As my work covers many different subject areas and communities, I spend a fair amount of time following trends in these different areas and how they impact my central focus of Engaging Internationally (not coincidentally the name of this blog). Recently I have been following the evolution of the impact assessment field and most specifically discussions around “Data”. Data Revolution, Open Data, Big Data, Data Shift, you may have heard or been following some of these discussions yourself.   The following is some of what I have been tracking. Although I have categorized these short summaries according to these different ‘phrases’ there is obviously a lot of linkage among them.

  • Big Data – Through computer collected and aggregated means we now have data coming at us in faster and seemingly endless ways. But the story of the increasing availability of this ‘raw data’ is of course more complicated in reality and application than it may seem. I recently attended a session “Big Data and its role n Evaluation” moderated by Linda Raftree of Tech Salon. During this session it was suggested that the term can be used to mean either (1) A very large amount of data and/or (2) Tools that come out of computer science that are increasingly merging with social science to create ‘data science’.   In the discussion of the latter it was noted that the computer science and social science communities often have different perspectives, and that finding commonalities will be one of the important aspects of making Big Data useful as a tool in developing and evaluating programs designed to improve people’s lives. It was also noted that one of the current gaps in Big Data it is that collecting information via technology is often limited in low resource or low connectivity areas. The group generally agreed that although collecting and using Big Data is most likely an important tool (especially for larger projects with more variables and/or complexity) its use should be context specific and just one of other methods considered and/or used.
  • Open Data – The notion of open data and specifically open government data, information (public or otherwise) which anyone is free to access and re-use for any purpose , has been around for some years. In 2009 open data started to become visible in the mainstream, with various governments (such as the USA, UK, Canada and New Zealand) announcing new initiatives towards opening up their public information. This movement has accelerated, and more pressure is being placed on governments due to forces such as (1) The increasing ease of making data accessible to the public online; and (2) Global movements such as the Sustainable Development Goals and the Data Revolution (see more below).
  • DataShift and people centered data is a movement to support more citizen-generated data as an alternative or complement to data generated by governments and international institutions. It is hoped that the initiatives that create citizen-generated data can also empower people, giving them ways to engage with political processes that may otherwise seem too removed from their lives.
  • Data Revolution – This is a term that is primarily being used at the global level by international organizations such as the UN to apply to finding ways to evaluate and monitor the implementation of the Sustainable Development Goals or SDGs.   The UNData revolution project focuses on ways to deal with the inequalities between developed and developing countries and between data-poor and data-rich people.
  • Data Philanthropy – A term which describes a partnership in which private sector companies share data for public benefit  emerged as a specific concept at the World Economic Forum in Davos in 2011. Since then different companies have tried out pilot projects and considered how it might be another form of their Corporate Philanthropy.
  • Data Visualization (#dataviz) – is the presentation of data in a pictorial or graphical format. Not new per se, it has taken on a new life through the ease of creating computer generated graphics and a way to display large amount or complicated data. Here’s a piece on making your data visuals more effective & memorable.

Some general ideas from these data discussions:

  • Data is a ‘thing’; we always need to be aware of what dynamic process we are going to use to collect , analyze, and use it.
  • The collecting and analyzing of data in its increasing complexity is calling for more cross-sector discussion and cooperation which we are beginning to see more of through efforts such as Open Data, the Data Revolution and Data Philanthropy.

And like so much of what we need to do to be more effective in developing and evaluating programs, our use of data in the social sector comes down to critical analysis and asking probing questions such as:  Under what conditions do you want to use which collection and analytical tools? What might be the bias to the data you’ve collected? What might be missing? Who might you want to partner with and under what conditions? What’s actionable about the data you have? and others you will come up with to help you use data to fit your own goals and context.

Additional resources

Open Data Handbook

Open Data and the SDGs podcast

People powered data

Monitoring and Evaluation in a Tech-Enabled World

How to Secure Africa’s Data Revolution

Data Science for Good


Guide for Effective Data Collection