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An Applied Data Justice Framework for Datafication and Development

Data is playing an ever-growing role in international development.  But what lens can we use to analyse the impact of data on development?

The emerging field of “data justice” offers some valuable ideas but they have not yet been put together into a systematic and comprehensive framework.  My open-access paper – Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework, written with Satyarupa Shekhar – provides such a framework, as shown below.

The framework exposes five dimensions of data justice:

  • Procedural: fairness in the way in which data is handled.
  • Instrumental: fairness in the results of data being used.
  • Rights-based: adherence to basic data rights such as representation, privacy, access and ownership.
  • Structural: the degree to which the interests and power in wider society support fair outcomes in other forms of data justice.
  • Distributive: an overarching dimension relating to the (in)equality of data-related outcomes that can be applied to each of the other dimensions of data justice.

The dimensions can be used individually; for example, just to analyse data practices, or just to analyse the impact of context on new data systems in developing countries.  Or the model can be used holistically; for example, to understand the full development impact of a particular data initiative.

The Datafication, Development and Marginalised Urban Communities: An Applied Data Justice Framework paper takes the latter route.  It analyses “pro-equity data initiatives” that were implemented by data activists in four cities: Chennai, Nairobi, Pune and Surakarta.  These initiatives specifically sought to address the data injustices suffered by slum dwellers and other marginalised groups; particularly their invisibility to urban planners and other external agencies.

Using the data justice lens, this research finds that new data flows do have a positive impact in counteracting the injustice of invisibility, but they disproportionately serve those with the motivation and power to use that data.  Results in terms of service improvements and epistemic change are beneficial for slum communities and other marginalised citizens, and these initiatives can be justified on that basis.

However, though there can be no exact calibration from qualitative research, it is likely that these pro-equity initiatives actually increase relative inequalities.  Ordinary community members have seen some benefits but external actors who find the data to match their agenda and capabilities, benefit more.  It is the latter who are more empowered to access, use and control the new data.

If you would like to know more about this research’s findings, framework and recommendations for practice, then take a look at the paper: https://www.tandfonline.com/doi/full/10.1080/1369118X.2019.1599039

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Big Data and Urban Transportation in India

12 February 2018 Leave a comment

What effect are big data systems having on urban transportation?

To investigate this, the Centre for Internet and Society was commissioned by the Universities of Manchester and Sheffield, to conduct a study of the big data system recently implemented by the Bengaluru Metropolitan Transport Corporation (BMTC).  The “Intelligent Transport System” (ITS) took three years to reach initial operational status in 2016, and now covers the more than five million daily passenger journeys undertaken on BMTC’s 6,400 buses.

ITS (see figure below) processes many gigabytes of data per day via three main components: vehicle tracking units that continuously transmit bus locations using the mobile cell network; online electronic ticketing machines that capture details of all ticketing transactions; and a passenger information system with linked mobile app to provide details such as bus locations, routes and arrival times.

ITS Architecture (Mishra 2016)[1]

At the operational level the system is functioning moderately well: the data capture and transmission components mainly work though with some malfunctions; and the passenger-facing components are present but have data and functionality challenges that still need to be fully worked-through.  Higher-level use of big data for tactical and strategic decision-making – optimising routes, reducing staff numbers, increasing operational efficiency – is intended, but not yet evidenced.

Just over a year since full roll-out, this is not unexpected but it is a reminder that big data systems take many years to implement: in this case, at least four years to get the operational functions working, and years more to integrate big data into managerial decision-making.

Nonetheless some broader impacts can already be seen.  Big data has changed the mental model – the “imaginary” – that managers and politicians have of bus transport in Bengaluru.  Where daily operations of the bus fleet and bus crews were largely opaque to management prior to ITS, now they are increasingly visible.  Big data is thus changing the landscape of what is seen to be possible within the organisation, and has already resulted in plans for driver-only buses, and a restructuring that is removing middle management from the organisation: a layer no longer required when big data puts central management in direct contact with the operational front line.

Big data is also leading to shifts in power.  Some of these are tentative: a greater transparency of operations to the general public and civil society that may receive a step change once ITS data is openly shared.  Others are more concrete: big data is shifting power upwards in the organisation – away from front-line labour, and away from middle managers towards those in central management who have the capabilities to control and use the new data streams.

For further details of this study, see Development Informatics working paper no.72: “Big Data and Urban Transportation in India: A Bengaluru Bus Corporation Case Study”.

[1] Mishra, B. (2016) Intelligent Transport System (ITS), presentation at workshop on Smart Mobility for Bengaluru, Bengaluru, 10 Jun https://www.slideshare.net/EMBARQNetwork/bmtc-intelligent-transport-system

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