4 Practical Big Data Use Cases in Waste Management


Cities are developing along with population growth, and waste is produced as people’s requirements increase. With that in mind, waste management requires greater attention than it now receives. Waste management is generally required to preserve ecological balance and follow a sustainable lifestyle.

What is Waste management?

The term “waste management” refers to the entire process that takes place, from collecting different types of garbage to their proper disposal. Every company and home produce garbage, which can be enormous when viewed on a global scale. There are many different types of trash; hence, other procedures and methods for safe disposal are used depending on the type.

Naturally, health risks are a concern throughout the entire waste management process. This practice cannot be avoided because improperly disposed of trash and unorganized waste can have disastrous effects on the environment and humanity.

The proper waste management practices of reuse and reduction, animal feeding, recycling, composting, fermentation, landfills, incineration, and land application can contribute to resource conservation, pollution reduction, energy conservation, environmental protection, and other goals.

For instance: 

  • During the pandemic, the output of surgical masks, PPE kits, used syringes, and other medical wastes increased significantly. Sometimes, when it comes to disposal, these components are mishandled.

  • Animals and birds rummage through them, the uncut elastics of the masks strangle the birds’ beaks, cows and buffaloes ingest the dangerous plastics, and there is still a worry about the stench of the waste and unanticipated infections.

We can now prevent this by using proper waste management and disposal methods. A significant amount of waste output can be halted without more stress on municipal cooperation or the government on an individual level if we adopt the 4R of the environment, which stands for REDUCE, REUSE, RECYCLE, and RECOVER.

The capabilities of waste management systems and the associated risks can be reduced by using specific technology at work. Advanced analytics, such as Big Data, can resolve many management-related problems and help in understanding the system’s further requirements to dispose of the waste successfully with the fewest risk.

What is Big Data anyway? 

Big data is just the same data present in enormous quantities and volumes. Data is the term used to describe stored information and recorded actions conducted by the computer in various ways. This is made up of information given and includes people and technology.

Big data is a collection of data that is also expanding at an exponential rate over time. The scale, complexity, and storage of big data make it impossible for conventional data management solutions to understand and understand it effectively.

Big data can be used to expose previously missed patterns by utilizing analytical understanding and predefined conclusions to get the desired outcomes and understand and decode the data regarding current concerns. 

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Data gathering for waste management using big data

Since waste management involves more than merely gathering enormous amounts of trash and maintaining clean streets, the application of Big Data analytics can assist in identifying the demands and modifying the techniques best suited for them because it is a more complex undertaking.

Big data gathered, driven, and used by surveys connected to cleanliness can assist in determining the best places for bins on the streets where they can be used effectively. This can also determine how frequently bins are emptied based on location.

Such solar-powered trash cans with sensors and compactors have been put in NYC and San Francisco; when a predetermined amount of filling is achieved, they inform collectors to empty the bins.

  • Big data can assist in determining the best routes for garbage collection and the most convenient times to do so; in addition, it can assist in determining the region from which a specific type of waste originates so that it can be sent to the appropriate recycling and reuse facilities for simple procedures.

  • Big data techniques can be used in an industrial setting to formulate effective management systems and comprehend historical failures.

  • Big data techniques can assist in preserving a record of the successes and failures of the waste management processes so that people can continue to improve and adjust them.

Big Data Applications in Waste Management

Below are a few practical uses of big data in the waste management sector: 

  • Artificial Intelligence Systems

Understanding big data can create the demand for more cutting-edge technology that can be applied to waste management. The method of waste management includes recycling as a critical component. Big data can offer information on the type, amount, and locations of currently available waste.

Recycling is a labor-intensive operation with a significant risk of human injury and illness; this can aid in deploying artificial intelligence-based systems to carry out recycling sorting.

These AI-based robots designed to pick out and classify a specific type of trash could help the recycling sector by advancing a quicker, safer, and more affordable procedure. 

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  •  Recycling of Vehicles

Ironically, people are more likely to own their own cars as public transportation is mandated more frequently. Given the average lifespan of a car and the number of older vehicles allowed on the road, some vehicles leave service due to disasters and other unforeseen circumstances.

What can be done with the old, useless, and obsolete automobiles is raised by this one question.

Well, understanding the quantity and location of abandoned cars can be aided by big data. This will enable the salvage and scrap yards to remove the valuable components from the car before it is totaled. This increases the amount of money these companies can make and lowers the amount of potential waste.

  • Improving Stocks

Big data can assist in improving inventories by identifying how much of a product is required in each business, preventing the production or purchase of extra items that will ultimately go to waste. Big data analytics can help in partially predicting the required quantity. In times of crisis, specific sectors stockpile excess that is never used.

For instance: In the pharmaceutical industry, the drug inventory ages, making it impossible to treat everyone the same way. In such situations, stockpiled items and medicines go to waste.

Similar to how too much of a middleman’s buy would be wasted.

When this occurs, information may be gathered using big data to prevent waste, which is incredibly beneficial for the first stage of waste management, waste generation.

In order to verify the information provided by big data technologies, this information can be cross-referenced with other data such as market demand for the product, users in the nearby demographic area, patients’ medical histories, the effectiveness of the medication, and so on.

  • Satellite-Based Surveillance

Satellite data can be a handy tool for understanding what is being done to the environment’s capacity by natural resources. The massive barrier reef, the Amazon forest, and the Pacific garbage patch are all in decline.

By employing satellite data and cameras to monitor the harm being done by human activities and based on that data, it is possible to find clear strategies to protect against environmental degradation caused by human activity.

Coral reefs are being bleached by the warming waters caused by global warming, sewage and trash are being dumped into the oceans by large ships, and landfills are being built on potentially fertile land. These and other issues can be resolved for the environment’s good, but only if we have the skills and equipment necessary.


As you can see, the challenge of waste management is serious, but as technology advances, it is now possible to understand the issues and find solutions.


The waste management systems are being kept well-prepared to handle the crisis using data science in conjunction with roles like artificial intelligence, satellites, citizen involvement, and participation. This is done by drawing important conclusions while sifting through enormous amounts of available data from various locations and aspects. If you are keen to learn more about this technology, sign up for the best data science courses in India, and get started as a big data expert. 


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