ESG Data: Enhancing Sustainability and Corporate Responsibility

Unlocking the Power of ESG Data: A Comprehensive Guide

ESG data, including Environmental, Social, and Governance factors, plays a pivotal role in the corporate world's pursuit of achieving sustainability and responsible business practices. Understanding the nuances of ESG data is vital for companies looking to establish their commitment to ethical standards.

Below is an introduction to the terminology, with a clear breakdown of definition, criteria and company objectives for each contributing word to the acronym ESG.

Environmental (E): Driving Eco-Friendly Practices

• Definition: Assessing a company's impact on the planet and natural resources.

• Criteria: Includes carbon emissions, energy efficiency, waste management, water usage, and more.

• Objective: Companies strive to minimize their environmental footprint, adopting eco-friendly practices for sustainable operations.

Examples of Environmental Data

• Carbon Emissions: Quantifying the amount of greenhouse gases emitted by a company's operations, including Scope 1 (direct emissions), Scope 2 (indirect emissions from purchased energy) and Scope 3 (result of activities from assets not owned or controlled by the reporting organization, but that the organization indirectly affects in its value chain.)

• Energy Efficiency: Measuring the energy consumption and efficiency of operations, such as kWh per unit produced or per square foot of facility space.

• Waste Management: Reporting on the amount of waste generated, recycled, and disposed of by the company, along with initiatives to reduce waste and promote recycling.

• Water Usage: Tracking the volume of water used in production processes and facilities, as well as efforts to conserve water and minimize water-related risks.

Social (S): Fostering Inclusive Societal Impact

• Definition: Examining a company's relationships with employees, communities, and broader society.

• Criteria: Encompasses labor practices, diversity and inclusion, human rights, community engagement, and social responsibility initiatives.

• Objective: Companies aim to create positive societal impacts by fostering fair workplaces, respecting human rights, and actively supporting communities.

Examples of Social Data

• Labor Practices: Reporting on employee turnover rates, diversity and inclusion metrics, training and development initiatives, and adherence to labor laws and regulations.

• Community Engagement: Documenting community outreach programs, philanthropic initiatives, and partnerships with local organizations to address social issues and support community development.

• Human Rights: Assessing the company's commitment to upholding human rights standards throughout its operations and supply chain, including policies to prevent forced labor and ensure fair treatment of workers.

• Product Safety: Providing data on product quality, safety incidents, recalls, and compliance with regulatory standards to ensure consumer health and safety.

Governance (G): Upholding Ethical Leadership

• Definition: Assessing the structure and practices of a company's leadership and decision-making processes.

• Criteria: Includes executive compensation, shareholder rights, anti-corruption policies, and corporate governance.

• Objective: Companies strive for ethical leadership, transparency, and effective governance structures, fostering accountability and building trust among stakeholders.

Examples of Governance Data

• Executive Compensation: Reporting on executive pay ratios, incentive structures, and alignment with long-term performance goals and shareholder interests.

• Shareholder Rights: Detailing voting rights, proxy access, and governance mechanisms to ensure shareholder participation and protection of minority interests.

• Anti-Corruption Policies: Describing measures to prevent bribery, corruption, and conflicts of interest, including codes of conduct, whistleblower mechanisms, and compliance programs.

Measuring and Reporting ESG Data: Demonstrating Transparency

Metrics and Indicators: Effective measurement of ESG performance involves the use of both quantitative and qualitative metrics and indicators. Quantitative measures provide numerical data that can be easily compared and tracked over time, such as carbon emissions, energy consumption, employee turnover rates, and diversity ratios.

Reporting Standards: To ensure consistency and comparability in ESG disclosures, companies often adhere to established reporting frameworks and standards. Organizations such as the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) provide comprehensive guidelines for reporting on environmental, social, and governance issues.

Regulatory Reporting: CSRD, Corporate Sustainability Reporting Directive, replacing the Non-Financial Reporting Directive, extends regulatory reporting with regards to ESG, to ensure that larger and public organizations adhere to ESG policies and regulations. The new regulatory reporting will be mandatory from 2025 for all EU listed companies, companies having more than 250 employees or a net turnover of more than 40 EUR million or total assets exceeding EUR 20 million.

Importance for Stakeholders: Aligning Values for Success

Investors: ESG data serves as a critical tool for investors seeking to align their investments with their values and long-term financial goals. By analyzing a company's sustainability and risk management practices, investors can make more informed decisions about where to allocate their capital.

Companies with strong ESG performance often demonstrate a commitment to responsible business practices, which can translate into reduced risks and enhanced financial performance over time. As such,

Employees: Companies that prioritize ESG factors tend to attract and retain top talent by offering meaningful opportunities for employees to contribute to societal and environmental well-being. In fact, more than 40%, a recent study by Deloitte showed that more than 40% of gen-z and millenials would be willing to change jobs over climate concerns.

Also, employees are more likely to feel a sense of purpose and pride in their work when they are part of an organization that demonstrates a genuine commitment to sustainability and ethical conduct.

Customers: ESG considerations have become a significant factor influencing purchasing decisions, with recent studies showing how many consumers are more actively seeking out products and services from companies that prioritize environmental stewardship, social responsibility, and ethical governance. A recent study by NielsenIQ found that 78% of American consumers state that sustainability is important in purchasing decisions.

Summary

This in-depth guide to ESG data provides an overview of the terminology and demonstrates the importance of both measuring and reporting the aspects included within the scope of ESG.

Transforming Data Challenges into Strategic Insights: A Manufacturing Case Study

To achieve data-driven decision-making, businesses often encounter a shared hurdle - efficiently translating raw data into actionable insights. A second challenge is how to successfully forecast future market demands.

Example: Data Driven Decisions for Manufacturing Company

Imagine a small cap manufacturing company, standing at the crossroads of this common data dilemma. The management team, in pursuit of robust benchmarking and strategic growth insights, wants to both get a better grasp of their internal data to improve processes, while including 3rd party data to understand how the company performs in relation to market peers.

To bridge the gap between raw data and graspable insights, the introduction of STOIX Metrics emerges as a possible solution. Designed to streamline the process of data structuring, making it possible for management to point at data sources and connect them to the visualization tool.

STOIX Metrics is a plug-and-play metrics tool for a variety of common data sources, where customer data from the CRM system can be viewed together with financial data and order data from other systems. By also connecting STOIX to the 3rd party datasets acquired through UnionAll DataHub, internal metrics can be benchmarked and compared against market data.

Not only does the company have a better understanding of their actual performance, and through a fundamental understanding of their development over time, but they can also correctly benchmark against their market competitors and properly analyze the over- and underperformance to better understand where opportunities for improvement lie.

3rd party data for deeper insights

Implementing the UnionAll Data Marketplace, a transformative force in the data ecosystem, to search for and access relevant third party datasets - opens a new possibility for market forecasting. Our centralised platform, containing a diverse variation of 3rd party data products, presents an opportunity for businesses to overcome the challenges of benchmarking - and tap into valuable third-party insights.

The combination of better oversight and new data sources presenting a stronger benchmarking opportunity, and forecasting makes better decision making more accessible for management. Intertwining UnionAll and STOIX Metrics helps the imagined small cap manufacturing company get easy access to important operational data and relevant company KPIs, while also incorporating peer benchmarking and forecasting.

Turning Data into Revenue Streams: A Comprehensive Overview of Data Monetization

What is Data Monetization?

Modern organisations are increasingly recognizing the economic potential in their data - in the age of digital transformation, data is widely stated to be an organisation’s most valuable internal assets. Data monetization however, involves the strategic process of transforming internal data into new revenue generation, and most often not included in the general valuation of internal data.

By exploring how internal data can be packaged into sellable data products and making it available to external users, a whole new sphere of economic value and new insights can be generated.

This overview will dig deeper into the multifaceted area of data monetization, exploring its types, ethical considerations, steps, pricing strategies, and frameworks, with a focus on enabling organisations to turn their data into new revenue streams.

Data Driven Organizations Utilising Internal Data

Internal data can be strategically utilised to enhance internal operations, drive efficiencies, and contribute to revenue generation. Following are three types of internal data that organisations commonly possess and leverage to various benefits:

Operational Data: Data generated from day-to-day business activities. Examples include sales transactions, production metrics, and inventory levels. Analysing operational data can provide insights into process efficiency, identify bottlenecks, and streamline workflows.

Customer Data: Information about customer interactions, preferences, and behaviours is crucial for businesses. Internal customer data can be used to personalise offerings, improve customer experience, and implement targeted marketing strategies.

Supply Chain Data: For businesses involved in production and distribution, internal data on the supply chain is vital. This includes information on suppliers, logistics, and inventory management, which can be optimised for cost-efficiency.

Sensitivity in Data Monetization

Most types of internally gathered data can be used to generate operational efficiencies and benefits. However, it can also be of great value to external organisations aiming to gather insights into new business areas. Though, before delving into making internal data available to outside organisations, there are several factors to explore.

Individuals & PII classified data: User data can be of great value for external companies seeking insights into consumer behaviour. To balance business objectives with privacy, however, organisations can responsibly monetize data through anonymization and aggregation.

When monetizing user data, stringent privacy policies and user consent needs to be taken carefully into account, and expert advice is recommended before considering making user data available for monetization.

By removing personally identifiable information and consulting trusted third-party experts, businesses can extract valuable insights without compromising user privacy. This approach not only ensures compliance with regulations but also strengthens trust between companies and their user base, creating a win-win scenario for both parties.

Businesses: As written above, organisations generate substantial data during operations, encompassing transaction records, customer interactions, and various internal processes. This data is much less sensitive in terms of privacy issues, but can be risky to make available to the wrong buyer, should there for instance be a risk of making competitive insights available to direct competitors. It is therefore strongly recommended to carefully develop the data products and define who the data should be made available for.

Example of Industries Already Putting Data Monetization to Work

Financial Services Industry:

Financial services companies serve as typical examples of how to successfully generate revenue through data monetization. Credit card issuers and banks can strategically employ customer transaction data to refine cross-selling strategies. Partnerships with merchants can amplify revenue streams through data-driven reward programs.

Telecommunications Industry:

Telecommunications companies leverage data monetization by analysing customer usage patterns, preferences, and network performance. They can sell anonymized and aggregated data to advertisers, providing insights into consumer behaviour and enabling targeted advertising. Additionally, telecom companies can offer location-based services to third-party businesses, such as retail stores or advertisers, based on the geospatial data collected from mobile devices.

Healthcare Sector:

In the healthcare industry, organisations can monetize data through various means. Pharmaceutical companies can use patient data to identify trends, optimise clinical trials, and personalise drug development. Health insurance providers can utilise patient health records to create personalised wellness programs and offer insights to employers for employee well-being initiatives. Moreover, healthcare data analytics companies can aggregate and anonymize healthcare data to sell valuable insights to researchers, pharmaceutical companies, and other stakeholders.

How Data Monetization can be Streamlined with Automation

The ease of creating valuable data products and making these available to third parties through public data marketplaces has increased vastly over the last few years. Here, UnionAll has taken vast steps into automating the process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products.

The market for external data is continuously growing, and we are seeing a strong increase in demand for new and still unexplored sectors for data monetization. The need to enrich internal company data with external insights, or utilise third party data for training AI-models is taking off with great velocity in the coming years. This opens up great opportunities for companies seeking to increase revenues and explore the realm of data monetization.

How to Monetize Your Data

Monetizing internal company data can be a long and resource intensive process, including the below described step:

Identify the Data

Find the data that can be monetized. This can be both internal data such as customer, network, or operations data.

Analyze the data

Assess the quality, which can be done automatically by applying AI, and determine the data asset’s sellability by analysing what insights that could be drawn from the data.

Define the Value Proposition

Identify potential customers or partners that could benefit from the data and decide on the business model and pricing strategy. Consider potential regulatory limitations as mentioned above.

Develop the Data Product

Source, clean, transform, encrypt, enrich, document, and publish the data asset on the desired platforms.

Market the Data Product

Market the data through relevant platforms and apply search engine optimization (SEO), or leverage existing business networks to reach potential buyers.

Making the Data Available on Data Marketplaces

Data marketplaces serve as dynamic platforms where organisations can publish, share, and extract value from their data. These marketplaces act as intermediaries, connecting data providers with potential buyers, fostering an ecosystem for data exchange. Understanding the dynamics of data marketplaces is important for organisations seeking to capitalise on data assets.

Data Product Go to Market

Pricing data for external buyers involves a thoughtful approach to ensure fairness, attractiveness, and alignment with the value provided. Here are some strategies and considerations for pricing data in the context of data monetization.

Analysing current supply of similar products and building an understanding of existing demand is important for establishing the initial price for the data products. Factors such as data freshness, granularity and size of the datasets compared to existing products on the market will also play a significant part in pricing the data.

How UnionAll is Automating the Process of Monetizing Data

UnionAll has taken vast steps into automating the above described process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products. This process includes automated discovery, packaging and publishing. Based on vast insight into existing data on public marketplaces, as well as current demand for new data products, UnionAll can tailor products and price, as well as marketing campaigns and relevant content to get maximum throughput on value for published data products.