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Published on 19 Jun 2024

How is AI used in Data Analytics?

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updated

Updated: 24 Jun 2024

Data Analytics owe their origins to the praiseworthy work of John Tukey. Many consider John Tukey the father of data science as he laid the groundwork for many of the key concepts and methodologies that later came to define technology. As time progressed, the application of artificial intelligence in data analytics grew exponentially, driven by the need to analyse the increasing volumes of massive datasets.

Today, AI's pervasive presence in data analytics is something that we cannot ignore. From Fortune 500 companies to startups, from healthcare and finance to manufacturing and retail, almost every industry is employing AI in their data assets. But how exactly are organisations doing this? In this article, we will explore how AI is used in Data analytics across different industries.

What is AI in Data Analytics?

AI in data analytics refers to the use of advanced algorithms and machine learning techniques to analyse and extract valuable insights from large and complex datasets. AI plays an imperative role in various aspects of data analytics, including:

  • Data Preprocessing: AI algorithms can help in cleaning, transforming, and preparing data for analysis by identifying and handling missing values, removing outliers, and converting data into suitable formats.
  • Predictive Analytics: AI enables predictive modelling techniques, such as regression, decision trees, and neural networks, to forecast future trends, behaviours, or outcomes based on historical data.
  • Anomaly Detection: AI can identify unusual patterns, outliers, or anomalies in data that may indicate fraud, system failures, or other critical issues.
  • Natural Language Processing (NLP): AI-powered NLP techniques can analyse unstructured data like text, audio, and video, enabling sentiment analysis, topic modelling, and text summarisation.
  • Image and Video Analytics: AI algorithms like computer vision and deep learning can extract insights from visual data, enabling applications like object detection, facial recognition, and image classification.
  • Recommendation Systems: AI-driven recommendation engines can analyse user preferences, behaviour, and other data to provide personalised recommendations for products, services, or content.
  • Automated Insights Generation: AI can automatically generate reports, visualisations, and narratives based on the insights derived from data analysis, making it easier for decision-makers to consume and understand complex information.

Real-World Applications of AI in Data Analytics

AI is being used in various industries for data analytics, including:

Predictive Maintenance in Manufacturing

Predictive maintenance leverages AI algorithms to analyse equipment sensor data and predict when machinery is likely to fail. By identifying patterns indicative of potential breakdowns, manufacturers can proactively schedule maintenance, minimise downtime, and reduce maintenance costs. AI-powered predictive maintenance systems can also optimise maintenance schedules based on equipment usage patterns, environmental factors, and other variables, ensuring optimal operational efficiency.

For example, if abnormal vibration patterns are detected in a machine's motor, indicating potential bearing wear, the system can trigger a maintenance alert. Maintenance can then be scheduled during non-production hours to replace the bearings, preventing a breakdown during operation. This proactive approach minimises unplanned downtime, reduces maintenance costs, and ensures optimal equipment performance.

Also Read: How GPUs Power Up Threat Detection and Prevention

Healthcare Diagnostics 

In healthcare, AI enables advanced diagnostics and prognostics by analysing medical imaging data, patient records, and genomic information. AI algorithms can accurately detect abnormalities in medical images such as X-rays, MRIs, and CT scans, assisting radiologists in early disease detection and diagnosis. AI-driven predictive analytics can assess patient data to identify individuals at higher risk for certain diseases or adverse outcomes, enabling personalised treatment plans and proactive interventions to improve patient outcomes and reduce healthcare costs.

For example, in a chest X-ray, AI algorithms can highlight areas of concern, such as nodules or lesions, aiding radiologists in identifying potential signs of lung cancer. AI-driven prognostics can analyse patient data, including genetic markers and medical history, to predict disease risks and prognosis.

Also Read: Understanding the Role of GPU in Healthcare

Fraud Detection in Financial Services

Financial institutions use AI-powered data analytics for fraud detection and prevent financial losses. By analysing transactional data in real time, AI algorithms can identify suspicious patterns, anomalies, and deviations from normal behaviour indicative of fraudulent transactions. AI-driven fraud detection systems continuously learn from new data to adapt to evolving fraud tactics, enhancing their accuracy and effectiveness in identifying fraudulent activities across various channels, including credit cards, online banking, and insurance claims.

For example, if a credit card transaction is flagged for being significantly different from a customer's typical spending behaviour, the system can automatically trigger an alert for further investigation.

Also Read: How to Detect Fraud Using Data Science

Marketing and Recommendation Systems

E-commerce platforms and digital media companies leverage AI in data analytics to deliver personalised marketing campaigns and product recommendations to customers. By analysing customer behaviour, browsing history, and purchase patterns, AI algorithms can generate personalised product recommendations tailored to individual preferences and interests, improving customer engagement and conversion rates. AI-driven marketing analytics enable marketers to optimise advertising strategies, target specific customer segments, and measure the effectiveness of marketing campaigns in real time, enhancing ROI and customer satisfaction.

For example, a customer frequently rents NVIDIA A100 GPUs for Artificial Intelligence workloads. In that case, the system can recommend complementary services such as cloud storage for data backups or GPU-optimised software frameworks for faster large AI model training.

Also Read: How GPUs Supercharge AI and ML for Breakthroughs 

Limitations of AI in Data Analytics

While AI has brought many benefits to data analytics, there are also some challenges and limitations to consider:

  • Data Quality and Bias: AI algorithms heavily rely on the quality and diversity of data for training and decision-making. Biases present in training data can lead to biased outcomes and inaccurate predictions, especially in sensitive domains like healthcare and finance.
  • Ethical and Privacy Concerns: The use of AI in data analytics raises ethical concerns related to privacy, surveillance and data protection. Organisations must follow the ethical implications of data collection, usage, and algorithmic decision-making to ensure fairness, transparency, and compliance with regulations such as GDPR. Hyperstack offers GPU cloud services with high data privacy and security standards. We ensure European data sovereignty and adhere to strict data protection laws for our customers.
  • High Performance Computing: Training and model deployment in machine learning and AI often require significant computational resources, including high-performance hardware and large-scale data infrastructure. This can be a barrier for organisations with limited resources or budget constraints. You can check our transparent cloud GPU pricing for NVIDIA GPUs like the A100 and H100 PCIe, optimised for high performance and efficiency in AI. 

Conclusion

While AI has revolutionised data analytics allowing organisations to extract valuable insights from large and complex datasets, it is imperative to address the associated challenges and limitations. One of the key challenges is the high computing power required for training and deploying AI models. Traditional on-premises infrastructure may not be sufficient to support the computational demands of AI workloads. This is where cloud GPUs offer a compelling solution, providing organisations with access to high-performance hardware and scalable resources on demand.

Hyperstack offers cost-effective access to enterprise-grade NVIDIA GPUs, optimised for AI workloads. With features like role-based access control, pre-configured SKUs and a bespoke API for cloud GPU workloads, organisations can easily manage resources, scale operations, and maintain control over computational costs. For organisations seeking to experience the full potential of AI in data analytics while addressing the associated challenges, opting for cloud GPUs is the best solution.

Sign up to Hyperstack today to reduce your compute overheads by up to 75% and experience the power of AI in Data Analytics. 

FAQs

What are the Benefits of AI in Data Analytics?

AI offers several benefits in data analytics, including enhanced data processing capabilities, improved accuracy in predictive modelling, automated insights generation, and the ability to analyse unstructured data like text and images. It enables organisations to make data-driven decisions and gain a competitive edge through personalised recommendations and targeted marketing strategies.

What are some common AI techniques used in data analytics?

Common AI techniques used in data analytics include machine learning algorithms (regression, decision trees, neural networks), natural language processing for text analysis, computer vision for image and video analytics, and recommendation engines. These techniques leverage advanced algorithms to process large datasets, identify patterns, make predictions, and generate personalised recommendations.

What are some examples of AI in data analytics?

AI applications in data analytics span various industries, such as predictive maintenance in manufacturing, healthcare diagnostics and prognostics, fraud detection in financial services, and personalised marketing and recommendation systems in e-commerce and digital media. AI algorithms analyse sensor data, medical records, transaction patterns, and customer behaviour to drive operational efficiency, improve patient outcomes, prevent fraud, and enhance customer experiences.

Which is the best GPU for AI in Data Analytics?

We recommend using NVIDIA A100 and H100 PCIe to accelerate AI in Data analytics workloads.

 

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