So your company has publicly committed to big ESG targets - it's time to develop a proper data solution to guide how you’ll get there!

Note - for purposes of this post, I am generally emphasizing the data path specifically for environmental (“E”), and even more specifically emissions-related risks and opportunities, as those pose some of the most difficult data management and analytics challenges to address within ESG.

Per our recent annual PwC Corporate Directors Survey: Only 25 percent of directors say their boards understand ESG risks very well, despite 64 percent of directors stating ESG is linked to their company strategy. This is an alarming headline on the surface, but this should not surprise anyone who has truly ventured to quantify, articulate and propose detailed action plans to address ESG risks and opportunities.

In the spirit of “solutions” (and thanksgiving week!) ..let me start by partly coming to the defense of said board directors: The reality is that ESG risks and opportunities are incredibly complex and dynamic, must consider several diverse stakeholder groups (e.g. investors, regulators, lenders, customers, and company management to name some), include uncertain and quickly evolving regulations and taxation systems and policies that differ by jurisdiction, and layers of assumptions and dependencies such as those related to organic business growth /changes, immature or undeveloped technologies, complicated supply chain variables, customer price sensitivity /behavior, and the future qualification and treatment of emissions offsets... And exacerbating all this complexity and dynamism, the data needed to perform such comprehensive and actionable analysis is generally decentralized, often incomplete and/or unstructured, and natively lacks the level of validation and reliability relative to other traditional financial data. For these reasons, most companies (finance leaders / management teams) have not furnished a complete, data-driven analysis that clearly articulates the ESG risks, opportunities and financially optimal path forward (comparing many other paths and options) in a consumable way for their board directors or other internal stakeholders.

The good news is that just as they do with any important market force and data challenge, leading companies will (and are already starting to) figure out how they will not only “report on and manage” ESG risks, but how they will differentiate and profit from the ESG transition. Take note from several of the great clients our team is working with that are ESG-forward and I believe will achieve a material competitive advantage from it (and save costs /make more profit in the process):

One common and critical pillar of their efforts is the development of a robust, dynamic data model and data platform that surfaces, refines/enriches and performs calculations on ESG data to both drive their historical-looking sustainability reports AND (more importantly) drive forward-looking, actionable management insights to be able to achieve ESG goals in a financially optimal way. A good step 1 is to build a data solution/ model that automates and monitors the ESG metrics already being publicly reported today (i.e. look at the information and KPI’s your company is already including in annual sustainability reports).

Step 2 is then to bridge the additional datasets related to forward-looking commercial business and market forecasts, expectations around future tax incentives and costs such as emissions-related taxes, categories of many other relevant assumptions and scenarios, and logic (e.g to perform allocations of available emissions data to specific assets where source data lacks), and to develop options and recommendations for the important forward-looking sustainability-related decisions facing management. A proper data solution and analysis should incorporate options/ decisions around business models, tactical operational and product production levers, procurement and supply chains, tax-efficient investments, and inorganic m&a decisions.

Despite a recent abundance of exciting ESG reporting tech products from a variety of vendors, my advice is to assume that none of these individual products will be a silver bullet point solution to solve the data management and analytics problem statement around ESG (albeit, some may play an important role in the solution ecosystem). Further, an ESG data model and analytics solution that will meet the needs described above “now” and in the immediate foreseeable years for most companies, will likely be a combination of: a core cloud data platform that has virtually limitless capacity for data intake types and volumes and data processing, can perform and/or integrate with other tools as needed that can perform calculations and scenario logic (including ability to connect to/run ML models), will increasingly integrate with “upstream” core financial systems and IoT applications (e.g. emissions censors), and will integrate with “down-stream” end-user analytics /visualization tools. This type of solution will balance the need for utility with the need to be stood up and iterated on quickly. It cannot be over-emphasized that the solution design and governance must assume that the data inputs, logic and outputs will all expand and evolve significantly and frequently, so flexibility is paramount!

In summary - It will take many years for complete sustainability data ecosystems to develop with the same level of rigor, controls and integration as the traditional financial reporting /ERP ecosystems that companies have built up over decades. Yet material decisions and investments are being made, or at the very least evaluated, NOW by many companies that will determine the effectiveness and costs of their short and long-term execution relative to their sustainability commitments. Leading companies are beginning to move forward and will iterate quickly on their ESG data and analytics solutions following similar approaches to what I described above - and on the flip side, companies that wait to do this type of analysis (until inevitably forced to by new reporting /disclosure regulation, tax authority compliance requirements, and/or other internal or external stakeholder pressure) will severely limit their ability to manage ESG risks and costs, may ultimately be forced to exercise inorganic levers to achieve ESG goals as committed progress timelines “come due” (e.g. may need to decommission assets, sell off plants or entire product lines, or otherwise spin off “bad ESG” businesses, or acquire businesses that bring “green” benefits in lieu of more accretive business synergies in other areas) and will minimize their opportunities to profit from the ESG transition.

So back to the 75 percent of directors that say their boards do not understand ESG risks (per aforementioned PwC Directors survey).. I am optimistic that as more finance leaders /management teams develop the type of effective, quantitative ESG data solutions and analysis for their internal stakeholders described above both of the following will happen:

1- Board Directors will be able to truly understand the risks and opportunities around ESG as they apply to the specific company(ies) they oversee

2- Boards, as well as the other important internal advisors/stakeholders at the company that should collectively be engaged in the material ESG-related business decision making, will be able to contribute to the discussions about management’s optimal roadmap to achieving ESG goals, regardless of whether they have had a long and deep history of developing their individual ESG acumen to date.

Our team is growing rapidly and we have great opportunities for data engineers looking to solve the incredibly important problem statements around ESG and other strategic enterprise functions with some of the worlds best companies (pre-IPO to fortune 10). Just reach out to find out more!

#PwC #ESG #DataAutomation #SustainabilitySolutions