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In this article, we will present how to define sustainability goals and how to include them in data governance strategies.

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:

  • Better data quality: Accurate and reliable data provides companies with a tangible business asset. Using clean data brings business processes across the company into line with each other; this compatibility results in reliable performance measurement and dependable KPIs.

  • Cost and time savings: By applying greater data management discipline through better visibility and standardisation of processes, companies can redeploy 35% of their data spend. Moreover, reliable and accessible data saves time by reducing manual tasks.

  • Breaking down silos: Avoiding data duplication, outdated or incorrect information and silos (collections of data that are isolated across different business groups), reduces storage costs and, most importantly, increases operational efficiency. (Artefact’s experience shows that data scientists can spend more than 30% of their time on understanding and accessing data.)

  • Compliance: A data governance framework provides companies with data security and enables them to meet compliance regulations (such as GDPR) and stay on top of their legal obligations; data governance is designed to help companies operate more efficiently.

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.

  • Energy consumption reduction:

    Artefact supported a leading European telecommunications provider to address an environmental initiative to decommission copper across its network by 2030. The sustainability goals were to deploy a copper shutdown programme, minimise the energy consumption of copper while it was still being used, and quantify the risk levels of the project using AI. A tool was created to prioritise geographical areas for the work to be carried out and optimise costs for the initiative. As a result, the company estimated that it could save €1.15M in costs, 1.65 GWh in energy consumption, and 111 tonnes of eCO2 on average per year between 2025 and 2030.

  • Including sustainability use cases in data strategies:

    Companies are incorporating sustainability objectives within their data strategies, such as gathering energy and utility data from their facilities to work towards carbon emission reduction. For example, Artefact helped two leading property management companies in the United Arab Emirates (UAE) to understand their data and define clear sustainability objectives to achieve their goals in this area.The projects looked specifically at facility, utility and energy waste. One company partnered with third party providers to implement AI and smart technology across shopping malls to track air conditioning and electricity consumption and pinpoint how to reduce usage and save money. Additionally, dashboards to monitor and manage utility and energy waste were created, helping one of the companies with its main goal of identifying where solar panels could be installed to save costs in shopping malls and residential properties. Identifying sustainable objectives within their data strategies provided both companies with significant benefits; one forecasted 6% revenue growth over the next eight years.

  • Waste reduction

    French retail giant Carrefour had issues with stock availability/shortages and shrinkage in its bakery department due to a limited ability to predict consumption on any given day.  Artefact provided Carrefour with a forecasting model that generates daily predictions for each store and product line. Integrated into the current tool as enriched information, it provides each store manager with reliable predictions so that daily production of baked goods can be adjusted accordingly. The project let Carrefour reduce waste on fresh bakery by 12%.

    Additionally, Artefact developed a tool allowing Carrefour to measure and model the carbon emissions of its e-commerce sales in 2021, from click to delivery. It was able to measure 100% of emissions in four weeks and design a dashboard for simulation and monitoring.

  • Sustainability strategies need strong data governance

    Data is a vital lever for achieving sustainability goals, but it needs proactive management if organisations are to accurately measure their impact in this area.

    Structured data governance should therefore be an integral part of any sustainability strategy; once in place, companies will be able to lay the foundations for transparent and accurate decision making and derive real business value.

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