There's a saying that you can only manage what you can measure.
We know that more organizations are looking to improve their sustainability metrics,
for instance, to meet net zero carbon emission goals
or to satisfy demands from customers and other stakeholders.
But getting that data in order is not easy.
IBM recently surveyed almost 2000 CEOs on the topic of sustainability,
and lack of data and insight
was identified as one of the top four roadblocks to progress.
So, what makes ESG data special?
When we talk about ESG data,
we are referring to the data that's used to track environmental, social and governance metrics.
These metrics help an organization understand their sustainability goals and targets.
Examples relating to the "E", for environmental, might be greenhouse gas emission.
For the "S", it might be volunteer hours.
And for the "G", for governance, it might be board diversity.
Typical enterprise data models have not included ESG data objects,
and it's quite unlike data sets that organizations are more familiar with, such as financial data.
Unlike financial data, ESG data is very broad.
For instance, one day you might be gathering data on the fuel use of your vehicle fleet.
The next day you could be looking at gender diversity at your manufacturing site.
It's also very voluminous.
For instance, in some jurisdictions, energy data is produced in 15 minute intervals,
which produces a lot of data that needs to be captured and managed.
Much of this data is not available from within the organization.
For instance, it may be held by suppliers
and there are no standards for this data,
so it is often provided in different units of measure with inconsistent data structures and formats.
Extracting the data is challenging.
It's held in all manners of systems, including ERP systems and building management systems.
And sometimes it's not even available in electronic format.
All of this presents a problem because without a data foundation,
you can't effectively set sustainability goals, manage them or report them accurately.
So how do organizations overcome this problem?
Just like with financial data,
organizations need support from specialist software to address this challenge.
Using ESG data management solutions, like IBM Envizi,
can support data capture using tools to extract, transfer and load data automatically
down to the most granular auditable level.
Once the data is collected, software can clean up, normalize it,
structure and target so it can be used for multiple purposes.
A good ESG data management solution can represent very complex organizational hierarchies and structures
and reflect how they manage performance, how they report, and what control they have over various assets.
Sustainability goals are very long term and business changes over time,
so the data structure needs to reflect changes over time,
such as who owns the assets and what reporting requirements need to be met.
An ESG data management solution provides a very comprehensive and robust data management platform
that models organizational structure and track data from the corporate level right down to the operational level.
So now you can build a foundation for decision making and measure and manage your ESG goals.