WE live today in the digital age. The ongoing data revolution is radically transforming the way individuals, organisations, and societies operate and make decisions.

A social sector powered by data directs resources to development projects demonstrating the greatest impact, gives voice to the beneficiaries, and fosters a learning environment where failures are corrected and successes promoted. However, in Pakistan, the social sector and development sector projects are stymied by an insufficient capacity to collect and analyse the necessary data to evaluate the impact of their endeavours.

The first step towards building the infrastructure for data-driven projects is strengthening the monitoring and evaluating (M&E) process. This process of systematically collecting data to assess and enhance the efficiency of projects is often sidelined, understaffed, under-resourced, and under-budgeted. More importantly, when M&E teams collect data, the next step should be utilising that information to reformulate strategies and boost operations.

The second step entails directing resources towards measuring outcomes rather than outputs and inputs. The latter reveals information only about the processes and operations of the project, but not the impact.

Poor data collection stymies the social sector.

For example, stating that a programme trained 1,000 teachers is a measured output which reveals nothing of the quality of training. An example of a measurable outcome, on the other hand, could be improved literacy skills. However, projects often fail to clearly distinguish between outputs and outcomes, or focus resources only on primarily measuring the former and thereby neglecting the latter.

Third, outcomes should be quantitatively defined, measureable, and concrete. Many projects formulate abstract and vague outcomes that defy measurement and therefore cleverly evade accountability measures. Some may contend that not all changes and impact can be quantitatively assessed.

This assertion may be true, but when the money is delivered in the form of aid from donors, private or public, then that impact which is too abstract to be measured should not be pursued. Firstly, a project that chases abstract ideals and aims to change hearts and minds comes dangerously close to indoctrinating others with one’s own subjective value system. Secondly, because abstract outcomes cannot be assessed at the system level, it is far too easy to cherry pick examples where the project succeeded while ignoring instances where it failed.

In addition, differentiating between actual success and mediocrity becomes a challenge, and project implementers bask in their undetected incompetence.

The solution is embedding in the design of the project the process of collecting, analysing, and freely disseminating data. The availability of big data allows organisations and individuals to better understand operations on the ground and make more evidence-in­formed decisions on how to make things better. In fact, the role of data analytics advances in tandem with innovative initiatives to leverage technology to deliver development solutions.

For example, mobile technology is considered a highly cost effective way for NGOs, governments, and private companies to reach their beneficiaries and deliver their services or products. Moreover, the use of mobile technologies simultaneously generates rich and real-time data on how users receive and benefit from the services delivered.

Measuring outcomes and the cost per outcome enables decision-makers to quickly assess what works and what does not work in the development sector and consequently maximise their return on investment. In fact, algorithmic data can even allow decision-makers to not only design more effective strategies and mitigate risk, but also predict the success of a project before it starts.

Without data, there is no information feedback loop between the beneficiaries and projects. Beneficiaries’ voices and experiences are stifled, and there is no body of evidence to assess whether the services development projects deliver are actually what the beneficiaries need.

Information asymmetries, limited access to information, and a weak information infrastructure cultivates an environment fertile for suppositions, baseless theories, and hubris. The result is repetition and replication of obsolete models of development that are upheld which would have disintegrated if subjected to careful, data-driven scrutiny.

Strengthening the capacity of projects and government agencies to manage data undoubtedly necessitates heavy investment in building the capabilities of implementing actors to collect, analyse, and disseminate data. However, investing in building that capacity lays the groundwork for building institutions as a whole. Moreover, the majority of large scale projects are far from cash-strapped to allocate substantial financial resources to strengthening their monitoring and evaluating systems and staff.

Ultimately, harnessing the power of data to strengthen the social sector will increase transparency, accountability, and in turn will incentivise the social sector to deliver real, meaningful, and measurable impact.

The writer has worked in an education development project in Pakistan.

Published in Dawn, January 20th, 2015

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