{"id":12267,"date":"2023-02-03T15:09:31","date_gmt":"2023-02-03T08:09:31","guid":{"rendered":"https:\/\/bestarion.com\/us\/?p=12267"},"modified":"2024-10-06T03:21:13","modified_gmt":"2024-10-05T20:21:13","slug":"how-fashion-can-stop-greenwashing-by-cleaning-dirty-data","status":"publish","type":"post","link":"https:\/\/bestarion.com\/us\/how-fashion-can-stop-greenwashing-by-cleaning-dirty-data\/","title":{"rendered":"How Fashion Can Stop Greenwashing By Cleaning Dirty Data"},"content":{"rendered":"

\"dirty-data-in-fashion\"<\/p>\n

Here are three ways businesses can clean up their dirty data to improve the accuracy of their ESG reporting.<\/span><\/p>\n

Regarding sustainability and corporate social responsibility, the fashion industry is one of the most scrutinized and criticized in the world. Too often, fashion brands attempt to improve their environmental, social, and governance (ESG) reputation through greenwashing, which can take the form of evocative marketing, clever labeling, or even the sly misappropriation of KPIs and overstated sustainability commitments.<\/span><\/p>\n

However, in a global environment where corporate sustainability and responsibility have reached critical mass and legislative focus, consumers will no longer tolerate exaggerated ESG claims. At the legislative level, governments worldwide are not only paying attention but also acting.<\/span><\/p>\n

How can such brands tell a sustainable story that appears to be at least partially true? The answer is straightforward: dirty data.<\/span><\/p>\n

<\/span>Dirty data – a dangerous fashion faux pas.<\/span><\/span><\/h2>\n

Dirty data is defined as incomplete, inaccurate, false, inconsistent, unverified, and difficult to substantiate and verify. But it’s not like most brands deliberately set out to use dirty data to drive greenwashing. Instead, it is frequently the result of poor data management, low-quality data, and a lack of digital maturity, resulting in fragmented, obscured supply chain visibility.<\/span><\/p>\n

One of the reasons greenwashing is so dangerous is that it undermines governance and accountability. <\/span>According to Changing Markets 2021 research<\/span><\/a>, 59% of claims made by European and UK fashion companies were unsubstantiated or misleading.<\/span><\/p>\n

According to some, false claims are a form of fraud that can jeopardize stakeholders, customers, investors, and even the brand itself.<\/span><\/p>\n

<\/span>Cleaner ESG reporting is under pressure.<\/span><\/span><\/h2>\n

As public dissatisfaction with poor ESG practices grows, governments worldwide are taking drastic steps to hold brands accountable. What started as a whisper to improve transparency and demand accountability from some of the world’s most potent brands is now a massive roar of legislative policy with legal ramifications and global implications. Among the notable examples are:<\/span><\/p>\n

In the United States, the Fashion Sustainability & Social Accountability Act of 2022 was introduced in New York State. It would require environmental and social due diligence disclosures if passed.<\/span><\/p>\n

Many countries, including Germany, France, Norway, Australia, and the United Kingdom, have implemented supply chain due diligence.<\/span><\/p>\n

The Corporate Sustainability, Due Diligence Directive, is scheduled to go into effect in the EU by 2025.<\/span><\/p>\n

In November 2022, the EU Parliament approved the Corporate Sustainability Reporting Directive (CSRD), which is set to go into effect in 2024. The CSRD will require granular reporting and play a fiduciary role in the credibility of ESG reporting data.<\/span><\/p>\n

<\/span>How can fashion brands clean up dirty data?<\/span><\/span><\/h2>\n

Here are three common causes of dirty data, as well as our recommendations for eliminating them to ensure the credibility of ESG reporting:<\/span><\/p>\n

    \n
  1. Issue: Data abundance.<\/b> F<\/span>ashion brands generate massive amounts of historical and live data due to their massive social media presence and engagement with millions of followers and influencers. However, much of this data comes from unstructured and semi-structured sources, which raises concerns about credibility. The abundance of data also puts brands in the position of developing numerous contradictory KPIs, resulting in a hazy measurement of their ESG performance.
    \n<\/span>
    \n<\/span>Recommendation: Data governance<\/b>. Data governance ensures that data is in the correct format and protects the quality of data flowing within and outside the organization. Companies must also establish data quality standards that all vendors in the supply chain must adhere to. <\/li>\n
  2. Issue: Large and fragmented supply chains.<\/b> Fashion supply chains are notoriously fragmented, with numerous touchpoints from multiple sourcing materials and locations, multiple manufacturing stages, and a diverse range of products requiring many suppliers and sub-suppliers worldwide.<\/span><\/span> \n

    This fragmentation within the supply chain creates silos and a lack of a common data language. Various organizations use different data collection processes and measurement units, which reduces the data’s credibility.Recommendation: Data standardization<\/b>. Vendors and suppliers use different formats, quality levels, and values to understand data, resulting in different interpretations and misleading KPIs. The most validated way to eliminate such risks is through data standardization. It is a crucial process in data cleansing<\/a> that converts incompatible data formats into an acceptable standardized format.<\/span><\/p>\n

     <\/li>\n

  3. Issue: Outsourcing. <\/b>Most fashion brands do not own their production lines; instead, each garment item or sub-item, such as buttons, lenses, straps, and so on, is outsourced to vendors worldwide. Purchasing intermediaries who close the deal on behalf of the brand are frequently used to select these suppliers. As a result, brands lack visibility into their supply chains, resulting in a lack of real data that is replaced by assumptions and rough estimates.<\/span><\/span> \n

    Recommendation: Automate data collection.<\/b> Data automation is critical for businesses experiencing exponential data growth. The process is divided into three stages:<\/span><\/li>\n<\/ol>\n

      \n
    • Extracting data from spreadsheets.<\/span><\/li>\n
    • Transforming the data to ensure compatibility.<\/span><\/li>\n
    • Loading the data into a central repository.<\/span><\/li>\n<\/ul>\n

      Automated data collection reduces workloads and improves data quality.<\/span><\/p>\n

      <\/span>Data quality\u2014the first step to brand trust<\/span><\/span><\/h2>\n

      The “detergent” in greenwashing is dirty data. Credible and high-quality data is required to protect brand stakeholders and the brand itself, allowing it to meet demand more accurately and better manage its resources, reducing its environmental footprint.<\/span><\/p>\n

      However, simply improving data quality is not a cure-all. Companies must constantly work to monitor the supply chain and create a transparent corporate culture. Regular stakeholder engagement, both within and outside the company, is also essential because it assures everyone that the company’s values, policies, and sustainability strategies are respected and implemented.<\/span><\/p>\n

      As mandates and legislative measures become more stringent, businesses must implement robust reporting systems that allow for continuous control, monitoring, and data intake from disparate sources. This will ensure that they optimize data credibility, meet their sustainability commitments, and protect the brand for the foreseeable future.<\/span><\/p>\n