5 Ways Big Data Is Transforming Waste Management

5 February 2019 Admin Leave a comment Blog

Everywhere you look within the waste management industry, two words are dominating the conversation: big data. But what is big data, and is it really disrupting the waste industry?

On its face, big data hardly sounds like a game-changer for any industry. There is no universally agreed-upon definition of “big data,” but the term generally refers to very large data sets that can be analyzed computationally to reveal patterns, trends, and associations, primarily relating to human behavior.

The key two words in that definition are “analyzed computationally.” We’ve always known the importance of analyzing data, but in the past, when confronted with such large volumes, we were not always able to do so. But with the capabilities of modern computers, machines are now telling us what the data means; as a result, our focus can move from identifying problems to finding solutions.

So what does big data mean for the waste industry? You’d be amazed. Here are five ways that big data is driving significant changes in the waste industry.

1. AI-Enhanced Recycling Robots

Recycling has always been a labor-intensive process with a surprisingly high injury rate for human workers. Now, by replacing a portion of the human element with intelligent machines, the process of solid waste recycling is becoming cheaper, safer, and much more efficient. 

One such machine is Clarke, the result of a collaboration between two Denver-based companies and the Carton Council. Clarke is a recycling robot that uses artificial intelligence to identify a wide array of food and beverage containers so it can preemptively separate them from the rest of the recycling waste. 

The Clarke project uses an off-the-shelf robot that has been in use for two decades in other industries. This is one example of the way that big data allows the waste industry to reinvent itself by repurposing proven, existing technologies. Clarke has been consistently improving its recycling data skills, recently demonstrating near-perfect accuracy while grabbing approximately 60 cartons of recyclables per minute. Clarke is programmed to recognize images (including logos) on packaging; after encountering them for the first time, it learns the new patterns and applies them to the next round of sorting and grabbing. As the AI quotient increases based on accumulated solid waste data, the waste management system becomes able to sort at superhuman speeds, diverting material for reprocessing that might otherwise end up in a landfill.

2. Recycling Vehicles

Although the average car has a lifespan of around a decade and approximately 200,000 miles, many vehicles stay on the road much longer. At the same time, Americans buy approximately 17.6 million new cars per year, which creates something of a paradox. If we’re buying so many new cars, where are all of the old cars going? The answer provides another example of how big data is revolutionizing the waste industry.

Many cars are taken out of operation by natural disasters like floods and hurricanes. The severe conditions impact the cars in large numbers, creating an inventory glut for scrapyards and waste haulers. Although many of these vehicles are simply totaled, others are broken down for parts after recalled components are removed.

By using big data to identify trends in disaster-related car abandonment, and by cross-referencing that information with recall databases, scrap and salvage centers and auction sites can streamline the systems they use to process impacted vehicles. This enables salvage businesses to maximize owner payouts, improve part distribution, and keep the stream of totaled cars moving, allowing more cars to be repurposed instead of being relegated to the scrap heap.

3. GIS Analytics

The city of Stockholm, Sweden recently investigated the idea of using big data to solve its municipal waste problems. The primary focus of their inquiry used GIS to identify inefficiencies in waste collection routes in the city and to suggest potential improvements based on their analytics.

The city assembled a large dataset that consisted of approximately half a million data points, including waste fractions, weights, and collection locations. They developed a series of new waste management maps, followed by batch geocoding of the curated entries. 

After conducting a preliminary analysis, maps of selected routes were drawn up in detail, and the efficiencies of those routes were assessed using a proprietary efficiency index. The project revealed substantial inefficiencies, leading to many interventions, including but not limited to the creation of a shared waste collection vehicle fleet. 

4. Using Satellite Data to Safeguard Our Oceans

The Great Barrier Reef is dying, and the Australian government is doing their best to save it. Factors cited by scientists in the reef’s decline include warming waters which are accelerating coral bleaching, causing permanent damage to this natural resource.

One way that big data can help alleviate further environmental harm involves using satellite sensors and data processors to identify ecological threats to the oceans. Scientists can observe patterns in the routes taken by ships and use that data to determine what impact these shipping routes will have on the environment. This data could be used to provide regulatory agencies with a basis for investigations and enforcement actions.

5. Fullness Monitoring by Sensa 

These approaches demonstrate the usefulness of big data throughout the waste management industry, but they primarily focus on examining large data trends to implement strategic changes over the mid-to-long range. But other companies are going a step further by finding a way to utilize big data in the short term to improve waste management data operations at both the micro and macro levels.

One example of an industry disruptor is Sensa Networks, which works with waste management solutions at all levels. Sensa’s Fullness Monitoring revolutionizes the way waste haulers operate on a continuous basis by automatically scheduling pick-ups and issuing purchase orders only when compactors reach a specified fullness level. 

The sensor-based approach eliminates the cost of unnecessary collection activities while simultaneously reducing an organization’s carbon footprint. Everything is managed through an innovative and comprehensive web-based remote monitoring platform. Providing further peace of mind, all Fullness Monitoring data is updated in real time and is available 24/7.


Big data is far more than a simple buzzword; it’s a game-changer, and it’s here to stay. Companies who realize the organizational benefits of using big data to optimize their operations will undoubtedly emerge as industry leaders, while those unwilling to evolve with new technologies will see their profits decline proportionately. The time to take advantage of these new processes and products is now.

For more information on how your organization can benefit from cutting-edge developments in waste management, contact the team at Sensa Networks today.


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