There is a growing ‘datafication’ within global development, where main actors like NGOs, donors, scholars and private companies use new technologies to generate large amount, machine-readable, and often in real-time, data for intervention purposes. Similar to AirBnB which disrupts travel industry, datafication within global development is regarded as silver lining for development, either to improve existing intervention practices or even create completely new ones (e.g. big data analysis for predicting election result). Number of datafication terms are widely used by development actors such as data-intensive development, big data for development, data for social good, citizen-generated data, and open data for development.
The current debates is centred around what could be the potential benefits of data for development interventions, especially big data and open data. Its flaws and negative consequences is often ignored. The main question is would citizens in middle and lower-income countries be able to reap these promises?
The promising benefits
There are four main areas where data can have impacts in our society, either direct and indirect, shown in table below:
|Economic||Monetary value created for companies, people and governments||Profits, income and tax revenue generated from software development|
|Governance||Value created through improved government efficiency and effectiveness, and transparency & accountability||Responsive disaster efforts using recommendations from modelling software|
|Human Development||Value created through advancing measures of development, and cultural and social value that may be created or lost||Targeted health assistance using data generated from mobile use pattern|
|Right||Individual or societal utility allowed through freedoms of speech, expression, movement, and through privacy and the right to protection by law||Public awareness from incidents of sexual harassment mapped online|
|Environment||Intrinsic value of the environment and the utility gained through environmental benefits||Policy change from air quality software|
The other side of the coin
While sounds promising, the benefits from data-centric development, especially in economic form, leans toward high income countries . Middle and lower-income countries, characterised by socio-economic and political inequality as well as digital divide, may face its dual impacts: gaining or losing values. Some downside examples are job opportunities threatened by automation (economic), discrimination based on online profile (human development) and reduced privacy due to lack of government accountability and protection laws (right).
Majority of low or lower-middle-income countries do not have privacy or data protection laws in place. In 2013, only 8 states out of 55 in Sub-Saharan Africa had data protection laws . When individuals and citizens are not in charge over their data nor how they are represented, data only serves for private companies whose fully or partially in possession of data collection, interpretation and dissemination.
Multinational technology firms enjoy the most economic benefits of datafication practice that shapes global development . Under PPP or similar arrangement, ‘corporations act as development donors’ and thus reduces the power of “old” players: donors, government or international NGOs.
Fail or prevail?
To date, many data-centric development initiatives has become problematic. Data collection and analysis, usually morphed as mobile apps, has become the end goals rather than solving the root causes of problems. Morozov  describes this phenomenon as solutionism that is “the application of engineering solutions to problems long-term and structural in nature”. They repeat the failure of techno-centric approach at recognising the importance of context.
That is why “ they produce data but only rarely produce results“. Data itself only creates value when people transform it into information which then used to make decisions and actions.
But then how to make data-centric interventions prevail? First, acknowledging information value chains explicitly in projects design and implementation. Second, a more multidisciplinary approach involving wide-range participants with expertise in information systems, organisation development and political economy.