Friday, July 19, 2024

How UK businesses can keep on top of sustainability expectations with data-driven insights and AI

Jason Janicke, SVP, Sales, EMEA at Alteryx

The clear message from last December’s COP28 conference was that climate action can’t wait. Nations across the world are working to accelerate carbon-cutting plans, including the UK which has outlined its ambitions to reduce emissions by 68% by 2023 compared to 1990 levels.

To meet this target, businesses are under increased pressure to monitor their energy consumption and develop a plan to limit and reduce future carbon footprints. This will only become more important given the UK government’s aim to introduce climate disclosure standards in July for listed companies, based on those set by the International Sustainability Standards Board (ISSB). These standards envisage the disclosure by businesses of material information about all sustainability-related and climate-related risks and opportunities they are exposed to. Even for companies that aren’t listed, pressure for similar disclosure will only grow as carbon-cutting efforts remain front of mind for stakeholders.

To reckon with these new requirements, data-driven technologies, especially Artificial Intelligence (AI), are critical to keeping on top of the sheer amount of data related to energy usage and automating its analysis for insights. This encompasses sources like smart meters, historical consumption, external usage records, pricing information, consumption forecasts and more.

Consolidating all this data into a cohesive data pipeline, however, is a massive challenge. The data is sprawling and decentralised by nature. It comes in varying formats and poses integration complexities that hinder seamless processing.

But these complexities aren’t reasons to opt out of building a data pipeline. Doing so is urgently needed for gaining customer and business insights but, more than that, so businesses can reap the benefits of running more environmentally efficient operations. But, where to begin?

Starting the journey to an environmental impact data lake

Forward-thinking companies are learning the value of getting data into the hands of more people. When getting started with sharing and collating data from disparate sources and diverse departments, relationship building is key to understand how data can drive business value.

Conversations with data owners across the business provides context to the problem they are solving with analytics, as well as making clear exactly what data is required from each stakeholder. It’s also helpful to be clear that the trajectory of building a data pipeline is towards automated workflows. With access to automated analytics, data workers can answer bigger questions, seek out more transformative business outcomes, and ease the burden of those time-consuming, manual tasks that eat up precious minutes every day.

Automating the data pipeline processes can also help avoid errors and ensure the data that drives key business decisions can be trusted to be complete, accurate, and valid. When the data is clean, consolidated and consistent, it can be used to train AI models to identify energy usage trends and waste anomalies as well as forecast consumption and costs. The powerful potential of this for better, more sustainable decision-making shouldn’t be underplayed. These four real-world areas show where businesses can use AI to find ways to cut their energy consumption and dramatically reduce their energy costs.

AI and data analytics: true allies of energy efficiency

Some of the most effective applications of AI to optimise energy consumption and reduce costs include:

  1. Managing energy use in buildings and factories

By using AI to consolidate electricity bills and analyse energy consumption, organisations can identify which aspects of their operations take up the biggest share of total consumption, as well as consumption peaks. This information can be used to inform decisions that improve operational efficiency.

  1. More control over grid management

If a company’s energy supply includes renewable sources, occasional unpredictability needs to be accounted for. With the right training data, AI applications can spin up very effective predictions to help optimise energy storage and, therefore, limit the risk of shortages.

  1. Smarter transport management

AI’s ability to consolidate and analyse data from disparate points makes it well suited to smarter transport management. Applications can automate the verification of drivers’ compliance with legal guidelines, optimise transport routes in real-time, schedule maintenance to preserve fuel economy and more.

  1. A more efficient supply chain

Global demand continues to grow for environmentally friendly logistics, with 75% of shippers actively seeking greener options when exporting goods, according to the World Bank. Every supply chain is susceptible to inefficiency given the complex nature of cumulative links. AI intervenes to reduce friction and maintain good coordination between carriers, suppliers and customers through effective predictive capabilities and automated decision-making.

For example, AI can optimise supply chains through functions like predicting product shortages and price fluctuations. It can reveal scheduling issues and enable better management of a smart warehouse while reducing costs. 

Ushering in real sustainability informed decision making

Environmental sustainability is a priority for more boardrooms than ever. But far fewer of these boardrooms are actually factoring information about their business’ energy usage into decision-making. This equates to flying blind.

Organisations that haven’t made a start to centralising data sources related to their energy efficiency should do so quickly. Once this happens, business stakeholders can apply AI to effectively address current and future energy challenges in a data-driven way.

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