Since November 2012, on the first Monday of each month, the Black Monday Movement—a coalition of local NGOs and civil society groups—has taken to the streets to highlight the effects of corruption in Uganda and to press public officials to act."  READ MORE

#4 from 2013: Numbers Are Never Enough (especially when dealing with Big Data)

Susan Moeller's picture

Our Top Ten Blog Posts by readership in 2013
This post was originally published on January 8, 2013

The newest trend in Big Data is the personal touch.  When both the New York Times and Fast Company have headlines that trumpet: “Sure, Big Data Is Great. But So Is Intuition.” (The Times) and “Without Human Insight, Big Data Is Just A Bunch Of Numbers.” (Fast Company) you know that a major trend is afoot.

So what’s up?

The claims for what Big Data can do have been extraordinary, witness Andrew McAfee and Erik Brynjolfsson’s seminal article in October in the Harvard Business Review: “Big Data: The Management Revolution,” which began with the showstopper:  “‘You can’t manage what you don’t measure.’”  It’s hard not to feel that Big Data will provide the solutions to everything after that statement.  As the HBR article noted:  “…the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.”

The DIQA initiative: Supporting the silent data revolution in the Arab world

Sebastian Trenner's picture

DIQA - Photo: Arne Hoel/World Bank

Statistical agencies in the Middle East and North Africa have now started to open up access to their raw datasets (micro-data). In a break with their old ways, they have begun either to post them on their websites or to share them on a bilateral basis. To support this wind of change, a group of donors active in the statistical domain and avant-garde partner countries joined forces for the first time to launch the Data Improvement and Quality in Access initiative (DIQA – which reads as “precision” in Arabic).

Toing and Froing in Freetown

Mark Roland Thomas's picture

Countries coming out of crises undergo rapid structural changes, including migration and big economic shifts. This can complicate the measurement of their progress, sometimes in unexpected ways, as we found out recently in Sierra Leone.

Thanksgiving Woes? IBM and Big Data May Help.

Tanya Gupta's picture

In just about a week, on Thursday November 28, people all over the United States will kick off the "holiday season" with the celebration of Thanksgiving Day. While the day's significance is both historical and profound, in modern times it consists of a lot of shopping and a big meal with family and friends gathered around the dinner table. Pre-thanksgiving is a time to be on the lookout for creative new recipes.  Sure, we can get recipes from magazines, websites and friends and while they may be special, they will not be unique.  Wouldn’t it be nice to have an app that would create a special unique recipe just for you? A delightful recipe that has never been executed before.  Well the idea is not as futuristic as it sounds. It may be here sooner than you think.  IBM and big data have a lot to do with this particular innovation.
Can computers be creative?  IBM thinks they can.  IBM scientists Lav R. Varshney and other members of an IBM team, have used data sets and proprietary algorithms in the daunting field of the culinary arts to develop a computational creativity system. The data sets they have used are recipes, molecular level food related data and data about the compounds, ingredients and dishes that people like and dislike.  They then developed an algorithm that produces thousands or millions of new ideas from the recipes.  The recipes are then evaluated to select the best ones that combine ingredients in a way that has never been attempted before.  Humans can interact with the system by choosing a key ingredient and the kind of cuisine.