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. At present, this is more potential than actuality because big data initiatives in developing countries face many barriers.
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.
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”.Follow @CDIManchester
 Hilbert, M. (2016) Big data for development, Development Policy Review, 34(1), 135-174
 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