Archive for August, 2016

Measuring Barriers to Big Data for Development

How can we measure the barriers to big data for development?  A research paper from Manchester’s Centre for Development Informatics suggests use of the design-reality gap model.

Big data holds much promise for development: to improve the speed, quality and consistency of a wide variety of development decisions[1].  At present, this is more potential than actuality because big data initiatives in developing countries face many barriers[2].

But so far there has been little sense of how these barriers can be systematically measured: work to date tends to be rather broad-brush or haphazard.  Seeking to improve this, we investigated use of an ICT4D framework already known for measurement of barriers: the design-reality gap model.

In its basic form the model is straightforward:

  • It records the gap between the design requirements or assumptions of big data vs. the current reality on the ground.
  • The gap is typically recorded on a scale from 0 (no gap: everything needed for big data is present) to 10 (radical gap: none of the requirements for big data is present).
  • The gap can be estimated via analysis of researchers, or derived directly from interviewees, or recorded from group discussions.
  • It is typically measured along seven “ITPOSMO” dimensions (see below).

As proof-of-concept, the model was applied to measure barriers to big data in the Colombian public sector; gathered from a mix of participant-observation in two IT summits, interviews, and secondary data analysis.
WP62 Graphic v2


As summarised in the figure above, the model showed serious barriers on all seven dimensions:

  • Information: some variety of data but limited volume, velocity and visibility (gap size 7).
  • Technology: good mobile, moderate internet and poor sensor availability with a strong digital divide (gap size 6).
  • Processes: few “information value chain” processes at work to put big data into action (gap size 7).
  • Objectives and values: basic data policies in place but lack of big data culture and drivers (gap size 7).
  • Skills and knowledge: foundational but not specialised big data capabilities (gap size 7).
  • Management systems and structures: general IT systems and structures in place but little specific to big data (gap size 7).
  • Other resources: some budgets earmarked for big data projects (gap size 5).

A simple summary would be that Colombia’s public sector has a number of the foundations or precursors for big data in place, but very few of the specific components that make up a big data ecosystem.  One can turn around each of the gaps to propose actions to overcome barriers: greater use of existing datasets; investments in data-capture technologies; prioritisation of value-generation rather than data-generation processes; etc.

As the working paper notes:

“Beyond the specifics of the particular case, this research provides a proof-of-concept for use of the design-reality gap model in assessing barriers to big data for development. Rephrasing the focus for the exercise, the model could equally be used to measure readiness for big data; BD4D critical success and failure factors; and risks for specific big data initiatives. …

We hope other researchers and consultants will make use of the design-reality gap model for future assessments of big-data-for-development readiness, barriers and risks.”

For those interested in taking forward research and practice in this area, please sign up with the LinkedIn group on “Data-Intensive Development”.

[1] Hilbert, M. (2016) Big data for development, Development Policy Review, 34(1), 135-174

[2] Spratt, S. & Baker, J. (2015) Big Data and International Development: Impacts, Scenarios and Policy Options, Evidence Report no. 163, IDS, University of Sussex, Falmer, UK

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