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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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Do Outsourcing Clients Want Decent Digital Work?

22 December 2017 Leave a comment

There are growing concerns that digital gig work – supplied by platforms like Mechanical Turk, Upwork, Freelancer, etc – falls short of decent work standards.  (For further details see the working paper, “Decent Work and the Digital Gig Economy”.)  To address this, and as discussed previously in this blog, there are plans to encourage new ethical standards.

But almost all evidence on this to date comes from workers.  The voices of only a few platforms have been heard, and there seems to be no evidence from clients.  Yet clients are central to decent digital work standards: if they create incentives for platforms to improve, that will be a powerful motivation.  Conversely, if clients don’t care, it removes a key driving force from the gig economy ecosystem.

So, what evidence can be found?

Here, I summarise Babin, R., & Myers, P. (2015) Social responsibility trends and perceptions in global IT outsourcing, Proceedings of the Conference on Information Systems Applied Research, v8, n3663.  This in turn summarises results from surveys conducted during 2009-2014 by the International Association of Outsourcing Professionals.

The survey was specifically about corporate social responsibility (CSR) in IT outsourcing.  So: a) it is not exactly about digital gig work but a broader category of outsourcing; b) the survey may encourage some level of “virtue signalling”: respondents wanting to appear more socially-responsible than they are in reality.  Nonetheless, it offers some relevant guidance about client attitudes to decent digital work.

In general terms, half the respondents were US-based; half were non-US; a fair reflection of gig work clients.  They ranged from SMEs to multinationals and just over half had a written CSR policy.  They are thus larger and more formally-CSR-inclined than the modal micro-enterprise client for digital gig work, but important given the increasing involvement of firms in gig outsourcing.

Key findings include the following:

– Nearly half “often” or “always” gave preference to outsourcing providers who had demonstrable CSR capability.

– Nearly two-thirds expected CSR consideration to become “more” or “much more” important in their future IT outsourcing.

– The largest factor in evaluating CSR capabilities of an outsourcing provider was its labour practices (see figure below).

Figure: Key factors in evaluating the CSR capabilities of an outsourcing provider, survey median (IAOP, 2009-14)

At least for this group of clients, then, the type of labour practices covered by proposed decent digital work standards were the top CSR issue; and CSR was quite widespread as a determinant in digital-related outsourcing (only 5% said they never used CSR as a determinant).

This gives some basis for believing – at least among larger clients for digital gig work – that an appetite exists for better employment and working conditions; an appetite that can encourage platforms to change.

Adding ICT4D To Your Curriculum

29 November 2017 1 comment

ICT4D – information and communication technology for development – courses are increasingly being added to the curriculum for undergraduate and postgraduate programmes in development studies, information systems, business studies, computer science, etc.  Why?

First, the size and growth of the ICT4D domain.  US$ billions are being spent each year: by individual users at the base of the pyramid; by telecom and digital firms reaching BoP markets; by governments and NGOs and social enterprises serving those markets; by donor agencies.  Annual growth rates – for mobile and smartphones, for broadband – are always double-digit.

Second, the urgency and morality of the goals that ICT4D serves.  ICT4D means using digital technology to address the world’s most pressing problems: poverty, inequality, disease, illiteracy, corruption, climate change, etc.

Third, the buzz of ICT4D innovation.  There is both a need and exciting agenda for innovation that engages students: not just technical developments but also new business and social models.

If you want to add an ICT4D course to your curriculum, it is now easy to do so.  The Routledge textbook supporting such a course, “Information and Communication Technology for Development (ICT4D)” is available in physical and e-book formats via: https://www.routledge.com/Information-and-Communication-Technology-for-Development-ICT4D/Heeks/p/book/9781138101814

The textbook uses extensive in-text diagrams, tables and boxed examples with chapter-end discussion and assignment questions and further reading.  Online resources (slide packs, session outlines and notes) support use for teaching and training purposes, and inspection copies can be requested for those planning to adopt the book as a core text.

The book is designed for use around a ten-session course, but can be modified for alternative course designs:

  1. Understanding ICTs and Socio-Economic Development
  2. Foundations of ICTs and Socio-Economic Development
  3. Implementing ICT4D
  4. ICTs and Economic Growth
  5. ICTs, Poverty and Livelihoods
  6. ICTs and Social Development
  7. e-Governance and Development
  8. ICTs and Environmental Sustainability
  9. The Future of ICT4D
  10. *Visit / External Speaker / Lab Session*
Categories: Teaching ICT4D Tags: ,

Decent Digital Work and the FairWork Foundation

31 October 2017 1 comment

How can we improve standards for digital gig workers: those undertaking micro-work and online freelancing via platforms like Upwork and Mechanical Turk?

The recent research paper – “Decent Work and the Digital Gig Economy” – explains why such standards are needed.  With up to 70m workers worldwide registered for online work and growth rates of 20-30% per year, this is already a sizeable activity.  It is especially popular with the c.80% of workers based in middle- and low-income countries, who often see online work as better than local alternatives.  However, this ignores the chronic precarity and structural inequality associated with such work: damaging outcomes that will only spread if nothing is done.

But what should be done?

The paper develops an inventory of “Decent Digital Work” standards.  This is a comprehensive set of guidelines that integrates two things: first, the global decent work standards set by the ILO; second, the actions needed to address specific digital gig economy problems.

A key value for this inventory is as a comparator with other decent work initiatives.  For example, the paper analyses the way in which two major initiatives – SA8000, and the Ethical Trading Initiative – do and do not cover the requirements for decent digital work.

Below, a further comparison is undertaken, between the Decent Digital Work standards, and the criteria adopted by the FairWork Foundation; an initiative aiming to rate and certify gig economy platforms.  The table indicates those elements which are the same in both standards; those where a completely-different element is included; and those where there is some variation in the element.

From this, three things can be concluded:

a) A number of Decent Digital Work standards are absent in the FairWork Foundation certification criteria. Several of these relate to the broader context for work, would be outwith the scope of an individual platform, and therefore are not relevant to platform certification. However, those identified under “Employment” and “Work Conditions” can form part of a further discussion to consider their relevance to certification.

b) Some elements (e.g. around access to digital work opportunities, and accounting for worker costs other than unpaid time) speak to the particular conditions of gig workers from the global South. This is the location for the great majority of gig workers: already for digital gig workers; increasingly for physical gig workers. As such, the FairWork Foundation must ensure its global North origins do not skew its focus.

c) The FairWork Foundation should review how prevalent the non-competition and non-disclosure agreement problems are, and whether they are worth including. (Human review of task instructions – something unlikely to be practicable for most platforms – appears to have been dropped from later versions of the certification criteria; hence, its inclusion in brackets.)

As noted in the Decent Work and the Digital Gig Economy paper’s action research agenda, next steps here would be:

– Survey of worker, client and platform views about identified standards.

– A multi-stakeholder dialogue to agree a minimum set of certification standards and evaluation methods.

– Parallel research on the impact of standards and certifications in the gig economy, and analysing the costs and benefits of interventions such as standards and certifications at micro- and macro-level.

This is just one example of the application of the Decent Digital Work standards.  We hope you can identify other uses . . .

How Big Data Changes the Locus of Organisational Power

19 September 2017 Leave a comment

Big data can lead centres of power in organisations to move.  Recent research on this was undertaken in an Indian state electricity corporation (“Stelcorp”), reported in the paper, “Exploring Big Data for Development: An Electricity Sector Case Study from India”.

This found three shifts to be occurring, as illustrated.

Power Shifts Associated with Big Data

1. From Public to Private. Previously, Stelcorp was responsible for its own data and data systems. As a result of sectoral reforms, private firm “Digicorp” was brought in.  While de jure control remains with Stelcorp, de facto control has shifted to Digicorp.  Digicorp controls knowledge of the design, construction, operation and maintenance of the data systems; it operates those systems; and Stelcorp staff have been reduced to a clerical service role.  In theory, Digicorp could be replaced.  But as seen in other public-private partnerships, in practice there is an asymmetry of power and dependency that has locked in the private partner.

2. From Workers to Managers. With the introduction of online meters for bulk and urban electricity consumers, requirement for human meter-readers has fallen. As a result, during 2013-2016, 40% of meter-readers lost their jobs.  For the remainder, the writing is on the wall – online metering will spread throughout the rest of the electricity network, and their jobs will slowly but steadily be automated out of existence.  For those that remain, the data they collect is less critical than previously, as it forms a declining proportion of all meter data; and they have less control when reduced to just capturing data on hand-held devices (they barely own and access this data and do not use or regulate it).  As a result, Stelcorp managers are decreasingly resource-dependent on the meter-readers and power has shifted away from the latter and towards the former.

3. From Local to Central Managers. The advent of big data led to creation of a central Finance and Energy Management Unit (FEMU). Previously, managers at divisional and zonal levels would be accountable to their immediate superiors, typically within that level: it was those superiors who collected performance data on the managers and negotiated its implications, and those superiors who held power over their junior managers.  Data was relatively “sticky”; tending to be restricted to localised enclaves within the organisation.  This is no longer the case.

Now, all forms of data flow readily to FEMU.  It sees all that goes on within Stelcorp (at least to the extent reflected by current online data) and is able to drill down through zonal and divisional data to individual assets.  It holds regular performance meetings with Stelcorp managers, and has introduced more of an audit and performance management culture.  As a result, managers now largely see themselves as accountable to FEMU.

For further details, including the models of resource dependency and data-related power that underpin this analysis, please refer to the working paper on this topic.

Big Data and Electoral Politics in India

What happens when big data and big politics collide?  One answer arises from a recent study of big data in the electricity distribution sector in an Indian state: “Exploring Big Data for Development: An Electricity Sector Case Study from India”.

[1]

The state electricity corporation has introduced millions of online digital meters that measure electricity flow along the distribution network and down to the level of consumers.  Producing a large stream of real-time data, these innovations should have addressed a critical problem in India: theft / non-payment by consumers which creates losses up to one-third of all supplied power.  But they did not.  Why should that be?

Big data does reduce some losses: technical losses from electrical resistance and faults are down; payment losses from urban consumers are down.  But the big data era has seen an unprecedented expansion of rural electrification, and in rural areas, payment losses have risen to 50% or more.  In other words, the corporation receives less than half the revenue it should given the electricity it is supplying to rural areas.

The expansion in rural electrification has been mandated by politicians.  The high level of rural payment losses has been condoned by politicians, given significant positive association between levels of electricity non-payment and likelihood of seat retention at an election.

Is this the silencing of big data in the face of big politics: the capability for accurate metering and billing of almost all consumers simply being overridden by electoral imperatives?  Not quite, because big data has been involved via an offsetting effect, and an epistemic effect.

  1. Offsetting Effect. Big data-driven technical and urban consumer loss reductions have allowed the State Government to “get away” with its political approach to rural electrification. The two effects of technical/urban loss reduction and political loss increase have roughly balanced one another out; a disappointing aggregate outcome but one that just falls under the threshold that would trigger some direct intervention by the regulators or by Central Government.
  2. Epistemic Effect. Big data creates a separate virtual model of phemonena: a so-called “data double”. This in turn can alter the “imaginaries” of those involved – the mental model and worldviews they have about the phenomena – and the wider discourse about the phenomena.
    This has happened in India.  Big data has created a new imaginary for electricity, particularly within the minds of politicians.  Before big data, the policy paradigm was one that saw electricity in terms of constraint: geographic constraint such that not all areas could be connected, and supply constraint such that “load-shedding” – regular blackouts and brownouts – was regarded as integral.
    After big data, the new paradigm is one of continuous, high-quality, universal electricity.  Plans and promises are now based on the idea that all districts – and all voters – can have 24 x 7 power.

In sum, one thing we know of digital systems is that they have unanticipated consequences.  This has been true of big data in this Indian state.  Far from reducing losses, the data-enabled growth in electricity connectivity has helped fuel a politically-enabled growth in free appropriation of electricity.

For further details, please refer to the working paper on this topic.

[1] Credit: Jorge Royan (Own work) CC-BY-SA-3.0, via Wikimedia Commons https://commons.wikimedia.org/wiki/File:India_-_Kolkata_electricity_meters_-_3832.jpg

The Affordances and Impacts of Data-Intensive Development

What is special about “data-intensive development”: the growing presence and application of data in the processes of international development?

We can identify three levels of understanding: qualities, affordances, and development impacts.

A. Data Qualities

Overused they may be but it still helps to recall the 3Vs.  Data-intensive development is based on a greater volume, velocity and variety of data than previously seen.  These are the core differentiating qualities of data from which affordances and impacts flow.

B. Data Affordances

The qualities are inherent functionalities of data.  From these qualities, combined with purposive use by individuals or organisations, the following affordances emerge[1]:

  • Datafication: an expansion of the phenomena about which data are held. A greater breadth: holding data about more things. A greater depth: holding more data about things.  And a greater granularity: holding more detailed data about things.  This is accelerated by the second affordance . . .
  • Digitisation: not just the conversion of analogue to digital data but the same conversion for all parts of the information value chain. Data processing and visualisation for development becomes digital; through growth of algorithms, development decision-making becomes digital; through growth of automation and smart technology, development action becomes digital.  Digitisation means dematerialisation of data (its separation from physical media) and liquification of data (its consequent fluidity of movement across media and networks), which underlie the third affordance . . .
  • Generativity: the use of data in ways not planned at the origination of the data. In particular, data’s reprogrammability (i.e. using data gathered for one purpose for a different purpose); and data’s recombinability (i.e. mashing up different sets of data to get additional, unplanned value from their intersection).

C. Data-Intensive Development Impacts

In turn, these affordances give rise to development impacts.  There are many ways in which these could be described, with much written about the (claimed) positive impacts.  Here I use a more critical eye to select four that can be connected to the concept of data (in)justice for development[2]:

i. (In)Visibility. The affordances of data create a far greater visibility for those development entities – people, organisations, processes, things, etc. – about which data is captured. They can more readily be part of development activity and decision making.  And they can also suffer loss of privacy and growth in surveillance from the state and private sector[3].

Conversely, those entities not represented in digital data suffer greater invisibility, as they are thrown further into shadow and exclusion from development decision-making.

Dematerialisation and generativity also make the whole information value chain increasingly invisible.  Data is gathered without leaving a physical trace.  Data is processed and decisions are made by algorithms whose code is not subject to external scrutiny.  The values, assumptions and biases inscribed into data, code and algorithms are unseen.

ii. Abstraction. A shift from primacy of the physical representation of development entities to their abstract representation: what Taylor & Broeders (2015) call the “data doubles” of entities, and the “shadow maps” of physical geographies. This abstraction typically represents a shift from qualitative to quantitative representation (and a shift in visibility from the physical to the abstract; from the real thing to its data imaginary).

iii. Determinism.  Often thought of in terms of solutionism: the growing use of data- and technology-driven approaches to development.  Alongside this growth in technological determinism of development, there is an epistemic determinism that sidelines one type of knowledge (messy, local, subjective) in favour of a different type of knowledge (remote, calculable and claiming-to-be-but-resolutely-not objective).  We could also identify the algorithmic determinism that increasingly shapes development decisions.

iv. (Dis)Empowerment. As the affordances of data change the information value chain, they facilitate change in the bases of power. Those who own and control the data, information, knowledge, decisions and actions of the new data-intensive value chains – including its code, visualisations, abstractions, algorithms, terminologies, capabilities, etc – are gaining in power.  Those who do not are losing power in relative terms.

D. Review

The idea of functionalities leading to affordances leading to impacts is too data-deterministic.  These impacts are not written, and they will vary through the different structural inscriptions imprinted into data systems, and through the space for agency that new technologies always permit in international development.  Equally, though, we should avoid social determinism.  The technology of data systems is altering the landscape of international development.  Just as ICT4D research and practice must embrace the affordances of its digital technologies, so data-intensive development must do likewise.

[1] Developed from: Lycett, M. (2013) ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381-386; Nambisan, S. (2016) Digital entrepreneurship: toward a digital technology perspective of entrepreneurship, Entrepreneurship Theory and Practice, advance online publication

[2] Developed from: Johnson, J.A. (2014) From open data to information justice. Ethics And Information Technology, 16(4), 263-274; Taylor, L. & Broeders, D. (2015) In the name of development: power, profit and the datafication of the global South. Geoforum, 64, 229-237; Sengupta, R., Heeks, R., Chattapadhyay, S. & Foster, C. (2017) Exploring Big Data for Development: An Electricity Sector Case Study from India, GDI Development Informatics Working Paper no.66, University of Manchester, UK; Shaw, J. & Graham, M. (2017) An informational right to the city? Code, content, control, and the urbanization of information. Antipode, advance online publication http://onlinelibrary.wiley.com/doi/10.1111/anti.12312/full; Taylor, L. (2017) What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level, TILT, Tilburg University, Netherlands  http://dx.doi.org/10.2139/ssrn.2918779

[3] What Taylor & Broeders (2015) not entirely convincingly argue is a change from overt and consensual “legibility” to tacit and contentious “visibility” of citizens (who now morph into data subjects).

 

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