Page 116 - AC/E's Digital Culture Annual Report 2015

Cultural business models on the Internet116content in a social media post contains a positive opinion, or a negative one.• Social network analysis to visualise and analyse communities and social groups (like Facebook’s “Social Graph”).• Machine learning and Data mining algo- rithms to classify individuals in groups and predict their behaviour, and to discover new patterns in data. This is often done without human intervention, as data models learn from their own performance and improve their accuracy over time.• Data visualisation methods to representdata graphically in order to explore it, orto communicate findings to others (seethe “Visual Complexity”19 website for an excellent collection of enlightening and often beautiful data visualisations.)• Web analytics uses data about visitors toa website to optimise its design and the marketing investment to promote it. One important technique used in web analytics is A/B testing, where visitors are shown different versions of a website in order to determine which of those versions works better.Data mining includes a series of statistic and analytical methods that make it possible to classify, detect patterns and predict behaviour.There has been an explosion in the areas for data application, that is, the domains where datainsights can be applied to create value inside an organisation (or societally). These include:a) Measurement and optimisation: Here, data insights are used to increase the efficiency of processes, evaluate the return on investment of different activities (e.g. spending in different marketing channels) and allocate resources to the areas with the greatest impact.b) Segmentation and prediction: Data can be used to classify users into different groups ina way that allows more personalised targetingof content, marketing messages, promotional offers and so forth. Patterns in historical data can also be used to predict future outcomes. For example, there are regularities in the behaviours of people who commit credit card or insurance fraud that can help to predict such instances in the future.c) Discovery: Data can be mapped and mined to identify new market opportunities. This can be done for cross selling, to find gaps in the market (e.g. regions where people are not engaging with a brand), and to identify new consumer trends that might require a change in business strategy.Many of these areas of application are not new: retailers, financial service companies, insurers and many others have used data to make strategic and operational decisions for decades and even centuries20. What is the novelty in all of this?What is new is that greater access to data,and innovations in ‘big data’ technology, are increasing the velocity and precision with which data can be used to optimise, segment andUsing data to create value in the arts and cultural sector