There’s a saying in the data world: garbage in, garbage out.
What it means is that if you have bad data quality, you are going to get bad results from any data initiative you try to put in place. It doesn’t matter how expensive, all-singing, and all-dancing the technology is, or how clear your goal is: if you’re fuelling it with poor-quality data you will not get the results you want.
In fact, bad data can do more harm than no data at all, feeding you inaccurate information that distorts your insights, influences poor decision making and skews reporting. It’s always frustrating, sometimes risky, and occasionally down-right dangerous – resulting in catastrophic commercial decision-making and failure to comply with regulations.
The role of data quality in ESG
You won’t be surprised, then, to hear that when it comes to ESG, data quality is critical – as we explain in our guide, ESG Unchained: a guide to finding your ESG hotspots. ESG is about examining your business’ environmental, social, and governance impact, using data to report on ESG issues and pinpoint areas that need to be improved – carbon emissions, for example – and to highlight areas of positive impact and improvements that are taking place, such as tangible CSR initiatives.
If that data isn’t high quality, how are you going to know that the picture it paints of your business is accurate, and not missing crucial details or portraying a false ‘image’ of your business’ ESG status?
Put it this way: that data picture could present an environmentally conscious organization with great ESG credentials, while just out of sight, one of your products or services is generating huge amounts of pollution or waste. Thanks to missing or inaccurate data, it’s invisible to you – but it may not stay invisible forever.
Over the last ten years, many high-profile businesses have fallen foul of ESG-related issues, thanks in part to either a lack of data or poor-quality data. From the use of child labor in factories and unsafe work practices to unclear provenance of raw materials (like the horse meat scandal), missing or inaccurate data has created a headache for organizations looking to address their ESG.
Where did you get those jeans? An ESG data story.
As an example, let’s take a seemingly innocuous product like stonewashed denim. A fashion retailer buys stonewashed material from a supplier to manufacture their jeans. They consider themselves an ethical company, and it’s a key selling point to their investors and their consumers. They use only ethical factories and ensure that their clothing has a long life and can be recycled. It’s all in the data.
Except that it’s not.
The data they have backed up their data credentials, but it’s the data that’s missing that is hiding a secret. The stonewash fabric they import is made with damaging techniques and chemicals that are harming the environment and the people involved in production. But they don’t know – because the data doesn’t tell them.
Now let’s say the supplier of the unethical stonewash is exposed by the press. How quickly can the fashion retailer assess their supply chain, identify the products involved, and quickly cut the supplier and replace it with a more ethical alternative? They don’t know, because the data doesn’t tell them.
Well, why don’t they know? Is it because they don’t import supply chain data? Is it because the ‘composition and fabrics’ field is only seen as a priority for specific products, like silk or cashmere? Is it because this type of data wasn’t identified as an ESG priority when pulling reports on environmental impact? Is it because this data existed in a silo that couldn’t be accessed for analytics? It could be any number of reasons, but the fact is, a robust data quality initiative could have been the first step to ensure that it either didn’t happen in the first place or could be quickly and effectively rectified.
It’s as we said. Garbage in – poor quality, inaccurate, or missing data that hides ESG hotspots. Garbage out – consumer mistrust, poor financial results, pulled investments, failure to meet corporate ESG targets, and, potentially, non-compliance.
Get rid of the garbage: tackling data quality
With good-quality data, it can be a very different picture. With good data, you’ll have confidence that you can identify ESG issues wherever they occur in your organization and are able to react quickly when hotspots do appear, whether it’s excessive water consumption, pollution, or corporate malpractice.
Yet good quality data doesn’t just fall out of the sky: it’s up to you to address what ‘good data’ looks like for your organization, and ultimately for your ESG initiatives, in order to set data quality expectations for your business. Data quality can be slightly different for every organization, depending on what you do, your priorities, and so on, but it will always share these six core attributes:
Six attributes of good quality data
- Complete Data meets the requirement set by your business: all required fields are complete.
- Unique Only one version of the data exists – it isn’t being stored, edited, or used elsewhere
- Consistent All data meets the same criteria and is consistently available.
- Valid Data is presented in the right format and meets your organization’s data rules.
- Accurate The data reflects the truth – it can be applied to the ‘real world’.
- Timely The information is up to date and is updated regularly.
A data quality project can get your data up to standard, to ensure that all information meets your organization’s criteria. However, as soon as a data quality project is complete, that quality starts to deteriorate, unless processes are put in place to maintain data quality for the long term.
To cut a long story short, if you clean up your data, you can clean up your ESG – but data quality is just one aspect of data management for ESG. To find out the full story, download our guide – ESG unchained: A guide to finding your ESG hotspots.