Editor’s Note: The pressure is growing on organizations to better integrate the Environmental, Social, and Governance (ESG) ethos into strategy and decision making. Alteryx Senior Principal Jawwad Rasheed highlights why data and analytics automation should be a priority for executive leaders in 2023 and beyond.
As we enter another year of business complexity and disruption, many organizations will be tempted to decouple their Environmental, Social, and Governance (ESG) ambitions from short term and strategic goals of cutting costs, increasing revenues and address market risks.
This could prove to be a grave mistake.
From the European Commission’s Corporate Sustainability Reporting Directive to the United States’ Securities and Exchange Commission (SEC) and mandatory greenhouse emissions disclosures, ESG legislation is doubling down on reporting mandates, and major players like BNY Mellon and Goldman Sachs have already felt the consequences of non-compliance. Though the fines of $1.5 million and $4 million respectively was relatively nominal, the brand damage has been significant and has caused nervousness to ripple across the markets.
But ESG is more than a simple matter of regulation and compliance, it’s an opportunity for organizations to showcase transparency and accountability and to demonstrate commitment to sustainable and ethical practices — factors increasingly demanded by consumers, investors and employees. So, given the potential penalties, brand damage, and pressure to operate ethically, why do organizational leaders find it so difficult to gather, analyze, and report on ESG data?
Challenges with ESG Data
Many organizations are grappling with internal data challenges, so the idea of sourcing and aggregating multiple datasets — including those from partners and third parties — can feel like an impossible challenge. Here are 5 common data challenges in relation to ESG:
Availability of ESG data
Understanding what ESG data is available and attainable is critical. Take a typical manufacturing firm, for example, where 60-80% of total emissions — the so-called Scope 3 related emissions — can be made up from supply chain and distribution processes. It can be challenging to ascertain which benchmarks from data providers and rating agencies can be relied upon to address shortfalls in Scope 3 emissions. Issuer-provided data may not follow a common standard, so metrics produced from that data could be flawed or unclear.
Sourcing ESG data
Executive leaders need to bring together huge datasets with multiple variables from various partners across the supply chain — a significant ask when manual processes simply aren’t up to the task. Now consider the data points that need to be sourced internally alongside the reliance on external organizations that may have varied collection policies, processes, and systems – and may not share the same level of accountability or urgency. The multi-dimensional and decentralized nature of the data with wider array of formats further exacerbates ESG data collection methodologies and integrations.
Traceability of ESG data
Maintaining traceability throughout the data lifecycle becomes extremely challenging when attempting to piece together disparate and varied datasets. For example, one company could be dealing with decarbonization data, procurement data, ERP data and management forecasts. With more rigor expected for ESG related audits, providing transparency of data from sources to reporting will remain a challenge as organizations reconsider legacy processes, tools and systems. Further consideration is required on when and how to integrate ESG auditing with existing risk assessments, and even financial reporting workflows.
Attention to data security has proliferated with the rise of big data and data protection laws globally. In addition to perpetual concerns such as anticorruption and climate change, cybersecurity is rising to the top of the ESG agenda. The consequences of failing to protect customer data can range from a loss of assets, eroded “trust” between organizations and customers and permanent damage to organization’s reputation. Taking a more ESG-centric approach to data security can promote digital trust in organizations, though execution on this mantra remains a challenge with the growth of ESG data.
Countless options — but no “one size fits all” solution
Organizations that fail to unlock value in ESG data and integrate insights into decision-making will be missing huge opportunities. Success requires not only the reassessment of performance management processes and capabilities – but also a fundamental shift in culture that democratizes analytics and applications to promote innovation. There are no shortages in data and analytical reporting solutions for ESG, though careful consideration is required to find the balance between control, flexibility and self-serve capabilities that align to each organization’s ESG strategy. So, given the extent of the data challenges and related impacts on analytics and reporting and the need to comply with mandatory reporting and disclosure, where should organizations begin to find technology solutions?
Here are five questions you should ask when evaluating data, analytics and reporting solutions for ESG:
- Can the solution seamlessly connect to multiple data sources and run large-scale and complex data integrations with ease?
- Can the solution be automated to alleviate the extensive manual efforts and high costs for sustainability data normalization and preparation?
- Is the solution dynamic and flexible to accommodate for changes across the ESG landscape, while still maintaining data governance standards?
- Does the solution drive an innovation culture with self-serve low-code/no-code capabilities that help to enable a wide range of users – from data novices to data scientists?
- Can the solution go beyond ESG data management to uncover and drive analytical insights to all decision makers?
The Alteryx Analytics Automation Platform delivers on all these requirements. For example, with our advanced machine learning capabilities, you can build sophisticated data aggregation processes that feed into your preferred monitoring and reporting platforms, such as Tableau and Power BI. With complete end-to-end transparency, it’s easy to see how data is ingested and enriched, giving you immediate visibility into the data journey — critical for building reliable and compliant ESG disclosures.
And with extensive analytical capabilities, such as Auto Insights, you can rapidly uncover insights and key drivers to optimize performance against your ESG goals.
By seamlessly connecting multiple datasets from across your operations automatically, you can keep track of large, changeable datasets with a more cost-effective approach and champion ESG in your industry. Make automated ESG data and analytics processing your goal for 2023.
While investing in more advanced ESG data and analytics solutions may seem like a false economy this year, the longer-term benefits of greater efficiency, social consciousness, and ethical integrity make it a no-brainer decision.