Why Use Big Data to Measure CSR?

Why Use Big Data to Measure CSR?

Why Use Big Data to Measure CSR?

In the past several posts, we have defined Big Data, shown the problems we hope it will address, and described how CSRHub has implemented a Big Data approach to creating corporate social responsibility (CSR) and sustainability ratings. It is time now to discuss the benefits and drawbacks of the “Big Data” approach.

The assumption is that this approach offers many benefits which are not available under traditional analyst-based ratings methods:

  • A broad measure of perceived performance. Input from most of the “stakeholders” who evaluate a company’s sustainability performance is captured. Investor input from the ESG/SRI sources, community input from NGOs and government groups, and input from suppliers, employees, and customers via supply chain tools, employee surveys, and product ratings are included. While no one can claim to measure true company performance—no external system can do this, it is possible to give an accurate overall multi-stakeholder-based estimate of how a company is perceived.
  • Increased transparency and accountability. The system described automatically reveals to users which sources have reported on each company rated. Via subscriber-accessible tables and custom reports, users can inspect the details of the data gathered. This allows companies and their stakeholders to identify the data elements that affect how they are perceived (transparency) and to respond to or correct data that may not be accurate (accountability).
  • Reduced impact from errors and bias. If a source contains a lot of factual errors or an undisclosed bias, this system automatically reduces the weight given to the source. In this way, the effect on our results of poor quality sources is minimized and corrected for systemic biases. Because sources generate their information independently, there is good statistical accuracy for our aggregated scores.
  • Regular update and trend tracking. Some sources update their information daily, some quarterly, some only once per year. However, because there are so many sources, our ratings are updated each month. This allows the system to show trend charts that connect actions and outcomes with perception.
  • Broad coverage of industries, geographies, and company types. An aggregation system is dependent on its sources for coverage. We do not yet have full data on small companies or on those in remote geographies or unusual industries. But, the system allows us to use whatever is available. We may not be able to rate all aspects of each new company we add to our system, but any ratings we can generate should be consistent across our system.

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