Leveraging Big Data Analytics For ESG Performance Measurement and Reporting

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
School of Business | Bachelor's thesis
Electronic archive copy is available locally at the Harald Herlin Learning Centre. The staff of Aalto University has access to the electronic bachelor's theses by logging into Aaltodoc with their personal Aalto user ID. Read more about the availability of the bachelor's theses.
Date
2023
Major/Subject
Mcode
Degree programme
Tieto- ja palvelujohtaminen
Language
en
Pages
28
Series
Abstract
As the demand for more sustainable business practices increases and stakeholder expectations evolve, organizations must be capable of measuring and reporting their environmental, social, and governance (ESG) performance. Despite these requirements, only a limited number of organizations can meet them. To harness the potential of their ESG data and effectively measure and report their performance, organizations can leverage tools such as big data analytics. Big Data consists of datasets in diverse formats and at large volumes. The prospects for utilizing big data analytics in the area of ESG are potential due to their shared characteristics. In this thesis, the characteristics of ESG data are examined through the theory of the 5Vs in big data: volume, variety, velocity, veracity, and value. This research offers insights into the existing knowledge gap in ESG data performance measurement through the application of big data analytics tools and methodologies. In addition to the conventional analytical methods discussed in the literature review, certain technologies like artificial intelligence, machine learning, and natural language processing are explored. The literature review and case examples provide evidence that big data analytics present solutions for addressing challenges associated with ESG data that organizations have encountered, for example, difficulties in handling qualitative data. Big data technologies possess capabilities such as the ability to process extensive datasets collectively and visualize the information, which proves to be particularly useful in the context of ESG measurement. Significant challenges are encountered, particularly in the absence of universally recognized standards to guide organizations in measuring ESG performance, or in focusing solely on the most relevant and valuable data. Moreover, optimal ESG data performance measurement occurs when tools are seamlessly integrated into organizational systems and cross-disciplinary teams are working with them.
Description
Thesis advisor
Bragge, Johanna
Keywords
big data analytics, ESG, ESG reporting, artificial intelligence, machine learning
Other note
Citation