Leveraging big data for competitive advantage in a media organisation

Abstract

Data sources often emerge with the potential to transform, drive and allow deriving never-envisaged business value. These data sources change the way business enacts and models value generation. As a result, sellers are compelled to capture value by collecting data about business elements that drive change. Some of these elements, such as the customer and products, generate data as part of transactions which necessitates placement of the business element at the centre of the organisation’s data curation journey. This is in order to reveal changes and how these elements affect the business model. Data in business represents information translated into a format convenient for transfer. Data holds the relevant markers needed to measure business elements and provide the relevant metrics to monitor, steer and forecast business to attain enterprise goals. Data forms the building blocks of information within an organisation, allowing for knowledge and facts to be obtained. At its lowest level of abstraction, it provides a platform from which insights and knowledge can be derived as a direct extract for business decision-making as these decisions steer business into profitable situations. Because of this, organisations have had to adapt or change their business models to derive business value for sustainability, profitability and transformation. An organisation’s business model reflects a conceptual representation on how the organisation obtains and delivers value to prospective customers (the service beneficiary). In the process of delivering value to the service beneficiaries, data is generated. Generated data leads to business knowledge which can be leveraged to re-engineer the business model. The business model dictates which information and technology assets are needed for a balanced, profitable and optimised operation. The information assets represent value holding documented facts. Information assets go hand in hand with technology assets. The technology assets within an organisation are the technologies (computers, communications and databases) that support the automation of well-defined tasks as the organisation seeks to remain relevant to its clientele. What has become apparent is the fact that companies find it difficult to leverage the opportunities that data, and for that matter Big Data (BD), offers them. A data curation journey enables a seller to strategise and collect insightful data to influence how business may be conducted in a sustainable and profitable way while positioning the curating firm in a state of ‘information advantage’. While much of the discussion surrounding the concept of BD has focused on programming models (such as Hadoop) and technology innovations usually referred to as disruptive technologies (such as The Internet of Things and Automation of Knowledge Work), the real driver of technology and business is BD economics, which is the combination of open source data management and advanced analytics software coupled with commodity-based, scale-out architectures which are comparatively cheaper than prevalent sustainable technologies known to industry. Hadoop, though hugely misconstrued, is not an integration platform; it is a model the helps determine data value while it brings on-board an optimised way of curating data cheaply as part of the integration architecture. The objectives of the study were to explore how BD can be used to utilise the opportunities it offers the organisation, such as leveraging insights to enable business for transformation. This is accomplished by assessing the level of BD integration with the business model using the BD Business Model Maturation Index. Guidelines with subsequent recommendations are proposed for curation procedures aimed at improving the curation process. A qualitative research methodology was adopted. The research design outlines the research as a single case study; it outlines the philosophy as interpretivist, the approach as data collection through interviews, and the strategy as a review of the method of analysis deployed in the study. Themes that emerged from categorised data indicate the diverging of business elements into primary business elements and secondary supporting business elements. Furthermore, results show that data curation still hinges firmly on traditional data curation processes which diminish the benefits associated with BD curation. Results suggest a guided data curation process optimised by persistence hybridisation as an enabler to gain information advantage. The research also evaluated the level of integration of BD into the case business model to extrapolate results leading to guidelines and recommendations for BD curation.