Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Companies and brands across the globe are taking a holistic approach to sustainability and adopting the ESG framework which addresses the management of risks and opportunities related to environment, social, and governance.
While ESG goals and targets are well understood within the organization, by its employees, regulators, partners, and suppliers it’s the “how” and “what” that must be clearly defined, collected, and analyzed. As a result, consensus on what data must be collected and how these should be disclosed and analyzed are still unclear.
As Databrick’s Junta Nakadai has clearly pointed out, for ESG to really take off and make global impact, data and AI must play a central role in collecting, verifying, and analyzing performance. This can only be done effectively today by leveraging technology.
Datasets tracking ESG factors often leave much to be desired. Investors continue to struggle with questions about data collection and interpretation. The absence of standardized ESG datasets and reporting methodologies makes it difficult for issuers to disclose meaningful information on sustainability. Data may not be widely available or manually collected by analysts, leaving data providers little choice but to produce subjective qualitative assessments. Such an approach means that different research companies and data providers use their own, often inconsistent methodologies to generate ESG scores.
Better data and better data tools could resolve many of these issues. Sophisticated players are learning how to use artificial intelligence (AI) techniques like machine learning, deep learning and neural networks to significantly improve the quality and amount of usable data, and to analyze it more effectively.
Today, finding ESG data even internally is a highly manual data-collection process. Leading companies disclose a range of ESG-related data from water consumption, and carbon emissions to workforce demographics. Each data point is likely kept in separate databases in different formats and schemas, making it difficult to ensure the data is high-quality or accurate. Centralizing the data in a single data lake can alleviate data access and quality.
Once information is centralized in modern cloud-based storage architecture, companies can get a real-time view and understanding of their own ESG performance. This enables self-correction and benchmarking, thus improving compliance with their stated goals. In addition, having the data accessible means metrics can be disclosed more frequently.
For most large companies today, ESG verification simply means asking partners to abide by the vendor code of conduct. But how do you verify it? AI can play a central role in the verification process by using techniques from natural language processing (programmatically extracting information from text) to graph analytics (learning how different entities influence each other’s ESG).
Data and AI can enforce ESG factors, automating the data-collection process as well as leveraging AI to analyze ESG enables
– Regular Reporting
– Verification of dislosures
– Hold companies accountable
To be effective and ultimately deliver real-world difference, ESG must be tied to business performance and integrated into the core business strategy and governance processes. Let AI Consulting Group help you create metrics and measures including carbon emission to effectively monitor your program.