Indranil Ghosh provides part one of a two-part series on efficacy and impact of ESG investing. This article pertains to the effects of the coronavirus on ESG investing.
Five years ago, many people dismissed environmental, social, and corporate governance (ESG) investing as a fad because it put purpose alongside profit. But today, ESG investing seems to have become mainstream as global flows into sustainable investing are worth upwards of $4 trillion annually. Furthermore, as the Covid-19 crisis mounted in Q1 2020, investors poured $45.6 billion into ESG funds while $384.7 billion flowed out of the overall fund universe.
According to the UN, the funding gap to meet the Sustainable Development Goals (SDGs) is at least $2.5-3 trillion annually in developing countries alone. We think it’s more like $5 trillion globally. Plugging this gap from the public purse would require a 20% increase in the global tax base, which stands at about $25 trillion today. Clearly, this is not feasible. However, steering a small portion of global private wealth, which stands at $200 trillion globally, into sustainable investments could address the world’s development challenges.
Key points in this article include:
- The problems with ESG investing
- The additive impact of ESG investing
- ESG and systems change
Read the full article, Does Covid-19 Mark the End of ESG Investing, or A New Beginning?, on LinkedIn.
Stephen Redwood’s clients have been asking questions about how operating models will change post pandemic and how to accelerate time to market. He collaborated with Colin Taylor, to identify six priorities to focus on when rethinking your go-to-market (GTM) model.
Cross-functional synchronization and alignment around a unified go-to-market approach is uncommon but has great value. Transforming your go-to-market approach can increase brand value, optimize growth investments, empower sales teams and accelerate time-to-revenue. This article discusses six tips to realizing this latent value in your organization:
Information in this article includes:
- Minimize your limiters (decision making and hand-off hold-ups) and maximize your accelerators (streamlined processes, formal collaboration mechanisms, clear accountabilities)
- Build a single company-wide model to establish a trusted and consensus view of all the interlocked go-to-market activities working together
- Clarify accountabilities and devolve decision making closer to hand-offs across the business system
- Build a company-wide, shared sense of accountability into processes and KPIs. Establish cross business communities that bring together critical silos at the intersections of hand-offs
- Adjust goals, provide training, communicate continuously, and keep leaders on point
- Establish oversight mechanisms to ensure the system is continuously updated to keep it relevant
Read the full article, Why is our go-to-market so inefficient and slow?, on LinkedIn.
Tobias Baer provides clear and concise examples of how Google uses the acquisition of select data to create bias, which leads to the dissemination of inaccurate information.
I’m an avid user of the navigation function of Google Maps. Every time I reach my destination, Google asks me for feedback on the navigation instructions. What could possibly be wrong with that? Well, I bet that the data and any analytics derived from that feedback often – and, vastly! – overestimates users’ satisfaction. Why is that?
The app is a perfect illustration of availability bias. I only am given this opportunity to provide feedback when I reach my destination. Which means that if I reach a river only to find that the ferry supposed to take me and my car to the other riverside has stopped operations an hour ago, or if after a few hours of cycling I find that the path indicated by the app leads straight into a gigantic military infrastructure that is fenced by barbed wires with large red signs threatening any trespasser to be shot (both has actually happened to me), and hence my only option is to abolish my route, exit the navigation, and go back to where I come from, no feedback is collected.
Points covered in this article include:
- The problem with creating algorithms quickly
- The lack of sufficient communication
- The challenge of creating objective, systematic assessment procedures
Read the full article, A Little Example How Google Creates Biases, on LinkedIn.
Data scientist and psychologist Tobias Baer (who recently published a book on algorithmic bias) is giving a talk on how to prevent algorithmic bias in the U.K. on Tuesday, 11 February 2020.
Algorithmic bias can affect us everywhere, from minor trivia such as our social media feeds to critical decisions where bias can wreak havoc with a person’s life dream or a company’s survival. Sources of algrorithmic bias are manifold – some, such as biased data and overfitting, sit squarely in the domain of data scientists themselves, while others only can be tackled by the business users and government agencies who use algorithms, be it through carefully crafted experiments that generate truly unbiased data or through deliberate tweaks of the decision-making process.
The discussion will include:
- The psychological and statistical sources of bias
- What business users and data scientists can do respectively to manage and prevent algorithmic bias.
- How regulators should think about algorithmic bias
To learn more about the event, visit: https://www.eventbrite.co.uk/e/how-to-prevent-algorithmic-bias-tickets-86670021367