A net zero data strategy can be a powerful tool for any organisation. By strengthening data infrastructure and governance, improving data literacy and shifting leadership culture towards a better strategic application of data, businesses can help the move towards achieving net zero goals.
Climate change is one of the greatest challenges of our time. It’s not only impacting the ecological balance of the planet, but society and the economy too. In the first blogpost of this series, we discussed the value of aligning data strategies with net zero strategies and made the case that businesses fundamentally need actionable insights informed through real evidence and suitably granular data in order to drive low-carbon growth.
In this blogpost, we’ll look at how we can leverage the right data, at the necessary granularity, speed and scale to avoid further climate catastrophe through the built environment. We’ll explore five key components of a powerful data strategy: strong data infrastructure, robust data governance, evolving skills and data literacy, data ethics, and the required cultural shift.
Building and maintaining strong data infrastructure lays the foundation for better embedding of net zero data strategies, and encompasses the effective management of data assets, standards, technologies, guidance and policies.
Data assets and technologies form a fundamental part of data infrastructure and can be used to measure, monitor and evaluate data in ways that make it easier to chart progress towards net zero. Corporate sustainability data is more frequently being collected using new collection methods, including surveys, web scraping, and accessing publicly available data portals. Increasingly sophisticated analysis and insight is being achieved through machine learning techniques such as natural language processing. Open Climate Fix publishes open energy data and provides open solar forecasting software to organisations like National Grid as part of its data strategy.
Data assets also include registers and identifiers. The Legal Entity Identifier (LEI) is an alpha-numeric code based on a standard developed by the International Organization for Standardization (ISO) and its end users access the LEI Repository via an open data licence. A project by OS-Climate incorporates LEI datasets made available in the Amazon Sustainability Data Initiative which aims to drive development of climate-aligned financial applications. Climate-related datasets stored in the cloud using LEIs help to minimise the cost and time required to acquire and analyse these often inaccessible datasets.
Data standards and policies can be beneficial aspects of data infrastructure if employed widely, as they can help the adoption of shared vocabulary in describing common attributes, definitions and models of data. Open standards for data can enable a better exchange of data within and between organisations using common formats and shared rules. Standards can also provide guidance and recommendations for sharing better quality data and understanding processes and the flow of information. Agreeing to adopt data standards helps to steward data in a more consistent way, and there are some clear examples of this being used to address climate change.
The Open Contracting Partnership has developed the Open Contracting Data Standard (OCDS), which specifies how to represent each stage of public procurement processes as structured data, which makes it easier for those in the procurement process to trace potential environmental impacts relating to the delivery of goods, works or services. The Follow the Water Project used the OCDS to evaluate the cost-effectiveness of flood management in Taiwan, helping investigate whether contractors applied the optimal construction methods to manage water resources and control floods.
Organisations such as Microsoft are at the forefront of developing and maintaining data infrastructure as part of their net zero strategies, such as the Planetary Computer Data Catalog, which includes huge amounts of environmental monitoring data, in consistent, analysis-ready formats.
Organisations like Open Net Zero and Stream are data institutions that are stewarding data on behalf of other organisations and communities in order to deliver benefits to the environment. These net zero data institutions represent the closest alignment of data and net zero strategies, and although most companies do not need this level of agreement, they can inspire less data-centric or environmentally-focused organisations.
For more on designing strong data infrastructure, read our seven principles for strengthening our data infrastructure.
Data governance is the exercise of authority, control and decision making over the management of data assets. Where senior leaders are able to exercise authority and create organisational alignment between net zero goals and the strategic management of data, they can set the tone for robust data governance in implementing net zero data strategies. At board level, there needs to be overall leadership in recognising data as a valuable infrastructure for both the business and their net zero commitments. At senior leadership level, they must set the direction of net zero in alignment to business objectives and remain accountable for data governance in their corporate area. At delivery level, there should be knowledge about specific data in their domain with an understanding of the quality, risks and opportunities to utilise such data.
The Standard for Environmental Risk and Insurance (SERI) Governance Framework proposes that creating a secured shared data governance framework that brings all valuable insurance related data together with the support from industrial partners and advisory groups in the insurance value chain can unlock the insurance industry’s potential to play a critical role in the transition to net zero. The governance framework would facilitate data sharing to encourage collaboration and innovation towards industry-wide net zero goals, through promotion of new incentives and levers of change in automated reporting, validation and more. Successful net zero strategies will be underpinned by strong data governance explicitly connected to net zero goals.
Data strategies need to have data ethics embedded within them to ensure net zero strategies do not replicate or exacerbate existing biases and provide not just data justice, but climate justice. Operationalising data ethics is a necessary consideration in implementing net zero data strategies. This means balancing the need to generate, extract and derive value from data with potential consequences of doing so. Ethical risks identified in the use of data and data-driven technologies are not exclusive to the net zero context but deserve additional caution because of the huge scale of deployment.
Energy Systems Catapult addresses data ethics and bias in their work on Smart Local Energy Systems (SLES), which bring together different energy assets in a local area, such as solar farms to power housing developments, and make them operate in a smarter way. SLES can help a local area to decarbonise more effectively, delivering wider social and economic value for communities too, but they report that increased uptake of smart technologies requires safeguarding to ensure responsible and ethical use of personal data. They propose a set of principles to prevent structural data bias from entering the data that underpins these new energy products and services
A net zero data strategy means providing data literacy and net zero literacy training with the skills and learning function of an organisation. A lack of data skills, engagement and data literacy is a clear limitation for successful implementation of any data strategy. Data literacy is relevant in every industry. More than simply technical skills, data literacy is the ability to think critically about data in different contexts and examine the impact of different approaches when collecting, using and sharing data and information. This includes the ability to critique data collection, use and sharing – an ability which can be seen as a business asset in its own right.
The Carbon Literacy Project offers carbon literacy training and advice on climate change, carbon footprints, and how people and businesses can play a part in achieving net zero emissions. They describe Carbon Literacy as the knowledge and capacity required to create a positive shift in how we live, work and behave in response to climate change, and is an example of the new competencies required in the implementation of data-centred net zero strategies. Organisations that have a Carbon Literate workforce can be accredited by The Project as a ‘Carbon Literate Organisation’.
Alongside technical tools, we need ways of defining and explaining social norms and expectations about contribution and value sharing. Embedding a data culture within an organisation can improve its net zero strategy by providing a foundation for evidence-based decision-making and continuous improvement. A data culture is characterised by a strong focus on data and analytics, thought leadership, as well as a commitment to using data and information to inform and drive business decisions.
Embedding a data culture is most effective and impactful when there is a compelling corporate vision for how data drives business value aligned with overall strategic objectives. The vision should motivate employees to use data in their work, should empower stakeholders in their understanding of the business and should help leadership in evidence-based decision making. By embedding this culture within an organisation, employees will be encouraged to use data and analytics to identify opportunities for reducing greenhouse gas emissions, increasing energy efficiency, and transitioning to renewable energy sources. This can help the organisation develop and implement a more effective and sustainable net zero strategy, and ultimately contribute to the fight against climate change.
We want to hear your thoughts and ideas! Feedback on this blogpost will help inform our final report on ‘Net Zero Data Strategies for the Built Environment’ launching in the new year.
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