Skip to main content Skip to footer
Cognizant Blog

Here are three ways business can improve the accuracy of their ESG reporting by cleaning up their data.

The fashion industry is one of the most scrutinized and criticized industries in the world when it comes to sustainability and corporate social responsibility. Too often, fashion brands try to improve their environmental, social and governance (ESG) reputation with greenwashing, in the form of evocative marketing, clever labeling or even the sly misuse of KPIs and overpitched sustainability commitments.

But in a global landscape where corporate sustainability and responsibility has reached a point of criticality and legislative focus, consumers will no longer tolerate overblown ESG claims. And at the legislative level, governments around the world are not only taking notice—they are acting, as well.

The question is, how are such brands able to tell a sustainability story that seems at least partially based in fact? The answer is quite simple: dirty data.

Dirty data—a dangerous fashion faux pas

Dirty data refers to incomplete, inaccurate, false, inconsistent and unverified data that lacks credibility and validity and is difficult to substantiate and verify. But it’s not as though most brands deliberately set out to use dirty data to intentionally drive greenwashing. Instead, it’s often an outcome of poor data management, low-quality data and poor digital maturity, which results in fragmented, obscured supply chain visibility.

And this is one of the reasons greenwashing is so dangerous—it comes at the expense of governance and accountability. In fact, research by Changing Markets in 2021 showed that 59% of claims by European and UK fashion companies were unsubstantiated or misleading.

Some compare the false claims to a form of fraud that can jeopardize stakeholders, customers, investors—and even the brand itself.

The pressure is on for cleaner ESG reporting

Amid growing discontent with poor ESG practices, governments around the world are putting in place drastic measures to hold brands accountable. What began as a whisper to improve transparency and demand accountability from some of the world’s leading brands is growing into a massive roar of legislative policy with legal ramifications and global implications. Some notable examples include:

  • In the US, the Fashion Sustainability & Social Accountability Act was introduced in New York State in 2022. If passed, it would require environmental and social due diligence disclosures.
  • Supply chain due diligence is now in effect in many countries, such as Germany, France, Norway, Australia and the UK.
  • The Corporate Sustainability Due Diligence Directive is set for implementation in the EU by 2025.

More recently in November of 2022, the EU Parliament gave the final green light to the Corporate Sustainability Reporting Directive (CSRD), which is expected to become effective in 2024. The CSRD will play a fiduciary role in the credibility of ESG reporting data and will require granular reporting.

How fashion brands can clean up dirty data

Here are three common causes of dirty data and our recommendations for eradicating it to ensure credibility in ESG reporting:

  1. Issue: Data abundance. With their outsized presence on social media, and engagement with millions of followers and influencers, fashion brands generate massive amounts of historical and live data. However, much of this data comes from unstructured and semi-structured sources, which creates credibility issues. The data abundance also puts brands in the position of developing numerous and conflicting KPIs that generate a vague measurement of their ESG performance.

    Recommendation: Data governance. Data governance ensures data is in the right format and safeguards the quality of data flowing inside and outside the company. Companies also need to establish data quality standards that can be followed by all vendors in the supply chain.

  2. Issue: Large and fragmented supply chains. Fashion industry supply chains are notoriously fragmented, with numerous touchpoints from multiple sourcing materials and locations, multiple manufacturing stages and a wide range of products whose production requires many suppliers and sub-suppliers across the world.

    This fragmentation creates silos and a lack of a common data language within the supply chain. Various organizations follow different data collection processes and different measurement units that undoubtedly diminish the credibility of the data.

    Recommendation: Data standardization. Vendors and suppliers use different formats, quality levels and values in understanding data, which creates different interpretations and triggers misleading KPIs. The data standardization process is the most validated way to eliminate such risks. It is a critical process in data cleansing that converts inconsistent data formats into a common acceptable format.

  3. Issue: Outsourcing. Most fashion brands do not own their production lines; instead, each garment item or sub-item, such as buttons, lenses, straps etc., is outsourced to external vendors across the globe. Many times, the selection of these suppliers is done through purchasing intermediaries that close the deal on behalf of the brand. This means that brands lack visibility into their supply chains, resulting in an absence of real data, which is replaced by assumptions and rough estimates.

    Recommendation: Automate data collection. Data automation is crucial for companies with exponential data growth. The process involves three main stages: extract data from spreadsheets, transform the data to ensure compatibility and load it into a central repository. Automated data collection substantially decreases workloads and improves data quality.
Data quality—the first step to brand trust

Dirty data is the “detergent” in greenwashing. Credible and high-quality data is required to protect not only brand stakeholders but also the brand itself to accurately meet demand and better manage its resources, thereby improving its environmental footprint.

Yet, improving only data quality is not a panacea. Companies must continuously work to monitor the supply chain and build a transparent corporate culture. Regular stakeholder engagement within and outside the company is also critical as it reassures everyone that the company’s values, policies and sustainability strategies are respected and implemented.

As mandates and legislative measures continue to grow, businesses will need to adopt robust reporting systems that enable continuous control, monitoring and intake of data from disparate sources. Doing so will ensure they optimize data credibility, deliver on their sustainability commitments and protect the brand far into the future.


Cognizant UK & Ireland
Author Image

Our experts are contributing exciting insights about what's going on within technology and innovation.



In focus

Latest blog posts

More blog posts