The importance of sustainability use cases can’t be stressed enough, especially in the long term. But how do we know if we’re making progress and how can we make progress? Big Data-driven sustainability use cases provide the answers as they have the potential to improve the world and that potential needs to be used to the fullest. With that said, let’s jump straight into the five use cases for achieving sustainability through Big Data.
Big Data-driven sustainability use case 1: Education
The various types of educational data include both academic and non-academic data such as assessments, performance, attendance, demographics, enrollment, contact details, schedules, interests, and survey responses.
The abundance of data in the education system can inform several parties to help achieve this sustainability goal of better education in different ways.
One way is through real-time and automated progress tracking.
A paper about Big Data for Education demonstrated how a student’s monitoring of progress can be sped up by systematic, real-time data.
The teacher in charge would also receive an automated performance report from the software to know the points for improvement.
This is an alternative to the teacher manually grading test papers and handing them back to the students a few days, weeks or months later.
Another way is through the personalisation of learning for individual learning needs as an alternative to the cookie-cutter approach of the same lectures, same homework and same pace for all students of various needs.
Such mining, analyses and machine learning of assessment data can quickly determine the optimal approach to teaching and personalised curricula.
The students can go through the required resources and experiences using their own learning paths.
Big Data-driven sustainability use case 2: Healthcare
Another sustainability goal is to improve the healthcare system and there are numerous ways to do it with Big Data, from the heavy workload of sequencing genomes with hundreds of petabytes of data to pandemic-related actions.
The EHRs are the digital version of a collection of patients’ medical records.
They make it easier for different healthcare facilities to share a patient’s records with each other to coordinate care, prevent repetition of procedures and determine a more precise and personalised treatment plan without the tedious paperwork.
To make EHRs more effective, the use of wearable trackers automate the monitoring and recording of health conditions, saving 15 hours of manual recording per week.
Such trackers can also alert healthcare professionals to emergencies in real time.
Big Data-driven sustainability use case 3: Poverty
Ending poverty by 2030 is the very first sustainability goal listed by the United Nations.
The first step in achieving this sustainability goal is poverty tracking driven by Big Data Science.
It shows the real-time estimates of people living in extreme poverty and monitors each country’s progress towards meeting the sustainability goal of ending poverty.
By understanding what is presented in the poverty tracker, poverty alleviation efforts can then be directed to the groups in need.
Big Data sources for poverty alleviation efforts include mobile phone, satellite and biometric data.
Mobile phone data can be used as proxies to identify time-series changes in socio-economic conditions (e.g. in form of anomaly or concept drift detection).
This can be done in real-time by correlating call details with wealth patterns, for instance.
Satellite data can be combined with machine learning and data analytics to identify geographically-specific poverty trends like characteristics of a house or neighbourhood that indicate wealth levels.
Biometric data can help poor communities effectively access aid and subsidies with instant identity verifications while making sure that aid doesn’t fall in the wrong hands.
Big Data-driven sustainability use case 4: Water
Obviously, a discussion about Big Data-driven sustainability can’t go on without mentioning water refinement and conservation.
The abundance of water data provides opportunities to water utilities and farmers to analyse these data and generate useful insights to make better decisions.
Internet of Things (IoT) technology plays a big part in gathering the sensor data about water supply via the Wireless Sensor Network (WSN), Supervisory Control And Data Acquisition (SCADA) and Automated Meter Reading (AMR) systems.
WSN is used to monitor water quality by measuring the water’s pH level, electrical conductivity, ORP and turbidity.
By checking if the water’s pH is neutral, conductivity is low, ORP is high and turbidity is zero, utilities can ensure that the water is safe for public consumption.
In contrast, if the WSN detects any deviation from these standards, it means that the water is polluted and needs to be attended to ASAP!
SCADA is spread throughout the water supply system for sensing and communication.
It can inform operators and managers if the water treatment facility is secure and in top condition without needlessly putting patrollers on shift.
AMR can also remove the trouble of manual meter reading and billing by automatically gathering and transferring data to the database.
All the WSN, SCADA and AMR data about water quality, treatment plant conditions and meter readings are combined in one Big Data system, where data miners can extract issues such as:
- whether the water is safe to use
- which areas of a region is the water not safe to use
- which areas of a region has water leakage or disruption
- which areas of a region lack access to sanitised water supply
- for how many households are the water reserves enough to serve
- the implications of water supply problems on communities
- telling farmers to know precisely how much water they need to add to the crops for crop yield, cost and water usage optimisation.
Big Data-driven sustainability use case 5: Garbage
An iunera Big Data project assessed if Big Data can be used to track the movements of rubbish collection trucks, predict when and which rubbish bins need to be emptied and locations of irregular waste.
The truck movements determine the amount of fuel used which costs money and air quality.
Thus, Big Data’s role here is to process and clean data to evaluate the various options of routes and recommend routes that are the most productive, low-cost and efficient in the long-run.
To optimise the sweeping and emptying of rubbish bins, Internet of Things (IoT) sensor data can be used to detect whether the rubbish bins are full and need to be emptied.
It can also help with forecasting when the bins are likely to become full, so that cleaning can be scheduled in advance.
Machine learning can be fed with data on how full the rubbish bins were and when.
A sweeper then only has to visit the bins when the machine learning forecasts tell the sweeper to do so.
Each time the sweeper visits the bins, the machine learning can be updated. The work gets done with less effort.
IoT sensor data analysis can also be leveraged to locate hotspots of irregular waste.
Working towards sustainability using Big Data
The capacity of Big Data Science to improve analysis and decision-making appeals to various types of organisations including those involved in ensuring sustainability.
There are so many ways Big Data can make a big difference to sustainability initiatives, and the five use cases mentioned are among them.
On the educational side, Big Data is capable of driving decisions like administration, and adjustments to various learning needs to support student learning.
For healthcare, this advancement has changed the way genomes are used; the way a patient’s health condition is monitored, recorded and shared; and the way congestion is managed in the ED.
For the poor, the targeting of poverty alleviation can be backed by tracking the number of people living in poverty, who they are, where they are, what they need to survive, etc.
For water management, employing Big Data and IoT tech for real-time water monitoring enables timely responses to water contamination and/or leakage/wastage.
And for garbage collection, tracking truck movements, sensing the fullness of rubbish bins and locating irregular waste to optimise garbage collection efficiency is possible.
All in all, there’s no denying that the responsible application of Big Data in sustainable development can more effectively measure the progress of sustainability efforts and bring them to greater heights.