Big data management at the Kenya electricity transmission company (Ketraco)

Abstract

Big data has revolutionized how organizations around the world perceive and harness information, offering unprecedented opportunities to derive valuable insights and drive innovation from varied and vast datasets. Big Data can be defined as the information or data asset characterized by a high volume, variety, velocity and veracity that require specific analytical methods and technologies for its transformation into meaningful value. However, the ability to manage and originate meaningful insights from these datasets to make data-driven decisions aimed at improving organizational performance and growth remains a key challenge for many organizations. The purpose of this study was to analyse Big Data Management at the Kenya Electricity Transmission Company (KETRACO) with a view of proposing strategies for improving its management and use. The objectives of the study were to; Analyse the current state of Big Data Management at KETRACO; Examine the existing infrastructure for Big Data Management at KETRACO; establish the influence of collaborative partnerships on Big Data Management and propose a strategy for the improvement of Big Data Management at KETRACO. The study was underpinned by the Data Centre Curation Lifecycle Model, and the Community Capability Model. The study employed concurrent mixed methods approach employing survey design within a singular exploratory case study. The target population of 563 was stratified into different stratums and different sampling strategies was used on each stratum. Census sampling technique was applied to select 14 key informants comprising of 2 personnel from the Executive office of the CEO, 7 senior management team, and 5 system analysts with whom interviews were conducted to collect qualitative data. The MaCorr Research solution calculator was applied to the remaining target population of 549 to attain a sample size of 63 respondents from 8 departments. Thereafter, simple random sampling was used to select respondents from each of the 8 departments from which quantitative data was collected using questionnaires. The quantitative data was analysed using descriptive statistics and presented in tables, graphs and pie charts while the qualitative data was analysed thematically and presented in narrative descriptions. The findings revealed that there were no standardized and unified procedures for big data management at KETRACO and that there were no specific policies and guidelines with tight regulations that streamlined big data management and usage at every level of data life cycle; further, the findings showed that KETRACO had inadequate and equally under utilized the existing ICT infrastructure to effectively support data management functions. A great concern noted from the survey was the existence of knowledge and skill gaps such as the metadata skills gaps as exhibited by 51 (87.93%) of the surveyed respondents. The survey also established that Ketraco had not established unified mechanisms for managing its data from creation or receipt throughout its life cycle as this was unanimously confirmed by all the 58 (100%) respondents. Further, the survey established that privacy, confidentiality, and complexity in handling cumulative data assets were the major hindrances to Big data management at Ketraco as this was recorded by 54 (93.10%) and 47 (81.03%) respondents respectively. Finally, the study revealed inadequate collaborative mechanisms for effective data management with other partners. In conclusion, KETRACO had not adopted effective policies and strategies for Big data management, had under-utilized its ICT infrastructure, and not put enough mechanisms for collaborative partnerships, hence hindering its capability of deriving maximum value from its data assets. The study recommended among other solutions, the establishment and operationalization of policies and procedural frameworks for managing data throughout its lifecycle, capacity building and data literacy training programme to bridge the skill gap; development of a sustainable ICT infrastructure and security approach in support of big data; and building collaborative partnership to develop big data capabilities.