From Traditional Methods to Semi-Autonomous Systems
In the world of natural asset management (NAM), the need for effective monitoring has never been more critical. As our understanding of ecological systems grows and the infrastructure challenges we face become increasingly complex, the tools we use to track how our natural assets are being managed must evolve accordingly
Let’s explore the progression from traditional monitoring techniques to the innovative, semi-autonomous systems NAI is developing.
But first: what do we mean by ‘monitoring’?
For natural asset management to be a real solution, we need evidence showing that NAM interventions did in fact make progress towards the intended goals. Monitoring refers to the act of tracking and analyzing NAM initiatives —ideally, results will show where progress is being made, where it is still needed, and overtime, will help us ensure that NAM is as rigorous and effective as it can be.
Traditional Monitoring: A Foundation Built on Interviews
In our first few years as an organization, NAI relied on traditional monitoring methods using a monitoring framework developed by Dr. Michael Drescher at the University of Waterloo. This manual system worked primarily through structured interviews, collecting qualitative data from stakeholders, project leads, and field experts. This method fostered engagement with local governments, and produced valuable insights which are captured in NAI’s two monitoring reports.
It also came with its own set of challenges:
- Time-Consuming: Conducting interviews, transcribing results, and analyzing the qualitative data could take weeks, if not months, especially as the number of projects grew.
- Scalability Issues: As NAM initiatives expanded, it became increasingly difficult to keep up with the pace of change. The static nature of interviews meant that insights could quickly become outdated.
- Limited Scope: Interviews often provided a narrow perspective, focusing on specific projects or initiatives without capturing the broader trends and dynamics in the field.
The Need for Change? Rising Complexity and Demand
As the field of NAM has evolved, along with the sheer number of organizations taking action, so too have the complexities associated with keeping track of who’s doing what, what’s working and what’s not, in a timely fashion. The “old school” monitoring reports are, in effect, out of date the minute they are published. This challenge will only get worse as NAM becomes more widely adopted.
Why does this matter? Simply put, data on organizations’ experience with NAM is the lifeblood of the field. Data and products such as case examples are what encourages others to take action, provides the basis for guidance and standards, curriculums, areas for further research, and just about everything else.
Recognizing these limitations, our organization embarked on a journey to enhance our monitoring capabilities. We sought a system that could keep pace with the evolving landscape of NAM, allowing us to draw on a wider array of data sources while also improving efficiency and accuracy.
Enter the Semi-Autonomous Monitoring System
With support from RBC Foundation through RBC Tech for Nature and the Province of British Columbia through the Ministry of Municipal Affairs, NAI is developing a solution that combines technologies such as web-crawling, web-scraping, and natural language processing (NLP). This semi-autonomous system is designed to complement our existing methods while addressing their shortcomings. Here’s how it works:
- Web-Crawling and Web-Scraping: By leveraging these technologies, we can gather vast amounts of real-time data from various online sources, including research articles, policy documents, and community forums. This allows us to stay informed about the latest developments and trends in NAM without the need for extensive manual input.
- Natural Language Processing: NLP enables our system to analyze and interpret the gathered data, extracting key insights and identifying patterns. This means we can convert unstructured data into actionable information, streamlining our decision-making processes.
- Real-Time Monitoring: With automated data collection and analysis, we can respond more rapidly to changes in the environment or policy landscape. This agility is crucial in a field where conditions can shift dramatically and unpredictably.
- Enhanced Collaboration: By freeing up time previously spent on manual monitoring, our team can focus on more strategic tasks, such as engaging with stakeholders and rightsholders to develop innovative solutions for the challenges we face.
A New Era of Natural Asset Management
As we implement this semi-autonomous monitoring system, we anticipate significant improvements in how we track and manage our natural assets. The ability to access and study real-time data will empower us to make more informed decisions, anticipate challenges, and seize opportunities.
Moreover, this evolution represents a broader trend in the field of NAM. Organizations worldwide are increasingly turning to technology to enhance their monitoring and management practices. By embracing these advancements, we can not only improve our own processes but also contribute to a more sustainable and resilient future.