Sustainability is a key focus for today’s organisations, and with consumers’ purchase decisions increasingly based on ‘green’ credentials, it can be a critical element in remaining competitive. Businesses are starting to improve their sustainable practices by addressing the products and services they provide, the processes they use, the waste they generate as a by-product, and the supply chain that facilitates their operations. But while 90% of executives believe that sustainability is essential, only 60% of organisations have sustainability strategies in place.
In the data-driven world, companies have a wide range of effective tools at their disposal that can turn data into value to accelerate the implementation of a sustainability strategy. They can collect and examine data on a wide range of sustainability-related issues – from energy use to carbon emissions – to reveal key insights that drive initiatives. In addition to enabling green capabilities, analysis shows that, on average, every dollar invested in data results in $32 in economic benefit. In other words, ensuring that data is accurate and reliable is essential for organisations.
Focusing on creating, maintaining and securing high-quality data is key, but equally important is ensuring that this data is accessible for the analysis that enables data-driven decision making. Consequently, it is crucial to adopt strong data governance – a set of processes and policies that can be implemented to ensure data is reliable and trustworthy.
Defining sustainability goals: a challenging road for companies
In 2015, the United Nations presented its 17 Sustainable Development Goals (SDGs) as the blueprint to achieve a better and more sustainable future for all; it expects companies to have established sustainability strategies and implementations in response to them by 2030.
SDG-oriented business models have the potential to create significant market opportunities. In 2019, McKinsey estimated that global sustainable investment had exceeded $30 trillion, a tenfold increase since 2004.
Failure to define SDG goals and apply them to their business model puts companies at risk in three core areas:
1. Financial Companies can face enormous costs due to environmental risks that affect their supply chain. For example, Unilever estimated an annual loss of €300 million due to climate change endangering agricultural productivity; the company is currently working on a pilot project (using SAP’s GreenToken supply chain transparency technology) to further increase traceability and transparency of its global palm oil supply chain.
2. Legal, compliance and risk management: Different countries have different regulations, which may lead to confusion and even risk. In the UK, various laws and frameworks require organisations to be transparent in areas such as diversity, equal pay, carbon emissions and modern slavery. The Competition and Markets Authority (CMA) guidelines, released in January 2021, helps businesses understand the rules that apply to their operations and how to achieve sustainability goals without breaching competition law.
3. Customer trust: Today’s consumers are actively choosing brands based on their ethical behaviour and their initiatives linked to sustainability and climate change – although 48% of UK adults say they do not trust the information companies provide around sustainable products, indicating a risk factor for the relevant companies. In addition, 36% of people in the UK believe further regulation to make companies improve sustainable lifestyle choices for consumers should be introduced.
However, firms are facing challenges in defining and implementing their sustainability goals. One recurring obstacle is ensuring that the adoption of a sustainable strategy will not impact their profitability. Businesses need a quick return on investment, and a company must be profitable to be sustainable. At the same time, the work of measuring ESG scores may prevent some executives from fully investing in sustainability initiatives, as 63% of CEOs struggle to measure ESG across the value chain, representing a barrier to sustainability in their industry.
Companies need to identify relevant KPIs to create valuable sustainable insights. By measuring these KPIs, companies will have opportunities to achieve their ESG goals, such as carbon footprint reduction, energy consumption, waste and pollution tracking (i.e. within the supply chain), and social impact. But to accurately measure these KPIs, organisations must be able to rely on trusted data to create tangible results, and accessibility to the relevant data can be hard to gain. For example, if greenhouse gas (GHG) emissions reduction is identified as a KPI, sustainability teams will need to access hard-to-get financial data, such as travel mileage, and combine it with human resources data to calculate the GHG emissions of individual employees.
Sustainability strategies and goals are crucial for companies and if reliable data isn’t available and accessible, their societal, environmental and legal requirements won’t be met. Companies cannot implement sustainability strategies without data governance that offers transparent and valuable data for better data-driven decisions.
Data governance: what it is and why every company needs it
Data governance is the approach companies take to set standards and policies on how data is ingested, processed and used in a way that makes it secure, available, accurate and usable. It includes aligning the people, processes and technologies needed to support those standards. Putting a data governance policy in place provides businesses with a formal strategy with which to access, monitor and use data to support employees and business units. It highlights data’s role as a valuable asset that is essential to respond to strategic needs and enable data-driven decision-making, resulting in the following benefits:
Implementing a data governance strategy
There is no such thing as a one-size-fits-all approach to data governance. Each strategy is unique to the organisation it serves and requires a different solution. To best define the optimum data governance for the company in question, a framework should be followed; based on its long experience with clients, Artefact proposes the following approach:
1. Vision and business requirements: Define the company’s business priorities and objectives, as well as its vision for data strategy in the short, medium and long term.
2. Data infrastructure: Identify where the organisation’s data currently sits, whether the infrastructure is designed to facilitate operations, and whether data is constantly updated.
3. Current data governance: Establish whether the company’s data is supervised, stored securely and easily accessible, and if it is uniform across the organisation.
4. Data applications: Define what is required to achieve the organisation’s vision. Solid application development processes are essential (development, prototyping, industrialisation), and all applications need to be linked to business objectives and add value across the company.
5. Monitoring and evaluation: Ensure continuous checking of the data objectives through clear KPIs and targets.
6. People and processes: Put the right processes in place to implement the data strategy.
7. Tools and capabilities: Ensure that the right tools are used, and up to date, to facilitate data processes and enable the required changes.
Organisations need to set up a data governance programme, which should involve structuring data governance assets (definition of the operating model, tooling and roadmap of the data governance initiative) and the deployment of data governance within each domain (data quality, standardisation and accessibility).
Once companies have set up solid data governance and defined their sustainability goals, the next step is to identify how to leverage the first to achieve the second.
Using data governance to achieve sustainability goals
Advanced technologies can use data to uncover deep insights, opening a world of innovative ways to support sustainable practices across the enterprise. Artefact has worked with several organisations to build data/AI products and strategies, all of which are based on strong data governance, that integrate with business processes to tackle energy and environmental issues.