Analytics is one of the current buzzwords that crops up in a wide range of contexts - business analytics, retail analytics, supply chain analytics, human resource analytics, and political analytics, to name but a few of the more frequent ones, and, of course, sports analytics. Analytics is data analysis to support decision making. It is analysis with purpose, motivated not by interesting questions but by practical questions, by questions asked by those in positions of responsibility within organisations who have to make decisions that will affect future performance. It is actionable insight, analysis that aims to make a difference, combining technical rigour with practical relevance. Analytics (or data science) encompasses a wide range of analytical methods. Statistical analysis is at the core of analytics but other non-statistical techniques such as linear programming, cluster analysis and network analysis are all used to support decision making and so should properly be included in analytics. Indeed many would argue that analytics is just a new label for what we previously called operations research and management science. There is some substance to this argument. Ultimately what label we use is unimportant. The crucial point is that analytics (or data science or operations research or management science) is all about harnessing the power of data analysis to improve the effectiveness of decision-making processes. As will be argued in this chapter, analytics represents an evidence-based approach founded on the premise that decisions on the best course of action are more likely to be successful in terms of improved performance when all relevant available information is considered in a systematic manner rather than relying solely on intuitive judgment. Analytics consists of the three Ds — data, domain and decisions. Effective analysts not only need analytical skills but must also understand the objectives of the decision makers and the context within which they are operating. Analytics is not just about applying analytical techniques to a data set. When new problems emerge, they are often ill-understood initially and need to be structured more formally by the analyst to determine what relevant analysis is possible, what data is required, and which analytical techniques are appropriate. These analytical decisions require that the analyst and the decision maker work together. The decision maker will usually have much more knowledge and experience of the specific context and the analyst needs to utilise this expertise. So analytics is an art and a science with the effective analyst combining both technical skills and interpersonal skills. Organisations, even those in the same sector, vary hugely in the extent to which they employ analytics. Davenport and Harris (2007) propose a five-stage characterisation of the analytical capabilities of organisations. Stage 1 organisations are the analytically impaired organisations with negligible analytical capabilities. Stage 2 organisations have localised analytics with analytics used within a small number of departments to improve one or more functional activities. Stage 3 organisations have analytical aspirations in the sense of a strategic commitment to developing a distinctive analytical capability that will act as a source of competitive advantage but are only starting the process of developing the use of analytics across the organisation supported by an integrated database. Stage 4 organisations have built an organisation-wide analytical capability with analytics viewed as an important contributor to organisational success: Stage 4 organisations are differentiated from other organisations operating in the same sector by their extensive use of analytics but analytics is not yet the primary source of competitive advantage. Davenport and Harris reserve the accolade of `analytical competitors` for Stage 5 organisations for which analytics has become the primary driver of organisational performance with an organisation-wide imperative to continually innovate analytically in order to stay ahead of competitors.
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