Environmental Data Management

The Intersection Between MDM, PIM and ESG

Henrik Gabs LiliendahlLeave a comment

As touched on in the post Three Essential Trends in Data Management for 2024, the Environmental, Social and Governance (ESG) theme is high on the data management agenda in most companies. Lately I have worked intensively with the intersection of ESG and Master Data Management (MDM) / Product Information Management (PIM).

In this post I will go through some of the learnings from this.

Digital Product Passport

The European Union concept called the Digital Product Passport (DPP) is on its way, and it will affect several industries, including textile, apparel, and consumer electronics. The first product category that will need to comply with the regulation is batteries. Read more about that in the article from PSQR on the Important Takeaways from CIRPASS’ Final Event on DPP.

I have noticed that the MDM and PIM solution providers are composing a lot of their environmental sustainability support message around the DPP. This topic is indeed valid. However, we do not know many details about the upcoming DPP at this moment.

EPD, the Existing DPP Like Concept

There is currently a concept called Environmental Product Declaration (EPD) in force for building materials. It is currently not known to what degree the DPP concept will overlap the EPD at some point in the future. The EPD is governed by national bodies, but there are quite a lot of similarities between the requirements across countries. The EPD only covers environmental data whereas the DPP is expected to cover wider ESG aspects.

Despite the minor differences between DPP and EPD, there is already a lot to learn from the data management requirements for EPD in the preparation for the DPP when that concept materializes – so to speak.

Environmental Data Management

The typical touchpoint between the EPD and PIM today is that the published EPD document is a digital asset captured, stored, tagged, and propagated by the PIM solution along with other traditional digital assets as product sheets, installation guides, line drawings and more.

The data gathering for the EPD is a typical manual process today. However, as more countries are embracing the EPD, more buyers are looking for the EPD and the requirements for product granularity for the EPD are increasing, companies in the building material industry are looking for automation of the process.

The foundation for the EPD is a Life Cycle Assessment (LCA). That scope includes a lot of master data that reaches far beyond the finished product for which the EPD is created. This includes:

  • The raw materials that go into the Bill of Materials.
  • The ancillary materials that are consumed during production.
  • The supplier’s location from where the above materials are shipped.
  • The customer’s location to where the finished product is shipped.
  • The end user location from where recycling products is shipped.
  • The recycled product that goes back into the Bill of Materials.

All-in-all a clear case of Multi-Domain Master Data Management.

It is easy to imagine that the same will apply to products such as textile, apparel and electronics which are on the radar for the DPP.

Examples of Environmental Data

CO2 (or equivalent) emission is probably the most well known and quoted environmental data element as this has a global warming potential impact.

However, the EPD covers more than twenty other data elements relating to potential environmental impact including as for example:

  • Ozone layer depletion potential – measured as CFC (or equivalent) emission.
  • Natural resource (abiotic) depletion potential – measured as antimony (or equivalent) consumption.
  • Use of fresh water – measured as H2O volume consumption.

Can I help You?

If you are in a company where environmental sustainability and data management is an emerging topic, I can help you set the scene for this. If you are at an MDM/PIM solution provider and need to enhance your offering around supporting environmental sustainability, I can help you set the scene for this. Book a short introduction meeting with me here.

Three Essential Trends in Data Management for 2024

Henrik Gabs LiliendahlLeave a comment

On the edge of the New Year, it is time to guess what will be the hot topics in data management next year. My top three candidates are:

  • Continued Enablement of Augmented Data Management
  • Embracing Data Ecosystems
  • Data Management and ESG

Continued Enablement of Augmented Data Management

The term augmented data management is still a hyped topic in the data management world. “Augmented” is here used to describe an extension of the capabilities that is now available for doing data management with these characteristics:

  • Inclusion of Machine Learning (ML) and Artificial Intelligence (AI) methodology and technology to handle data management challenges that until now have been poorly solved using traditional methodology and technology.
  • Encompassing graph approaches and technology to scale and widen data management coverage towards data that is less structured and have more variation than data that until now has been formally managed as an asset.
  • Aiming at automating data management tasks that until now have been solved in manual ways or simply not been solved at all due to the size and complexity of the work involved.

It is worth noticing that the Artificial Intelligence theme lately has been dominated by generative AI and namely ChatGPT. However, for data management generative AI will in my eyes not be the most frequently used AI flavor. Learn more about data management and AI in the post Three Augmented Data Management Flavors.

Embracing Data Ecosystems

The strength of data ecosystems was latest examined here on the blog in the post From Platforms to Ecosystems.

Data ecosystems include:

  • The infrastructure that connects ecosystem participants and help organizations transform from local and linear ways of doing business toward virtual and exponential operations.
  • A single source of truth for ecosystem participants that becomes a single source of truth across business partner ecosystems by providing all ecosystem participants with access to the same data.
  • Business model and process transformation across industries to support agile reconfiguration of business models and processes through information exchange inside and between ecosystems.

In short, your organization cannot grow faster than your competitors by hiding all data behind your firewall. You must share relevant data within your business ecosystem in an effective manner.

Data Management and ESG

ESG stands for Environmental, Social and Governance. This is often called sustainability. In a business context, sustainability is about how your products and services contribute to sustainable development.

When working as a data management consultant I have seen more and more companies having ESG on top of the agenda and therefore embarking on programs to infuse ESG concepts into data management. If you can tie a proposed data management effort to ESG, you have a good chance of getting that effort approved and funded.

Capturing ESG data is very much about sharing data with your business partners. This includes getting new product data elements from upstream trading partners and providing such data to downstream trading partners. These new data elements are often not covered through traditional ways of exchanging product data. Getting the traditional product information through data supply chains is already challenged so adding the new ESG dimension is a daunting task for many organizations.

Therefore, we are ramping up to also cover ESG data in the collaborative product data syndication service I am involved in and is called Product Data Lake.