Since the first computers were introduced in the middle of the twentieth century, science and industry alike have learned that processing and analyzing big, raw data sets can be hugely rewarding. However, as datasets grow, so does the computational challenge of processing them.
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Artificial intelligence (AI) has evolved as one of the primary options for solving this challenge, using AI systems instead of manual data processing to save time and boost industrial innovation.
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Data Processing Methods to Handle Big Data
Big data analytics—the process of converting vast, raw datasets into meaningful insights—has reshaped nearly every industry and sector worldwide. From financial markets to space exploration, data analysis has been a driving force behind technological advancements over the past 50 years.
The value of big data lies in its scale: with a sufficiently large dataset, predicting future trends and behaviors becomes remarkably accurate.
Big data is typically characterized by three core dimensions:
- Variety: Data from diverse sources.
- Volume: The sheer size of data.
- Velocity: The speed at which data is generated and processed.
Over time, two additional characteristics have been recognized:
- Veracity: The reliability and trustworthiness of data sources.
- Value: The usefulness and quality of the data.
Despite its advantages, handling massive datasets presents challenges. The larger and more complex the dataset, the more difficult it becomes to process efficiently. Data processing involves several key steps, including collecting data in a machine-readable format, storing it, retrieving it, comparing it with other data points, and performing specific functions or analyses.
To manage these processes, algorithms play a crucial role. These mathematical instructions guide computers on what tasks to perform, when to execute them, and how to handle the resulting information. Computing power—measured by the speed at which a system can execute these instructions—has significantly advanced, allowing modern supercomputers to process millions of tasks simultaneously.
However, as datasets continue to expand in both size and complexity, driven by their increasing value, industries and researchers are constantly seeking new ways to enhance data collection and analysis. Companies like Google and Facebook have leveraged their platforms to gather vast amounts of user data, providing invaluable insights for marketers.
Additionally, passive sensing, remote facilities, and automated networks have introduced even more data streams. The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) now extract data from previously unmonitored devices, further increasing the processing demand.
At the same time, advancements in broadband, mobile data networks, and cloud computing have significantly expanded our ability to transmit, store, and process large volumes of data. Beyond hardware improvements, cutting-edge data science now incorporates artificial intelligence (AI) to accelerate data processing.
AI: A Smarter Approach to Big Data
AI has been found to go beyond the limits of traditional data processing methods, it can be used to spot unusual data points that could indicate errors, fraud, or significant events, convert data into a consistent format to ensure accuracy and compatibility, as well as for predictive analytics to forecast trends and outcomes, helping businesses make proactive decisions.
Some of the key AI techniques making an impact include:
- Machine Learning (ML): Algorithms that learn patterns from data without needing explicit programming, helping with predictions and pattern recognition. For example, ML algorithms like clustering can be used to segment customers based on their purchasing behavior, identifying distinct groups within a large customer dataset for targeted marketing campaigns.
- Deep Learning (DL): A specialized type of ML that uses neural networks with multiple layers to identify complex features and patterns in data. DL models, particularly recurrent neural networks (RNNs), are used in fraud detection to analyze sequences of transactions and identify patterns indicative of fraudulent activity, which is especially useful in high-volume financial data.
- Natural Language Processing (NLP): Helps computers understand, interpret, and even generate human language, making it easier to analyze text-based data. NLP techniques are used to analyze social media feeds and customer reviews to gauge public sentiment towards a product or service, enabling businesses to respond quickly to negative feedback and improve their offerings.
AI in Action: Real-World Applications
AI is already tackling complex data challenges across multiple industries. Here are a few examples:3
- TV Program Popularity Forecasting: Using a dataset of historical viewership ratings, social media trends, and keyword search volumes, analysts trained a Random Forest model to predict viewership numbers for upcoming TV programs. The model reduced forecasting error by 15% compared to traditional statistical methods.
- Demonetization Data Analysis: Researchers analyzed a dataset of 5 million tweets related to demonetization using a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The SVM identified key emotional responses (joy, anger, fear) and revealed a shift in public sentiment over time, providing valuable insights for policymakers.
- Multimedia Big Data Processing: A hybrid-stream model for video analysis incorporated data preprocessing, classification, and load reduction. A modified Convolutional Neural Network (CNN) was trained on a dataset of 10,000 hours of surveillance video to identify objects of interest (e.g., vehicles, people). The modified CNN achieved 92 % accuracy in object detection, while reducing network bandwidth usage by 20 %.
- Health Status Prediction: A decision tree model (specifically, C4.5) was trained on a dataset of 100,000 patient records to predict the likelihood of hospital readmission within 30 days. The model identified key risk factors (age, pre-existing conditions, medication adherence) and improved prediction accuracy by 8 % compared to previous methods.
- COVID-19 Medical Scan Analysis: An Inception-v3 model, pre-trained on ImageNet and then fine-tuned on a dataset of 5,000 chest X-ray images, was used to detect COVID-19 pneumonia. The model achieved a sensitivity of 95 % and a specificity of 90 %, outperforming radiologists in early detection of the disease.
New Developments
A recent study published in Science introduced new hardware that could revolutionize data processing by enabling faster AI computation with significantly lower energy consumption. Biological information processing in synapses and neurons is constrained by the aqueous medium through which weak action potentials of 100 mV propagate over milliseconds. However, artificial solid-state neurons have no such voltage and time limitations and can be fabricated at the nanoscale, making them many times smaller than their biological counterparts.
In this research, the scientists prototyped nanoscale protonic programmable resistors using complementary metal-oxide semiconductor–compatible materials. These resistors can withstand high electric fields of 10 MV/cm and offer energy-efficient modulation at room temperature. These devices are 10,000 times faster than biological synapses, providing a major breakthrough for AI-driven data analytics. By significantly enhancing processing speed and efficiency, they lay the foundation for handling vast datasets more effectively, reducing computational bottlenecks, and improving real-time decision-making in data-heavy industries.4,5
Limitations and Ethical Considerations
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Despite these ongoing advancements, there are some important challenges and ethical considerations that must be addressed.
- Bias in Data: AI models are only as good as the data they are trained on. If the data contains biases, the AI system can perpetuate and even amplify these biases, leading to unfair or discriminatory outcomes.
- Privacy Concerns: AI's ability to analyze vast amounts of personal data raises significant privacy issues. Without proper safeguards, sensitive information can be misused or exposed. Ensuring data anonymization and giving individuals control over how their data is used is essential to maintaining trust and compliance with privacy regulations.
- Explainability and Transparency: Many AI models, particularly deep learning systems, function as "black boxes," meaning their decision-making processes are difficult to interpret. This lack of transparency makes it challenging to identify errors, biases, or unfair practices, reducing trust in AI-driven systems. Developing explainable AI models is crucial for accountability and ethical use.
- Security Risks: AI systems are vulnerable to adversarial attacks, where malicious actors manipulate input data to deceive the AI into making incorrect decisions. These risks pose threats in critical applications such as cybersecurity, finance, and autonomous vehicles, highlighting the need for robust security measures.
All in all, AI is reshaping industries by accelerating data processing and improving decision-making, but it also comes with challenges that must be carefully managed. Addressing bias, privacy concerns, transparency, and security risks will be key to ensuring that AI-driven solutions remain fair, ethical, and reliable. As technology evolves, striking a balance between innovation and responsible AI use will be crucial for maximizing its benefits in fields like healthcare, finance, and media.
Concluding Thoughts
At the end of the day, AI and big data are changing the way we understand and interact with the world. With faster data processing and smarter analysis, AI is driving innovation across industries and helping tackle big challenges—whether it’s improving healthcare, making financial systems more efficient, or even fighting climate change. The potential for positive impact is huge.
But as exciting as these advancements are, we can’t ignore the ethical side of things. Issues like bias, privacy, and security need to be addressed to make sure AI is used responsibly. The key is finding the right balance—pushing innovation forward while keeping fairness and transparency in check. If we get that right, AI and big data can help build a more sustainable, fair, and thriving future for everyone.
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References and Further Reading
- Demigha, S. (2020). The impact of Big Data on AI. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 1395-1400. DOI: 10.1109/CSCI51800.2020.00259, https://ieeexplore.ieee.org/abstract/document/9458076
- Johnson, S., Samuel, A. (2022). AI-Driven Data Analytics for Real-Time Business Insights: Applications and Challenges. https://www.researchgate.net/publication/386732858_AI-Driven_Data_Analytics_for_Real-Time_Business_Insights_Applications_and_Challenges
- Rahmani, A. M. et al. (2021). Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Computer Science, 7, e488. DOI: 10.7717/peerj-cs.488, https://peerj.com/articles/cs-488/
- Zewe, A. (2022). New hardware offers faster computation for artificial intelligence, with much less energy [Online] Available at https://news.mit.edu/2022/analog-deep-learning-ai-computing-0728 (Accessed on 24 February 2025)
- Onen, M. et al. (2022). Nanosecond protonic programmable resistors for analog deep learning. Science, 377(6605), 539-543. DOI: 10.1126/science.abp8064, https://www.science.org/doi/10.1126/science.abp8064
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