Putting Data At The Heart Of Business Responsibility

Putting Data At The Heart Of Business Responsibility

This is a spoiler alert for those who create beautiful looking sustainability reports. It’s about time that we evolve from pdf based sustainability reports to a sustainability reporting system based on data science.

Financial data vs. Sustainability data

Do you know how the data system for financial disclosure works? Regulators have put well governed systems in place to provide timely data for users to take informed decisions. For example, a potential investor, customer, or employee can access financial data of a publicly listed company and assess its financial performance.

Similarly, a company’s sustainability data can help the community, investors, customers, workers, buyers and media assess its performance on sustainability. But unlike financial data, sustainability data is neither standardised nor easily accessible. However, this can be transformed.

Taking sustainability data seriously

Curious how this transformation would look like? Imagine you go to the supermarket and you’re able to scan a product label to see whether a brand pays fair wages to its suppliers. Imagine a local community that learns about a company’s policies and disclosures before it takes a decision to give its land to build a factory. Think of NGOs and policymakers who can analyse and monitor the actions of the corporate sector on the basis of sustainability data.

Soon enough a business’s sustainability data will be as important as its financial data. It will be easily available in standard comparable formats, just like financial data for publicly listed companies.

Open access to standardised & reliable data

Many Indian companies now publish their sustainability reports on their websites. The idea is to provide free and accessible CSR information for all.

The top 100 listed companies in India have to provide the Business Responsibility Reports (BRRs), as mandated by SEBI. Some companies use other reporting formats such as Sustainability Reporting Standards developed by Global Reporting Initiative (GRI) to communicate their impact. 58 organisations used the GRI format in 2016 to highlight their sustainability efforts, shows the GRI database.

Currently there is no way to compare company-wise sustainability data across varying formats. We need to develop a mechanism to compare business sustainability data. There is also some skepticism about how reliable this data is. There has to be a way to ensure that all sustainability data is authentic.

Comparability of data

Look at how different analysts add their unique value to financial data, which is pretty standard across the world. The same can be done with sustainability data too. A common approach to categorise sustainability data will make it easier to analyse and predict trends. Instead of collecting new data, we need to add and find value in existing data.

It is hard to compare data unless it is aligned in a standard format. Imagine the questionnaire fatigue it will cause among sustainability officers.

Such data is useless if it cannot be found easily. Common categorisation of data will not only make it comprehensive but also easy to understand. It’s time now to move over from pretty data to machine readable data.

Making sense of big data

Getting access to a steady amount of qualitative comparable data is only one aspect of sustainability data. You can’t make sense of any data until you’re able to see it.

“Data visualization plays a critical role in the ‘Human Computer Interface’ layer that allows us really ‘see’ our data,” says Ritvvij Parrikh, founder of Pykih. Pykih builds data-driven tec
hnologies and products that allow business stakeholders to understand the insights hidden in their data more easily. Ritvvij sees visualization driven analytics to be a key game changer to empower the average analyst to ask ad-hoc ‘what-if’ style questions without the need to understand the ‘science’ behind it.

Our eyes are high-bandwidth sensors. Data visualization uses our hardwired ability to recognise patterns and perceived movement, allowing us to naturally detect irregularities, predict trends and identify patterns. This is extremely beneficial when you’re trying to make sense of large chunks of data.

In India, we’re at an early stage of using a data driven approach towards business responsibility. Despite that, Ritvvij believes that data will revolutionise how we look at business responsibility in the near future.

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