Data is growing at an exponential rate. According to Statista, the global amount of data has grown from 2 zetabytes in 2010 to 79 zetabytes in 2021, and is expected to hit 181 zetabytes by 2025. One should think that with so much data available, companies could easily track, report and reduce emissions along the value chain – and thus respond to the increasing pressure from investors and consumers, as well as stricter government regulations.
Yet, reality looks much different. Companies fear that sharing sustainability-related information might negatively affect their competitiveness. They also face the cost and complexity of multi-stakeholder interactions that are required to make accurate reporting happen. Organizations therefore mostly use estimates and averages for their reporting, instead of primary data from their suppliers.
This lack of primary data hinders efforts to effectively reduce emissions, which have further grown from 33.3 billion metric tons in 2010 to 36.4 billion metric tons in 2021. As long as entire industries work with estimates only, it is difficult to perform emission data analytics – for instance to spot the high- and low-emitting sources in value chains. It also becomes close to impossible to track and report any individual improvements. As a result, companies have little incentive to invest in emission-saving technologies, because they cannot turn that investment into a competitive advantage. For example, if you sell jeans and produce them in a sustainable way, the lack of primary data makes it hard for consumers to really compare your product's carbon footprint to those of other producers. Thus, communications come down to marketing, rather than actual investments.
But why is sustainability reporting based on accurate primary data still such a pain for most organizations?
- The first challenge is the inconsistency of methods and standards concerning the capture and measurement of emission data.
- The second challenge is the missing operability of technology solutions. So even if an industry agrees on a common measure, the exchange of data between companies remains difficult.
- The third and main challenge is that companies are very hesitant to share data. Historically, locking data into silos made a lot of sense and still is what most people perceive as normal today. This is based on legitimate privacy concerns, especially the fear that data sharing could lead to the exposure of sensitive business information. And while this established approach to data sharing seems logical, it’s quickly becoming outdated. With increased pressure from consumers and investors, it might even turn into a disadvantage and make companies fall behind when they are not able to create the required transparency.
Luckily, the third challenge can be addressed by the use of new technologies that enable secure data collaboration. One of those technologies is Secure Multi-Party Computation (SMPC), that allows two or more parties to share data and generate superior insights without revealing their private data inputs. For example, competing logistic companies can jointly optimize their routes to cut costs, improve margins, and – most importantly – track and reduce emissions. Companies with complex value-chains can digitize their supply chain, building deeper, more resilient co-operation models and generate win-wins such as in the Trado project by the University of Cambridge Institute for Sustainability Leadership (CISL) and companies such as Unilever.
This is why we are extremely excited to launch SINE’s very own SMPC engine Tandem.
Tandem is open-source software that everyone can use – free of any cost or limitations in terms of licensing whatsoever.
At SINE, we built Tandem to make sure that everyone can collaborate on primary data while keeping full control over its use. Tandem is open-source software that can be run for free on any platform – and does not require specialized hardware whatsoever.
It enables every firm to unlock the value of their data and to generate synergies, win-win scenarios and competitive advantages in the process. Like in the examples given above, our SMPC engine allows companies to jointly run operations on shared data. This very real kind of magic works through cryptography and mathematical obfuscation, which makes it impossible to guess or calculate the original input, while still generating the desired outputs.
Give it a try! To showcase how our SMPC engine works, we have built Encryptle, a privacy-preserving word-guessing game. To the best of our knowledge, this is the first time ever that anyone can make use of SMPC technology simply by browsing the world wide web.
Still don’t believe it? The code for our engine is open source and can be found here.
About us:
The SINE Foundation is a non-profit think and do tank. SINE solves data collaboration dilemmas by designing governance mechanisms and building open-source software to enable individuals, organizations and societies to generate synergies through their wealth of data. Among other projects, SINE works with the World Business Council of Sustainable Development, a CEO-led initiative of over 200 leading businesses and one of the co-authors of the Greenhouse Gas Protocol, on building the first network for emissions transparency as a public good.
Are you interested in learning more about how you and your company can leverage these new technologies and prepare for the future of data science?
- Reach out to us and learn how you can partner with SINE.
- Check out our Github repository for Tandem here.
- Follow us on LinkedIn.
- Read our White Paper on the Data Commons
- Watch our video address which was shown at COP27.