Exploring the Application of Analytics to Environmental Sustainability

In the age of big data, organisations are employing analytics to gain insights about their business activities and inform the decision-making process. Business and sustainability analytics help answer questions, solve problems, and predict outcomes that leverage competitive advantage. Some companies apply analytics tools and techniques to gather and report environmental-related data, such as resource consumption, greenhouse gas emissions and waste generation. Although the notion of Sustainability Analytics has received the attention of some practitioners, it remains an under-researched area. The analytics capabilities to collect and analyse environmental data can help organisations gain insights into their impact on the environment and make timely and fact-based decisions; however, these need to be better understood. Consequently, this research develops a theoretical model through selective coding that outlines the main capabilities that organisations employ to understand and manage their interactions with the natural environment and the challenges that need to be addressed for the successful application of Sustainability Analytics. To achieve this, the researcher conducted a comprehensive literature search using the systematic literature review method for studies published during the past two decades in either high-quality peer-reviewed journal articles or Ranked A* and A conference papers. The SLR helped determine the direction of the research on analytics. Qualitative data was obtained through twenty in-depth interviews with environmental analytics practitioners. The transcripts were inductively analysed using grounded theory methodology. The open coding process produced a large number of concepts coded in vivo, which were refined and reduced to 63 dimensions. These dimensions were then analysed, and relations among the concepts were established, producing 22 broad concepts named attributes. The attributes were then examined and grouped under six main categories: Planning, Knowledge and Awareness, Data Traits, Data Processing, Skills, and Reporting. The relationship among these categories was examined in the light of grounded theory and through axial coding, resulting in three key sustainability capabilities: Business Capability, Data Capability, and People Capability, and the associated external and internal challenges for each. The challenges included Blindness, Low-Quality Data, Comprehension Obstacles, No Knowledgeable Professionals, and Socio-Environmental Commitment. It has also been determined that these three capabilities are interlinked. For Sustainability Analytics, Business Capability requires knowledge to be developed and helps develop attitudes within People Capability. In return, People Capability enables Business Capability. Additionally, Business Capability affects Data Capability. Furthermore, Data Capability requires technical knowledge and skills from People Capability to address low-quality data and comprehension obstacles. Overall, the developed theoretical model has implications for both scholars of grounded theory and sustainability staff aiming to adopt and implement sustainability analytics within organisations.