Abstract:
Abstract.
Big data is data whose size is beyond the ability of commonly used software tools to capture, manage, and process within tolerable time. The concept of big data has been necessitated by the growing capacity of the available information systems to facilitate the capture, processing, storage and use of large volumes of variable but credible data fast enough to generate optimum value for the users.
Land records in Kenya have been over the years managed through paper based
systems which are vulnerable to loss, wear and tear, compromise and poor usability. Consequently, land administration processes became inefficient,
time consuming, unreliable, costly and ineffective. To address these challenges,
the Government of Kenya in 2007 resolved to automate all land records and
transactions by developing and deploying a land information management
system founded on big data technology which is capable of holding vast and
diverse data sets on land ownership and transactions. The decision to automate
land records and transactions was in compliance with the provisions of the
country’s National Land Policy launched in 2009. This study assessed how well
the technology used by the new electronic system fits the needs and contexts of
the users of land records in Kenya.
The study was conducted as an exploratory research based on the fit-viability
theory. Data for the study was collected using interviews with 48 users of the new land information management system. The findings revealed that big data has a high fit and viability for the performance of land records and transactions management in Kenya. In spite of the high fit and viability, it was noted that the viability of the system is hampered by inadequate infrastructure,skills, organisational culture and organisational structure. Addressing these challenges through essential infrastructure development, institutional strengthening and capacity building will enhance the viability of the land records management system. The findings of this study may be used by policy makers in other developing countries to model big data projects. The findings may also be used by the managers of big data projects to enhance their fit and viability so as to yield optimum impact for their stakeholders.
