The field of Knowledge Management (KM) calls for organisations to work smarter to gain efficiency savings, competitive advantage and long-term sustainability. KM literature states that in the current and emerging knowledge era businesses cannot rely on events from the past to ensure future success, therefore many organisations struggle to predict strategic direction, resulting in ‘guestimations’ of what the future may hold. However, the field of data analytics would question this statement. By understanding historical perspectives through extensive data analytics, organisations are equipped with patterns and trends on which to base forthcoming decisions. Armed with a wealth of past, present and emerging data key decision makers can shape future direction, influencing market penetration and customer relations. Employing data analytics can lead to upselling, cross-selling and market diversification.
This project will investigate predictive data analytics in a variety of sectors to ascertain best fit algorithms for business intelligence (BI). While a ‘one size fits all’ approach is difficult to obtain in the business world due to the discrete and heterogeneous nature of organisations, the creation of a BI toolkit is needed to assist public, private and third sector organisations in preparing for the future. The concept of ‘big data’, whilst being a branded concept, is not yet understood within many organisations and companies are struggling to implement knowledge management as they lack the data and information tools to do so. While some are capturing core data, others are not yet on the starting block. For those that have commenced data collection, most are unsure what to do with it, how to identify knowledge nuggets and how to base decisions on what they find. This project will contribute to literature, practice and impact in the BI field
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
Vice Chancellors Research Scholarships (VCRS)
The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
Monday 19 February 2018
12 March 2018
When applying for this PhD opportunity please quote reference number: