Big data created over 6 million jobs – The Anatomy of Impact with AI, data & analytics

Published On March 19, 2021
In ESG, Blog Archives

Corporations today are on a constant hunt to outdo their competitors and stay dominant within their respective industries. This process is aided by consistent technological developments and breakthroughs in terms of business development tools. The focus has shifted from conducting business activities to generate revenue and stronger profit margins. Businesses are aware of responsibilities and stakeholders that affect external factors such as the impact on the environment and compliance with government regulations. As a result, ESG or Environmental, Social and Governance investment models are gaining momentum. 

ESG models are frequently coupled with clear, concise supplementary data to make the best possible business decisions. Artificial intelligence or AI has taken root in a growing number of businesses. Corporate investment into AI is said to reach USD $266.92 billion (Oberlo) by 2027. Artificial intelligence has been a firm method to facilitate internal optimization and data gathering at both micro and macro levels. The two business strategies work to ensure successful overall operational efficiency and effectiveness. 

What is Data ?

Data is a broad term used to describe the information available to be meaningfully processed by an organization for enhanced awareness. In terms of developing a meaningful CSR (Corporate Social Responsibility) plan, businesses are able to detect mutually beneficial endeavors by assessing the information surrounding the opportunity and the organization’s ability to implement.  

ESG data

Within the ESG field, there are a number of organizations that provide ESG data services centering around investors, corporations and the specific concerns they may have or face. As the field itself does not offer too many firm metrics to assess the feasibility of the application, relying on these services providers offer professional insight into an abstract situation.  

What is Data ?

ESG data service providers use three key assessment criteria to gather all required information and streamline their decisions; 

Variety 

ESG data does not appear in one consistent format. ESG related service providers are often charged with collecting useful information from hundreds of data pools gathered from both internal and external sources. Being able to explore a number of avenues for the best possible result is critical to ensuring data relayed is as comprehensive as possible. 

Velocity 

ESG data, much like other business pertinent information, is constantly changing. To ensure the information is meaningful, data gathering and processing must account for factors such as evolving industry trends, negative traction based on product recalls or poor social media depiction and even demand/supply fluctuations. Staying on top of the latest information helps businesses make better decisions faster. 

Volume  

When data appears from multiple sources, the pools of information reach far and wide. Relevant data is gathered from a variety of sources on a larger scale, such as social media, the news and industry-relevant events. Data is also gathered from internal sources such as data transfers between IOT devices and customer micro-movements. This ventures into Big Data management, a truly large scale operation that requires strong guidance and professional understanding.  

Data procurement is critical to ensuring the successful implementation of ESG guidelines and general awareness of business impact. Big data has created over 6 million jobs (Hosting Tribunal) across the globe (over the last nine years). The professional availability is at any organization’s disposal for the best and most comprehensive decision-making process.  

What is AI ?

AI or Artificial Intelligence is a broad term used to describe machines or technology that mimics human intelligence in order to complete tasks. AI has a number of different application areas and can take the form of ML (machine learning) or Automation at a lower level and intricate problem solving or large scale information processing at a higher level.  

Artificial intelligence has quickly become a widely adopted technology across a growing number of business sectors. 50% (Mckinsey & Company) of all survey respondents to the McKinsey Global Survey on Artificial Intelligence have incorporated AI into at least one business function. The flexibility and optimization offered by the diverse use of AI applications have seeped into ESG investing.  

Technological developments in AI have allowed businesses to understand and analyze previously unusable information n leveraging AI, machine learning and automation offerings. AI capabilities today can offer in-depth insight into ESG investing by allowing businesses to obtain a 360-degree view of investor/stakeholder interest, the feasibility of adopting new internal practices, risk analysis and how to implement sustainability in the long run. AI technology offers the opportunity to reduce concerns brought about by processing large data pools. 

What is AI ?

Additionally, AI offers information on perception and impressions made by the organization. Sentiment Analysis Algorithms can be employed by artificial intelligence to understand the context and discern tone. Businesses can read through copious amounts of information and deduce whether a majority of the information shared about operations and efforts are brought to light in either positive or negative undertones. For example, when CEOs share information, using natural language processing is an effective tool to perceive dedication towards the cause, which in this case would be ESG consciousness.  

AI in ESG investing

It is important to keep in mind the drawbacks to ESG investing that improper AI use can bring about. When AI systems are implemented, chances are they stay online almost twenty-four hours a day. The environmental impact of this can be profoundly negative in the long run. Additionally, if the AI is programmed with any degree of bias, the results found or data mined are likely to follow suit. The ethics behind AI are lax and unmonitored; therefore it is in a business’s best interest to ensure their AI systems are maintained with clarity and relevance in order to prevent the development of existing social issues or concerns for the organization.  

What are Analytics ? 

Analytics covers the conversion of large scale information into data relevant to the business. Data mining and artificial intelligence working together allow a strong streamlining method to ensure businesses are only offered information that is relevant and critical to the decision making process. When trying to implement stronger ESG investment protocols, it becomes easy to get overwhelmed by the lack of structure in achieving the same but the growing information pools that should affect decision making. Introducing a strong team of professionals or outsourcing to a knowledgeable organization creates better funneling processes and, as a result, better decision making.  

Big data created over 6 million jobs – The Anatomy of Impact with AI, data & analytics

With the developed interest in ESG scores and better all-round impact on society, weighted metrics are slowly being introduced into the field. The first being the identification of influencing factors. Using this technique allows businesses to understand the degree of influence stakeholders hold within the decision making process and where relationships can be enhanced for a strong ESG score. The second is introducing word cloud visualizations to create a more dynamic understanding of internal and external activities highlighting areas of interest and development for all stakeholders. The connections a business thrives off of can have a strongly negative impact on ESG goals without a business recognising the same. This is where the word cloud visualizations shine as a solution. Finally, using the ESG and Market Risk correlation is critical to understanding how a company is perceived when the right ESG protocols are implemented and work successfully. Usually depicted as a graph charting performance over time, this information helps businesses understand perception and identify industry trends.  

Incorporating data analytics and metrics pushes businesses to understand what ESG means at its core and how far the business itself is willing to add, remove, modify and replace components within existing activities to propel optimization and efficiency.  

Conclusion 

The development of technology has allowed businesses to take charge of the decision-making process and make educated, comprehensive choices that propel positive outcomes. Technology is not only enabling corporates but also the consumer, the environment, society and the government to understand long term impact and true value addition. When an organization takes the time and resources to invest in better ESG practices, it communicates with all stakeholders the importance of their opinion and contribution to the business cycle.  

The end result? Stronger relationships with the community and all stakeholders. Should a time arise where the business is in dire need of assistance or information, higher ESG scores create trust and a better likelihood of gaining what they require to move forward in comparison to competitors who do not pay ESG the same attention.  

Understanding a business’s ESG goals is critical to incorporating better practices. Employing a professional, either in a house or outsourced, is heavily recommended to achieve higher ESG scores.  

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