Benefit with intent is a modern lodestar for business. However a sound data-driven strategy is required to convert positive intentions into scalable solutions that are tightly integrated with industry.
Sanjay Podder and Shalabh Kumar Singh
Trying to balance shareholder interests with staff, partners, communities and social interests is also a top priority for companies. With businesses adopting new workplace policies to protect workers and repurposing supply chains and company processes to help communities navigate the crisis, the COVID-19 pandemic has only intensified the focus on environmental, social and governance (ESG) goals.
Although some brands, such as Unilever’s Sustainable Living Brands, have managed to boost market success and create customer satisfaction and trust by solving social issues, most others are failing to do so. This can be due to reasons ranging from regulatory barriers to hierarchical organisational structures. However, our analysis reveals that the primary hurdle is one that most businesses appear to overlook-data or more specifically, lack of data.
Companies have years of expertise in data gathering and processing for commercial purposes. However they also lack a consistent data-driven approach to collect and convert insights into results when it comes to topics such as climate change, gender equality, public health, poverty, human rights, the future of jobs and responsible creativity.
Businesses can truly understand and resolve these problems only if they have the right data and know how to do it. And not getting the right details can be very confusing. For example, the data may indicate high unemployment in one area and opportunities but lack of skills in another. But once you know that people without work are not able to relocate, you are likely to draw the wrong conclusions, as Nobel laureates Esther Duflo and Abhijit Banerjee have shown for the US.
Without data, companies are struggling to effectively advocate for policy and regulatory support. And finally, they cannot measure progress on their ESG targets, which ensures that they cannot produce compelling outcomes to stakeholders, including shareholders, potential investors, consumers and staff.
Below are four main concepts for creating comprehensive data for a good strategy that will help companies achieve their ESG goals.
Outline your goal of kickstarting your data-discovery process
As a first step, businesses need to explicitly define the results they are trying to achieve, their success metrics, and then work backwards to map how they can meet the targets. It would help us get started with the data-discovery process – to determine the type of data they need and where it can be retrieved. Consider Akshaya Patra, which expressed its goal of being able to feed five million children through their mid-day meal programme by 2025. To accomplish this goal, a data collection process was initiated, which involved gathering a series of questions and obtaining input from students, school leaders and school kitchen workers. The observations derived from this data in combination with advanced technology have helped Akshaya Patra initiate a pilot programme whose improved efficiency could potentially translate into nearly two million additional meals each year based on the scale of the kitchen.
Tap the data to create a coalition for change
The overcoming of social problems cannot be achieved by any single organisation on its own it requires the support of the ecosystem-governments, non-profits, start-ups, academics and even competitors. In order to build an ecosystem, businesses must first define their own core competencies that can be used to meet their social objectives and then examine how that expertise, in tandem with the larger ecosystem, can produce the desired outcomes. Data may help to identify interdependencies-both synergies and possible bottlenecks-and help us to take an inside or outside approach to social innovation.
Mastercard’s Center for Inclusive Development for example, aims to advance equitable economic growth and financial inclusion around the world. According to our study, its core competencies include large-scale, individualised expense results, the analytical capabilities of its staff, and MasterCard’s big investment in emerging technology for financial inclusion. In combination with other institutions with common social agendas, they enable the Center to have a larger influence, allowing both a “inside-out” and a “outside-in” approach to economic and financial inclusion.
Building a data-driven organisational culture
Although partnerships will help businesses solve some of their limitations, they would also need to develop new data science skills – including understanding not just the evolving spatial and temporal aspects of data to create dynamic solutions, but also how to combine different types of data—voice, vision, emotion—for greater insight.
Organizations will need to cultivate expertise to translate insights into results. Companies need to develop a corporate and compensation structure that holds project management accountable for achieving the ESG objectives. Only then will they begin to evaluate the possible effect of each initiative on the ESG priorities of the organisation and start thinking more critically about how to track relevant data and measure change.
Smart businesses also realise that a big part of doing good is showing what they have done. They need to leverage their data to effectively communicate progress on their ESG outcomes and impact to their stakeholders.
Taking an Ethics-First Data Strategy
Organizations need to take a value-driven approach to evaluate how data is deployed in the social context. Data should be used to incorporate, not exclude, protect and not exploit people. As technology progresses, businesses need to put in place sufficient safeguards-such as developing prudent technologies that protects against the potential of algorithms to amplify pre-existing, frequently unconscious biases.
In addition, businesses must also mitigate the dangers of exchanging data within their environment for the benefit of society. Data is also not collected for a social reason and the exchange or access to data collected in another context may give rise to the danger of a business working under regulations, such as the General Data Protection Regulation (GDPR) of the European Union. Various approaches can help businesses to mitigate data-related threats and create trust. For example, they might work together and create a data trust, which is a non-profit entity that has the right to host data and the responsibility to protect the data and the needs of the individuals whose data it is. They should also adopt techniques for protecting privacy, such as differential privacy, which helps organisations to analyse data together without revealing all facets of data. Safe Multi-Party Computing (MPC) protocols may keep all inputs of the parties private, except for what is deliberately disclosed by the intended results of the computation.
In short, almost every organisation is now a data company. By developing a sound data plan, businesses will begin to overcome obstacles to the blending between market value and social value.
