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Vision & objectives

Our vision is to become a global centre of excellence for digital analytics research, particularly applied to business and sustainability problems.

Our key objectives are to:

  • Produce and disseminate world-leading research in digital analytics.
  • Develop and apply state-of-the art analytics tools and techniques in practice.
  • Develop a multidisciplinary approach to digital analytics research.

CODA, the Consumer and Organisational Digital Analytics research centre, officially launched at King’s Business School in May 2018. CODA is a future-facing research centre that focuses on predicting, understanding and seeking solutions to tomorrow’s business, management and sustainability problems using advanced methods, including machine learning and big data. CODA aligns with some of the distinctive characteristics of King’s Business School, including advancing our understanding of how work and consumption is being disrupted by new technologies; and bringing fresh perspectives to our understanding of how global business operates and needs to be rebalanced to ensure innovation, sustainability and high productivity.

“Coda” literally means tail – from the Latin cauda. The term “long tail” is sometimes used to refer to understanding complex, subtle niches in data sets, rather than only mainstream trends. CODA aims to find hidden patterns, often those that are hidden in the long tail and not immediately obvious. The substantial nature of the issues investigated necessitates multi- and interdisciplinary collaboration, building on diverse foundations in areas such as marketing and other areas of business, statistics, economics, development studies, law, environmental science, information systems, and computing. We aim to work with industry decision-makers and policymakers to develop relevant collaborative research projects with live data, disseminate practical knowledge and build impact from our research.

We’re agile and quick to adopt new ways of working. Some of our current methods include machine learning, econometrics, Bayesian statistics, lab and field experiments, direct market observation (e.g., automatic online data collection), surveys and simulation.