What are the most pressing data analysis challenges for sustainability professionals?

In my experience, the biggest challenge for sustainability professional when it comes to data is the availability of data. And in the case that data is available, the quality is often lacking. I am always surprised when my clients provide hand written notes or Excels where numbers are manually added. I believe it is key that companies transition to automated data management systems to reduce the workload that is associated with manually collecting data. This will result in the increase in data quality.

Sustainability professionals face challenges, including the need for accurate & comprehensive data collection from diverse sources. Ensuring data quality & reliability poses a hurdle, as sustainability data often comes in various formats & standards. Integration of data from different sectors & aligning it with sustainability metrics remains a complex task. Furthermore, there's a challenge in deriving actionable insights from vast datasets, requiring advanced analytical skills. Accessibility & sharing of data across organizations & industries also present hurdles, hindering collaborative sustainability efforts. Overcoming these challenges is essential to effectively drive sustainable practices and informed decision-making within the field.

In my experience as Internal auditor, the main challenges facing an organisation in relation to data quality and availability in the area of non-financial reporting are as follows: - Teams unfamiliar with periodic collection and reporting of non-financial information. - Lack of self-review and documentary support for non-financial information, resulting in increased workload during review and identification of errors. - Lower priority to comply with internal timelines for non-financial information compared to financial information. - Poorly automated systems and lower level of maturity compared to their equivalents for financial reporting.

At the start of my career I was doing audits (Grenelle II) of companies' extra-financial reporting systems. I was shocked by how many multi-billion dollar companies, at the ground level, were resorting to pen and paper to keep records of things as important as heavy fuel oil usage of tanker ships! What's more is that different international branches often used vastly different data gathering tools with varying levels of sophistication. Proper company-wide trainings on how to use state-of-the art software can improve data quality and in turn easily pay for itself with the efficiency improvements they unlock!

While collecting the background data while writing two critical chapters of the SDG 12 UN Corporate Guidebook Series with @SHERPA, I realized that not only my chapters 12.2 and 12.3, but almost all SDG targets lack data availability. The quality of data especially when it is self-reported might have a bias or cover-up leading to less informed decision-making. According to me, the first challenge is not the data analysis. There should be data to analyse in the first place! The data unavailability root cause is there are no means/ knowledge/ awareness and tools to collect clean and authentic data covering specific indicators of SDGs.

For large companies, data interoperability across geographies has been a severe challenge ranging from issues such as not knowing who in country X is responsible for the data to having completely different data management systems. Recommend the following: - Identify a common standard that needs to be adhered to across the organization - Conduct a short yet powerful live training that explains not just the process but how this data feeds into the organization’s end goals - Identify sustainability data champions and connect with them regularly to ensure problems are flagged at the right stage - Implement a data collection and visualization system

As a sustainability auditor, I witnessed that one of the significant challenges in data analysis is the lack of standardization of sustainability data. For example, organizations may employ varying metrics and measurement methods, making data comparison and interpretation difficult. This inconsistency can lead to uncertainties in assessing sustainability performance and undermine the credibility of sustainability reports.

As a finance lawyer in sustainability, I've witnessed the complexity of integrating diverse data sets. The challenge lies in merging data from varied sources and formats into a unified, coherent framework. Legal agreements and contracts often contain critical data points that need integration. My approach? Advocate for universal data standards and interoperable systems. This streamlines data flow, enhancing our understanding of sustainability impacts. By harnessing advanced analytics, we turn fragmented information into actionable insights, driving forward our sustainability goals.

Integrating and aligning data from various sources poses a common challenge in sustainability analysis. Overcoming issues like data silos, fragmentation, and duplication requires sustainability professionals to prioritize interoperability, compatibility, and integration. Adopting common data models and standards, along with utilizing data analytics tools like data warehousing, mining, and visualization, can help create a more unified and comprehensive view of sustainability performance and impact.

The dynamic nature of sustainability data makes data integration and alignment extremely challenging. The constantly changing environment means there are no fixed sustainability data sets to work with. Finding the right datasets that align not only in terms of sources and formats but also the time period they were collected from is crucial for easy integration and accurate results.

In my role, the power of data lies not just in its collection, but in its presentation. The challenge is making complex sustainability data comprehensible and actionable. Legal documents are dense with information, but distilling them into clear, visual formats is key. It’s about crafting a narrative that resonates. Utilizing data visualization tools, I focus on simplifying the complex, ensuring data tells a compelling story. This approach not only informs decision-making but also engages stakeholders effectively, making sustainability data a powerful tool for change.

Stakeholders often feel drowned in dense reports filled with jargon.They rarely inspire action. To break through the noise, sustainability professionals need to become data storytellers, understanding the emotions and motivations that drive their audience. This isn't just about fancy infographics or flashy animations. It's about connecting with people on an emotional level, building trust, and showing the human impact of our sustainability efforts. Tools like data visualization platforms and interactive dashboards can help, but the real power lies in understanding the story behind the data and knowing how to share it in a way that resonates with the audience.

Effectively communicating and visualizing data is a key challenge in sustainability analysis. Overcoming issues like data overload, complexity, or ambiguity requires sustainability professionals to embrace data storytelling and visualization best practices. This includes selecting relevant data, choosing appropriate formats, incorporating essential elements, and crafting a compelling narrative to provide clarity, context, and engage stakeholders in decision-making processes.

Addressing data communication challenges is paramount for Inside Sustainability. We acknowledge the struggle with data overload and ambiguity faced by sustainability professionals. To overcome this, we emphasize employing data storytelling and visualization best practices. This involves selecting pertinent data, utilizing appropriate formats, incorporating essential elements, and crafting narratives that resonate. By prioritizing clarity, context, and effective storytelling, our commitment is to ensure our data communicates in a compelling and actionable manner, facilitating informed decision-making and meaningful stakeholder engagement.

La tarea de transformar datos de sostenibilidad en comunicaciones claras y efectivas va más allá de la simple visualización; es un arte de traducción y narración. El desafío real aquí es cómo convertir una mara?a de datos, a menudo abrumadora y compleja, en un relato que no solo informe, sino que también inspire y movilice. Los profesionales deben ser narradores hábiles, capaces de destilar la esencia de los datos en historias que resuenen emocionalmente y motiven a la acción. Este proceso implica una comprensión profunda de lo que los datos significan en el gran esquema de la sostenibilidad, y cómo esta comprensión puede ser compartida de una manera que catalice el cambio.

Both ESG and RAI are fundamentally rooted in the promotion of ethical and sustainable practices within their respective domains. Recognizing this common ground, it is imperative to align these two initiatives. By doing so, responsible AI can contribute to ESG goals of reducing environmental impact and promoting social good, while ESG principles can guide the development and deployment of AI in a manner that adheres to high ethical and governance standards.

Responsible and ethical handling of data is a paramount challenge in sustainability analysis. Sustainability professionals must be vigilant to avoid data risks, dilemmas, or controversies, particularly concerning issues like awareness, consent, and accountability. Addressing this challenge requires adherence to data ethics and privacy principles and guidelines, encompassing practices like data minimization, anonymization, robust data security measures, and responsible data stewardship.

Interesante punto. Los proyectos de sostenibilidad son proyectos de datos. Los datos deben ser gobernados y tienen que disponer de una capa de de privacidad recogida en normativas que al mismo tiempo forman parte de los proyectos de sostenibilidad. En el ámbito de lo social y lo corporativo se manejan infinidad de datos que están sometidos a reglas de privacidad que no deben saltarse: sexo, salario, bajas, expedientes internos relacionados con delitos de corrupción, contra los derechos humanos, etcétera.

Unlocking the full potential of data for innovation and learning poses a significant challenge for sustainability professionals. To overcome this obstacle, professionals need to foster a data-driven culture and mindset within their organizations. Additionally, they should explore and apply advanced data analytics methods and approaches, such as data science, machine learning, or artificial intelligence. Cultivating curiosity, creativity, and a willingness to experiment with data will be crucial for realizing new insights, solutions, and opportunities in the realm of sustainability.

I am taking a different track from regulatory-driven data management here. How about we think about corporate sustainability data as something to be monetized? Can organizations create new markets for data? We are using this data to support our product claims, and overall sustainability picture across workforce demographics, footprint, and business strategy. Is there space for a data exchange? Something innovative to think about!

The purpose of sustainability data must be to inform decision making. Consideration must be given to data integrity, assurance and usage. Sustainability data must be effectively combined with other 'non-sustainability' data so that an organisation or investor can make informed decisions. Increasingly scrutiny and reliance on sustainability data will mean that a new level of data rigour and assurance is required. Companies need to ensure that suitably qualified internal and external skills and support are in place.

To begin with, the domain of sustainability is plaqued with far too many jargons, acronyms, definitions, interpretations. While taxonomies are evolving (EU taxonomy leads the pack) to make sure there is limited scope for ambiguity while publishing sustainability data, the utopian state of high quality data is still a moonshot away. The report preparers, at times, take advantage of the wide-ranging possibilities to interpret a disclosure parameter and report what is convenient for them. This happens in spite of standardised formats like XBRL being mandated for Sustainability reports. At the root, it is a matter of stepping up awareness and driving discipline among the preparers to ensure analysis-ready sustianability data is available.

Another challenge sustainability professionals face when analyzing data is deciding on a baseline year for tracking progress on various sustainability metrics. Often, there are certain things to consider when choosing a baseline year: availability of baseline data, accuracy of that data, changes in scope and boundaries for the data compared to the present situation, and granularity of the data. Sustainability professionals should consider these before choosing baseline data. Once a baseline is selected, it is recommended to document - 1) all considerations and justifications for choosing the baseline, 2) exceptions if any, and 3) criteria for re-evaluating the baseline in the future.

Every material data point should have a “data responsible” assigned to it. This would make it easier to find, reconcile, and update data. I have been surprised many times when Heads of Sustainability do not know who is responsible for a certain data point.

Analysis Complexity: Multi-dimensionality: Sustainability issues are inherently interconnected, involving environmental, social, and economic factors. Analyzing their intricate relationships and trade-offs requires sophisticated techniques. Long-term impact assessment: The true effects of sustainability initiatives often manifest over long periods. Accurately predicting and understanding long-term impacts from short-term data presents a major challenge. Attributing causality: Establishing clear cause-and-effect relationships between interventions and outcomes is crucial for measuring success. However, external factors and confounding variables can make isolating direct impacts difficult