Why data products are critical to environmental, social, and governance (ESG)

Environmental regulations are coming. 

If it’s not a priority right now, remember GDPR? Financial services were warned about GDPR. In fact, many organizations knew GDPR was coming, protecting PII was critical, and assumed that the regulation would be, well, ‘fine’. And when actual penalties and fines arrived, financial services, banks and insurance companies were in a panic because they got started far too late. 

Now that we’ve been warned. The umbrella term we use for environmental regulation is environmental, social, and governance(ESG). E for environmental in ESG covers things like water wastage, green energy, carbon emissions, greenhouse gases. 

When it comes to emissions, data is absolutely key. 

Defining emissions with Greenhouse Gas Protocol

In taking a closer look at how we use data for emissions and in terms of regulations, we have the Greenhouse Gas Protocol(GHG), where emissions are defined across three scopes. These definitions are critical to meeting the challenges facing organizations to extracting ESG-relevant data across these scopes.

Scope 1 is what we call direct emission scope. 

Scope 1’s official definition states, “emissions from operations that are owned or controlled by the reporting company.” These are the emissions that have everything to do with your organization, so be certain to build the strategy to manage that type of data. 

Examples: Emissions from combustion in owned or controlled boilers, furnaces, vehicles; emissions from chemical production in owned or controlled process equipment. 

Scope 2 is more the indirect services you use

Scope 2’s definition states, “emissions from the generation of purchases or acquired electricity, steam, heating, or cooling consumed by the reporting company.” 

Examples: Use of electricity, steam, heating, or cooling. As far as IT implications, it would also include your cloud service provider’s electricity, even if, for example, you’re using IT services from Capgemini, and the ESG data from your system integrator. 

Scope 3 looks at indirect, upstream and downstream emissions

Scope 3’s definition states, “all indirect emissions(not included in scope 2) that occur in the value chain of the reporting company, including both upstream and downstream emissions.” 

Scope three gets more complicated, particularly with supply chain. You could be a manufacturer of a mobile phone, the suppliers providing the batteries for your mobile phone— that’s what we call upstream scope 3 emissions, which you will be responsible for reporting. And downstream could be, what are the processes surrounding end of life for those mobile phones? 

What the three scopes mean for your organization

As an organization, what new regulations require is attention in not only Scope 1 emissions, but also Scope 2 and Scope 3.

One key data point you will be responsible for is the emissions for your own organization. You’ll also have to report on the partners that supply you direct services. But also if you outsource the supply chain — those close partners that make up your full supply chain— you will be responsible for reporting on your own emissions, but also scopes two and scopes three. 

So, a well-organized data is a key lever. 

Why data mesh and data products are key for an organization to organize their data strategy

If your organization still has centralized massive data lakes, it becomes really, really difficult to shift through that data lake to find out particular data points that need to be extracted to do your reporting. So the only game in town are the principles of data mesh and data products because that’s where you have the domain experts who can determine if a piece of data within that data set is relevant for your ESG reporting or not.

If you’re thinking about building data products the traditional way by ingesting data, you might want to reconsider. It is more expensive and time consuming — and building data products this way is far more complicated. As an example: you’ll ingest data, you’ll hire some data engineers, they write Python or Scala, it runs on a Spark engine, and it ends up in a cloud data warehouse or in a S3 bucket. Except, there may be times when you’ll need to prepare data and you may need the sophistication of a programming language to prepare that data set, but it is still more complicated. 

Fortunately, we have an easy way to build data products with Starburst. We’ve been able to demonstrate to clients an example of what a data product looks like in terms of its searchability, its addressability, and the quality aspects. And where there are relevant ESG data points within your organization, they need to be surfaced at the base level and then aggregators of data products help you to build up so that you can get a handle on this data. 

Putting it all together: ESG data aggregated via Data Products

Scope one is your organization. You’re using Starburst, you’re working with Capgemini. Together, we have helped with the methodologies to design what you want to build and the methodologies around how you do it in an efficient way. You’re conscious and aware of your own organization. Bottomline: you have a strategy in place. 

Direct Service Providers

You’ll need to ask about your direct service providers? Are they as organized as you? And even the partners that assist you with supply chain, you may in the future, need to choose partners that comply with the same rigor of your organization when it comes to being conscious about emissions and ESG reporting. It’s important to take that into consideration.

Listen more to our talk at Datanova and contact Dan and Andy if you would like to dialogue about ESG and data mesh.