Looking back at 2021 - Clean Technology Meets Data Science

As we enter 2022, let’s take a look back at what happened in the clean technology and data science space during the last year. It was certainly an interesting year from a technology perspective - and not just because the pandemic upended industries all over the world! 

Clean technology saw some remarkable growth during the past year. We saw the explosion in the market share of electric vehicles (EVs), significant increases in renewable energy production and use around the world, the growth of wastewater surveillance and startups and utilities focusing on water management tools, and finally with COP26, we saw climate and sustainability come to the forefront with major players asking for better monitoring and decision making tools. 

As clean technology sectors grew, we also saw data science tools and technologies being deployed more frequently - not just by startups and other innovators, but also by larger companies and organizations. And data science was being used not just to develop a cool new tool or to make pretty pictures, but to solve some of the biggest challenges facing sectors ranging from energy to agriculture to water to sustainability. 

So, let's take a look at some of these challenges!

In many countries, an existing challenge that the pandemic brought to the fore was the labour crisis. In many clean tech sectors, this was an existing problem that was exacerbated by lockdowns, wage issues and worker safety. As an example, in the water sector, an aging workforce meant that companies were trying to attract younger workers and figure out ways to increase automation. Or in other sectors, fewer people were available to do highly manual, physically tough work - like harvesting and processing food in agriculture for example. 

This meant that there was a lot of focus on 1) automation including smart sensors and robots, 2) ways to remotely monitor systems by using tools like predictive analytics for maintenance in water treatment plants, and 3) improving the efficiency of the existing system by accessing and aggregating resources - a prime example being startup Conservis’ app to aggregate all data on the farm in a single platform as opposed to having to use multiple apps for farm data, satellite data, fertilization data, machinery data, price data and so on. 

So, 2021 saw autonomous tractors being introduced from John Deere as well as Monarch’s fully electric version, a total of 557 underwater robot and drone startups, and a whole suite of data aggregation and data visualization products in different clean clean technology sectors. 

The second area where we saw data science in different clean technology sectors was in building products that provided useful information to the end-user - whether that’s a farmer managing fertilizer applications on crop fields, a worker monitoring the state of transmission lines, or a building owner evaluating the ROI on energy efficiency upgrades and on-site solar panels. There was greater focus on building what are being called "simple apps" - the simplicity coming from the fact that these products focused on a specific problem in a single sector and developing a solution for that problem. The data and the algorithms that these products use are relatively straightforward - their value lies in the ability to combine knowledge of the clean tech sector, the problem and build a straightforward solution that works for the customer. An example of a startup in this space that gained significant traction in 2021 is Rheaply which focuses on identifying and reusing materials and resources within companies - thus combining sustainability, minimizing waste and improving the bottom line for companies.

And finally, there’s the area where we expect to see significant interest in 2022 and in the years ahead! We expect to see a lot more products being developed that combine expertise in the sector and subject matter with novel data sources and new algorithms. 

Since we're still in Covid times, a classic example is the development of tool that helps predict future pandemics by combining existing bat physiology models with spatial statistics models and machine learning algorithms like random forests. That's something that is currently being studied in research labs around the world, but could be deployed at scale in a few years. Or what’s currently happening in wastewater surveillance startups like Biobot where viral fragments in wastewater are monitored to identify Covid-19 outbreaks in areas - data that are then used to help scale up hospital staffing and equipment needs.

The challenge in all this is that building anything useful in the sector needs expertise in both the clean technology domain and in creating and coding the algorithms that provide the necessary results! A common refrain we’ve heard from experts and long-term professionals in the field is -“the technology and applications are there - we just need the people who can build and use them”.  

Some colleges and universities began creating programs in 2021 in clean technology fields with specializations in data science for enrolled students. In the Agtech sector, the University of Georgia's Certificate for enrolled degree seeking graduate students, North Carolina State University's Certificate for undergraduates in agriculture, Purdue University's Online Certificate Program were some of the programs that began last year. In Civil and Environmental Engineering programs, which graduate many of the professionals in water, energy, air, transportation and urban sustainability, specialized tracks and certificates were created at Carnegie Mellon University , University of Washington, and University of Illinois at Urbana-Champaign. 

Additionally, scientific conferences like American Geophysical Union’s Fall Meeting and California’s Open Water Data conference had tutorial sessions during the conferences where machine learning, data science and data engineering where taught. In the conservation sector, non-profit organizations like Wildlabs.net also organized hackathons and data tutorials.

However, there were few options in 2021 for professionals looking to upgrade their skills with data, models and machine learning algorithms that were directly applicable to their sector. 

And that’s where we at Ecoformatics come in! Starting in February 2022, we’re launching specialized tracks for each clean technology sector where we focus on helping professionals working in these sectors go from “I’ve never coded, I hate statistics and I’m not sure how useful machine learning and AI are to my problems” to “I can now build useful machine learning models, I understand when and how to apply data science, and I can successfully build and manage a team that can use these new technologies in my company and sector”. 

Check out our courses and sign up for our free plan here!

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

Our online community space is now live!

How much water should an email consume? Data centers and water use