Much of the conversation around ESG reporting has been on the regulatory reporting front, with companies scrambling to meet “Environmental, Social, & Governance” disclosure requirements taking effect over the next year in the European Union (EU) and the United States (US), among others.
But regulatory reporting is only the beginning. As awareness grows that ESG and sustainability reporting is largely about identifying and managing financial risks and opportunities, more companies are moving beyond the near-term challenge of satisfying regulators, and looking at the data being collected as an asset that can inform better strategic and financial planning. FP&A (Financial Planning & Analysis) teams are getting involved.
Moreover, any company that has announced a “Net Zero” or similar carbon reduction goal needs to treat it like any other business target: if there is no plan to track emission levels as well as the costs of the carbon reduction and offsetting initiatives, it is nothing more than an empty public relations exercise that could backfire.
The good news for companies that already own IBM Planning Analytics is that it can provide both tactical (“quick & easy”) and strategic support for several ESG and sustainability use-cases, in addition to being a data hub for all quantitative ESG data and a data source for other downstream applications. We’ll explore this more below.
If it is your job to help your company increase the value it derives from its investment in IBM Planning Analytics, read on!
1. Materiality Assessments
Every ESG reporting framework and regulation requires a materiality assessment, which is loosely about identifying information that a reasonable investor would consider important when making an investment decision.
Materiality assessments generally involve sending out surveys to stakeholders (employees, customers, business partners, board members, etc) to gather information on “what is material” from as many perspectives as possible. Once the data is collected, it needs to be aggregated and summarized. One subtlety that is sometimes missed is that this data collection step can also include a preliminary analysis of quantitative data, in addition to typical survey questions, to validate sentiment at least directionally. For example, suppose the survey data exposes Energy Efficiency as a moderate impact issue, but the preliminary analysis shows an increasing trend in energy usage, this should induce closer scrutiny.
The most common tool used to do all this is … the spreadsheet.
The most basic value proposition for IBM Planning Analytics is that it transcends the limitations of spreadsheets and makes data collection and processing easy. For the survey piece of materiality assessments this allows more energy to be spent on designing quality surveys, increasing participation, automating the generation of aggregated reports, and doing good analysis. The analysis piece is important because focusing on fewer materiality factors – and executing well on them – will yield better results.
For the preliminary data analysis it is also a useful tool depending on the scope of what is being analyzed.
Furthermore, materiality can evolve and shift over time, so these assessments should be a regular annual exercise. Having the data in IBM Planning Analytics makes it easy to monitor trends, and to support audit processes when materiality must be justified.
2. Carbon Accounting
Carbon accounting is how greenhouse gas (GHG) emissions are measured and tracked. Most ESG reporting frameworks accept the GHG Protocol as the basic methodology for converting measured emissions from different GHGs into a standard unit (CO2-e - “carbon dioxide equivalents”) and aggregating the data for consolidated emissions reporting.
The companies (and countries!) that have announced carbon reduction or “net zero” goals are committing to reducing (or offsetting) emissions relative to a baseline and announcing plans to do so.
IBM Planning Analytics can support carbon accounting on several fronts, including:
- As a repository for collecting emissions data, including from within the organization (Scopes 1 & 2) and from the value chain (Scope 3).
- For converting emissions to CO2-e at detailed levels and aggregating them.
- This can be done with actual data (for reporting) and with plan data (for performance management)
- For reporting and analysis.
Refer to our earlier blog post on Using IBM Planning Analytics for ESG Reporting and Performance Management for more information on emissions reporting.
3. ESG Metrics & Disclosure
GHG emissions get a lot of attention and are the primary (or only) metric that many regulators are looking for, but they are only one metric in the sustainability pantheon. It may turn out that your business does not directly emit a material amount of GHGs but is materially impacted by other environmental, social or governance factors. For example, consider the professional services industry in which high employee turnover, a social (“S”) metric, reflects the high cost of attracting and replacing lost talent, or a beverage company whose biggest concerns are water availability and plastic bottle recycling.
Ultimately ESG metrics are quantitative business metrics, and many of them incorporate financial data (for example carbon intensity metrics such as [GHG Emissions]/[Revenue]). IBM Planning Analytics is a proven platform for collecting, organizing and aggregating such data and has clear advantages over typical IT solutions because of its flexibility to be business-driven and adapt rapidly to changing and various data sources and new additions.
IBM Planning Analytics is also an excellent reporting tool that can easily populate pre-existing Excel formatted reports, and it can be used to satisfy audit requirements which will evolve towards higher assurance standards in coming years.
Lastly keep in mind that sustainability reporting requirements extend beyond what the SEC (in the US) or CSRD requires. IBM Planning Analytics can also support data collection and management for the growing number of Extended Producer Responsibility (EPR) reporting requirements that are appearing at the state level, for example in the states of Washington and Maine.
4. ESG Performance Management
If an organization is serious about achieving ESG goals, e.g. reaching Net Zero Emissions by a certain date, there has to be a way to manage performance against a plan, as we wrote about here.
Performance management is what IBM Planning Analytics was built for, and it can help you track (and report on) ESG targets and data just as easily as it can track (and report on) financial targets and data.
Early detection of divergences between actual performance and targets becomes easy when everything is on one platform which in turn buys time to analyze the variances, understand their causes, and devise a course of action.
Shortening the time between analysis and action in this way provides the agility needed to manage the constant change and unexpected turns which are normal in business.
5. Integrated Financial and Sustainability Modeling
Achieving ESG goals will require investment (sometimes called “transition costs”). With IBM Planning Analytics performance management can be taken to the next level through its ability to integrate financial and ESG planning models.
Models are powerful tools for exploring and analyzing scenarios and finding ways to maximize return on investment (ROI) in financial as well as ESG terms.
Integrated financial and ESG models benefit financial performance too since there are many non-financial metrics that can have significant financial impact as we wrote about here. The possibilities are limitless, but here are three examples:
- - A Bill of Materials (BOM) model can be linked to cost, waste and emissions models
- - An employee labor model can be linked to travel and office space emissions models
- - An existing capital investments/fixed assets model can be modified to carry emissions information as well
Ultimately outcomes are what matter, and having an extended model of your business increases the probability of making the best decisions to reach those outcomes through scenario and impact exploration.
IBM Planning Analytics is particularly strong on this front because of its superior performance and flexibility at scale, and its ability to easily connect to many diverse data sources natively and via its rich set of APIs.
6. AI & Optimization
Perspective 1: Optimizing the Cost of AI Investments
AI, especially generative AI which has exploded since 2023, is known to consume tremendous amounts of energy resulting in significant increases in greenhouse gas (GHG) emissions. Meanwhile many companies are expressing commitments to incorporating generative AI in their internal and customer-facing solutions, ranging from productivity tools to intelligent chat-bots while also struggling to manage and optimize their cloud-computing spend, which has led to the whole new discipline of cloud financial management, or “FinOps”.
This sets the stage for a classic IBM Planning Analytics analysis use-case to collect the data, model the anticipated growth in the cloud-spend run-rate to match the new AI investments, and compare that to the expected return. Alternatives can be compared – should we train a model from scratch, or fine-tune a foundational model such as (say) an IBM Granite model? Undertaking this activity will likely uncover cost-saving opportunities as well, for example from cloud servers that are under-utilized. And once an organization has a cloud FinOps model in place, IBM Planning Analytics can easily track the impact of the accompanying GHG emissions in tandem to complete the picture.
Perspective 2: Applications of AI and Optimization for ESG Use-Cases
A second perspective is to look at “AI & Optimization” as tools that can enhance IBM Planning Analytics in all the use cases listed earlier.
IBM Planning Analytics has built-in out-of-the-box AI features in the form of its embedded AI forecasting which uses the IBM watsonx Time Series libraries to generate automated forecasts that account for seasonality and trends, with options for detecting and excluding outliers. In ESG performance management use-cases such as planning to meet carbon reduction and “Net-Zero” emissions goals this feature provides a way to see how progress is trending using just a few clicks.
AI forecasting can be useful for ESG Metrics and Disclosure too, to help with gaps in data collection, which is a common issue for ESG data which has not historically been subject to the same business process controls as financial data.
The IBM Planning Analytics REST API creates even more opportunities for enhancing any of these solutions. This IBM video shows (1) how the IBM watsonx Assistant chat-bot can be deployed to create a conversational user experience with Planning Analytics Workspace including exploring data and updating a forecast, and (2) how an external data science model can be deployed to the Planning Analytics database in real-time. The same approach also works with IBM ILOG CPLEX to solve complex optimization problems.
Conclusion
IBM Planning Analytics is a powerful and versatile platform that can be deployed quickly and inexpensively to address many kinds of data collection, reporting and modeling challenges including the growing number relating to sustainability and ESG. This blog post has highlighted the most obvious ones that many IBM Planning Analytics customers are already exploring.
If you will be attending IBM TechXchange Conference 2024 in Las Vegas from October 21 – 24 and would like to hear more on this topic, please attend our session 1908 on Tues Oct 22, 2024 at 3pm PDT on How IBM Planning Analytics with Envizi and AI Enables Sustainable Business Success!
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