Will Big Data Save Our Water Resources?
Whether you consider it a buzzword or a panacea for the world’s problems, big data is a hot topic lately. Simply put, big data analytics combines massive computing power with the knowhow to collect, curate, integrate and analyze large swaths of complex information to make better-informed decisions.
In this new world, real-time mobile monitoring tools (including your cell phone) can be utilized for everything from making highways safer to categorizing the universe. For water managers, big data is showing big promise.
“With high powered computing and advanced information analytics, what were previously overly complex, demographic and environmental datasets can be defined, abstracted, or modelled and optimized.” said Geoff Briggs, business development executive with Smarter Cities and Water Initiative for IBM Canada.
“What we can better measure and assess, we can better understand and manage.”
Riggs and his colleagues at IBM are putting this theory to the test through a number of innovative watershed-based projects across the country. Out west, the company has partnered with the University of Calgary’s Advancing Canadian Wastewater Assets (ACWA) to understand the increasing pressures on watersheds.
In one flagship project, the ACWA group identified the need for improved distribution of precipitation gauging stations to aid in forecasting rivers flows. This led the team to begin developing a new water app to crowd-source precipitation data and engage citizen scientists in data collection.
“This approach parallels coarse and fine focus on a microscope. We can ‘focus’ on fine detail in key locations or on processes that warrant attention to detail, yet step back for a broad view when we need to evaluate large scale patterns,” wrote Professor Lee Jackson on his project blog.
“By partnering with the IBM Alberta Centre for Advanced Studies on sensor networks which provide data from remote sites, we have the opportunity to monitor water activity and quality remotely,” he said.
A Grand Idea
IBM is also a collaborator in a data integration project for watershed management led by the Southern Ontario Water Consortium (SOWC). The SOWC platform assimilates data collected every 15 minutes from more than 120 sensors across the Grand River watershed. The sensors capture meteorological, surface, subsurface and groundwater data, monitoring rain, snow, soil moisture, water turbidity, flow rates, temperature, and groundwater quality.
“The opportunities enabled by highly-instrumented, data-centric smart watersheds will not only improve our understanding of watershed management challenges, they will allow the development of new tools for monitoring and incorporating real-time data into decision-making,” said Brenda Lucas, Executive Director of SOWC.
Matching capacity with needs
At the watershed scale, better automation and efficient management of data has the potential to improve decision making for capital spending on infrastructure repair, water licenses; even prioritizing future research. The opportunities to make use of the platform are vast—that is, if useful information can be gleaned from it.
Therein lies the challenge in effective big data analytics. In addition to identifying the appropriate data, synchronizing data sources, and inventing a platform that will deliver sound results, the output must also be accessible and meet the end user’s needs. The only way to achieve the uptake is to know your customer.
WatrHub Inc., a small Toronto startup in the business of big municipal water data play, is an example of end-user driven big data analytics design. WatrHub was the brainchild of a couple of University of Waterloo engineers who met in Silicon Valley. Sunit Mohindroo and Ahmed Badruddin decided that they wanted to apply their skills in the sustainability sector, so they moved back to Toronto and formed the company in 2011.
“We started meeting with leaders in the water industry and they really opened our eyes to how big a challenge water is,” said Ahmed Badruddin, CEO of WatrHub during a MaRS entrepreneurs panel talk. “The challenge really comes when you start taking that challenge down to ‘what are the customer’s specific pain points,’ ‘what is the market need,’ ‘how do you deliver a great product.’”
“It’s not just about the information, but also how you present it,” said Mohindroo, WatrHub’s Chief Product Officer. “We’re taking a customer-centric approach to building the platform,” he said.
For WatrHub, the customer-centric approach led the company to pursue aggregation and analysis of existing data sets and government regulatory documents to deliver intelligence for municipal, industrial, and agriculture water sector clients. By capturing data that characterizes the challenges of a municipal water and wastewater system industrial tech companies are better able to develop new technologies that address the specific needs of utilities. Once the technology is implemented, that data then feeds back into R&D on how the company can continuously improve their product, and the benefits come full circle. Testament to this approach, WatrHub has been recognized and rewarded for its success by several groups including Water Canada’s Water’s Next 2013 Innovation award.
Other Canadian companies are capitalizing on the need for water data infrastructure ecosystems too. As described in The Droplet this week, AQUARIUS Informatics has earned a global reputation for innovation in environmental data management. AQUARIUS has been instrumental in aiding the federal Water Survey of Canada streamline their data management activities for over 2,400 monitoring stations and 250+ hydrologists and field technicians across Canada.
AQUARIUS’ work with over 400 clients–many of these municipal, provincial/state, or federal level water agencies–has won it numerous accolades, including the Environmental Business Journal® Business Achievement Award, Deloitte 2014 Technology Fast 500 company, and Rocket Builders Top 15 CleanTech Achiever (six times). WatrHub and AQUARIUS are some of the front runners, but the field of big data analytics is relatively new. There were surely be more startups in Canada following in their path.
Matching needs with capacity
In 2011, the McKinsey Global Institute released a report, Big data: The next frontier for innovation, competition, and productivity which projected huge demand for people with skills in big data management and analysis. The study forecasted that by 2018 there could be a shortage of between 140,000 to 190,000 workers in the field. This gap has not gone unnoticed by learning institutions in Canada. Many Canadian universities have responded by either encouraging interdisciplinary big data research through traditional programs or establishing specialized big data departments, courses, degrees and external partnerships.
Dalhousie University’s Institute for Big Data Analytics, the University of Victoria’s Ocean Networks Canada (ONC), Simon Fraser University’s School of Computing Science are a few examples. Ryerson University, University of Calgary and École Polytechnique de Montréal have programs too.
Some of these institutions have nominated candidates for Canada Research Chairs in big data research. Andrea Lodi, Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at École Polytechnique de Montréal is one of these. The Canada Excellence Research Chair in Data Science for Real-Time Decision-Making will bring together researchers from Polytechnique Montréal, HEC Montréal and Université de Montréal, and will receive about $22 million over seven years; $10 million of which will be invested by the Canada Excellence Research Chairs program. This reflects an effort among the major government research councils to put in place the elements of a well-functioning digital infrastructure ecosystem for research and innovation in Canada.
“We have limited resources as a planet,” said Dr. Lodi. “If we can limit our consumption of resources, control their waste and overuse—if we can use these resources in a smarter, optimized way, then it is better for our cities and towns, and better for the planet,” he said.
With greater human and computing capacity, and more innovative start-ups leading the evolution big data analytics, smarter management of natural resources in Canada will surely follow. What this will mean for water managers in the near future remains to be seen. WC