Green is the company's new favorite color. Most would like to work and produce things more sustainably which is why sustainability is one of the top priorities for 2023. But it's not so easy to guide and accelerate the complex process towards greater sustainability. One thing is clear: future-proof sustainability management requires a new way to handle data.
With current, managed, and interlinked data, companies can create transparency – this allows the detection of patterns and deviations at an early stage and the automation of recommended procedures as well as predictions and actions. Using this as a basis, companies can take the final step from regressive sustainability reporting to forward-looking sustainability management. Unfortunately, many companies don't know what information they need to collate, where they can find it, and how they can merge the data in the most efficient way. Consistently thought through to the end, it begs the question of how the increasingly large data quantity can be tackled in the most automated way.
Sometimes we can see things better from a distance: Satellites have been collecting data about our Earth in space for decades, giving us global and continuous environmental monitoring. Based on this information, we recognize how ecosystems change, where forests are dying off, or oceans are drowning in plastic waste. A consortium led by T-Systems is now providing the European Space Agency's Copernicus program access to this digital Earth monitoring data. This involves several petabytes of information. So that you can get an idea of the size: the information contained within all academic research libraries in the USA equates to two petabytes. This quantity of data is of course too big for us humans to evaluate. We can only get a handle on this with digitalization and automation – and this doesn't just apply to data from space.
At the moment it looks like we – my generation – will not be able to leave the planet to you in the best condition ... I really hope that we can still get our act together and improve a few things for you. I would also hope that we will not be remembered by you as the generation who have egotistically and recklessly destroyed your livelihoods ... This fragile spaceship we call Earth is much smaller than most people can imagine."
Alexander Gerst, geophysicist and astronaut in his “Message to my grandchildren”
Sustainability has developed into a strategic transformation and innovation driver for companies. Your employees, your clients, and investors expect it from you. Besides, you can't escape the statutory regulations. The EU's Green Deal intensified the climate goals for 2030 and 2050; the German Supply Chain Act obligates companies to have a transparent supply chain so that they can quickly react to infringements. Where are we now? This is the question at the start of every improvement measure. Many companies often don't know what they should focus on to be more sustainable and to protect the environment. They can't determine their current sustainability status because they are not in a position to monitor and check their end-to-end value and supply chain. A transparent data supply chain, from the supplier, through one's own company to the client is the prerequisite, for example, to meet increasingly demanding reporting obligations and to gain speedy agency.
The quantity of relevant data which can help companies ensure greater sustainability is phenomenal and the information within the company is often distributed across many silos. We humans have no chance of gaining an overview without the suitable tools. However, digital intelligence quickly and automatically establishes transparency. This is crucial, as we can only improve things that we have already measured. Three points are important for a company's data strategy:
In 2020, Gartner ranked hyperautomation as the most important strategic technology trend and included in this the intelligent and strategic end-to-end automation of complex business processes – such as the data supply chain here. So, this is not just about technology but also about a strategic approach. The companies combine various technologies, such as process mining, robotic process automation, or AI and machine learning – to quickly identify, check and automate business and IT processes. If you are interested in the question of why hyperautomation is crucial for your IT strategy, I recommend my previous blog contribution. Until now, hyperautomation has mainly been measured against its ability to make production more efficient. I find its contribution towards sustainability even more important. Without it, companies will not achieve their ambitious climate protection targets.
Deutsche Telekom and T-Systems are also pursuing ambitious plans: Since 2021, we have opted for 100 percent renewable energy and, for example, operate our data centers with green electricity. By 2025, we don't want to emit any CO2 at all (see scope 1 & 2). But as a company we are no island. We then only reduce our carbon footprint effectively if we include our supplier network and take account of how our services impact our customers. It is precisely the supplier network which gives each company a lot of leverage: this is where we can stipulate sustainability requirements of goods and services. But this only makes sense if we then monitor its adherence. This means that we need an end-to-end value chain of suppliers across our own company right down to the customer. The linchpin for this is collecting, processing, and then also visualizing this data.
With Excel & Co., etc., companies quickly reach their limits when it comes to sustainability reporting: climate change means we need speed, and this can only be done digitally. Our products and services aim to let our customers reduce their emissions. For this, their carbon footprint needs to be as small as possible. This means that we need to identify this for every product and every service. So we first of all collate the data across our entire value chain. This is done automatically. Our reporting includes data integration, in which we collate the data from various sources and present it in a dashboard. Collating, integrating, presenting – this is how we create the prerequisite for data analysis and provide important support to the decision-making teams in the company.
We have replicated our impact measurement process on our partner Pega's data platform, which measures the ecological and social impact of products, services, solutions, and projects. Pega is a specialist in hyperautomation; data value chains can be replicated on its platform using centralized software which prepares data and automates processes. But I also know that some companies have provisos for these kinds of platforms. It goes without saying that we will also find a way to process and evaluate data automatically. For example, we recommend combining different technologies like AI or robotics to structure data: robotics allows the monitoring of suppliers to see if they have adhered to their agreements; AI disciplines such as Computer Vision or natural speech processing can also embed any information which is not available in digital form – such as driving licenses.
According to a study by PwC, not all companies have made the subject of sustainability their top priority. But they should. Without a sustainable business model, companies risk their license to operate. Because policy will continue to tighten regulations the image can be right, left, or beyond the text or not existent. Analysts, investors as well as customers, and employees will look ever more closely. Technological obstacles can be overcome:
Companies now need to pick up the speed where sustainability is concerned and should take advantage of digitalization to help them. These findings are not rocket science. We can manage that easily on earth. What do you mean?