Managing big data with artificial intelligencePosted on: November 17, 2023
by Ben Nancholas
Big data has become an indispensable tool for businesses, but it also means that organisations now face an unprecedented deluge of information. While this immense volume of data holds the potential to unlock valuable insights, managing such vast and complex data sets can be an arduous task.
This is where the integration of artificial intelligence (AI) and machine learning algorithms in data management comes into play.
What is big data?
Big data refers to the massive amounts of structured, semi-structured, and unstructured data generated from various sources, such as:
- customer databases
- online transactions
- Internet of Things (IoT) device sensors
- social media platforms and interactions
- online communication and smartphone apps
The characteristics of big data are often defined by the 3Vs:
- Volume, which refers to the amount of data.
- Variety, which refers to the different types of data.
- Velocity, which refers to the speed at which data is generated and collected.
What is data management?
Data management is the process of collecting, storing, organising, and analysing data. It’s an important area of data science, especially within businesses.
“The goal of data management is to help people, organisations, and connected things optimise the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximise the benefit to the organisation,” explains the tech giant, Oracle. “A robust data management strategy is becoming more important than ever as organisations increasingly rely on intangible assets to create value.”
Traditional database management approaches, however, often involve human intervention in handling and processing the data. These methods are difficult to apply to big data management due to the sheer volume and complexity of information.
This is where artificial intelligence steps in.
How is AI used in data management?
AI’s ability to process, analyse, and draw actionable insights from large amounts of data is reshaping data management – and businesses that leverage AI effectively can gain a competitive advantage, unlocking unprecedented opportunities for growth and innovation.
This is because artificial intelligence algorithms enable systems to learn from data patterns and even make data-driven decisions with minimal human intervention.
Key applications of AI in data management include:
- Data integration. AI algorithms can streamline the process of data integration from various sources. They can identify relationships between different data sets and merge them efficiently, offering a unified view of the data.
- Data quality and cleansing. AI can automatically detect and rectify errors or inconsistencies in raw data, improving data quality and ensuring accuracy.
- Real-time data analysis. AI capabilities allow businesses to analyse data in real-time, enabling quick decision-making and rapid responses to emerging trends.
- Unstructured data processing. AI-powered natural language processing (NLP) and deep learning algorithms can interpret unstructured data, such as text, images, and audio.
- Predictive big data analytics and forecasting. AI algorithms and analytics tools can predict future trends and outcomes based on historical data.
- Automated alerting systems: AI systems can be programmed to detect anomalies and trigger alerts, allowing timely responses to potential issues or opportunities.
- Visualisation-enhanced insights. AI can analyse data and present it via user-friendly visualisations, making complex information more accessible and understandable for data scientists, decision-makers, and other stakeholders.
The advantages of using AI for big data management
There are many reasons to use artificial intelligence in big data management. These include:
- Enhanced efficiency. AI-powered data management significantly reduces the time and effort required for data processing and analysis, leading to faster results.
- Improved data quality. AI algorithms can identify and rectify errors in data automatically, leading to higher data accuracy and reliability.
- Deeper insight. AI’s ability to process unstructured data enables organisations to gain valuable insights from previously untapped data sources, providing a more comprehensive understanding of their operations and customers.
- Faster decision-making. With AI, businesses can benefit from real-time data analysis, allowing them to make agile and well-informed decisions to stay ahead in dynamic environments.
- Cost savings. AI-driven automation reduces the need for extensive human involvement, cutting down operational costs and freeing up human resources for more strategic tasks.
- Scalability. AI systems can efficiently scale up to handle large amounts of data, accommodating the ever-growing data volumes that organisations encounter.
What are the challenges of using AI for big data management?
Artificial intelligence is a powerful tool, but it’s not without challenges. These can include:
- Data protection and privacy. AI systems need access to vast amounts of data to learn and improve, which can raise concerns about data privacy and security, so safeguarding personal data and ensuring compliance with regulations is essential.
- Data integration complexities. Integrating data from diverse sources can be challenging, especially when dealing with varying data formats and structures.
- Skilled workforce requirements. AI integration requires skilled data scientists and AI specialists to design, deploy, and maintain the AI algorithms and systems effectively.
- Bias and fairness. AI algorithms can inadvertently perpetuate biases present in the data they are trained on, leading to unfair decisions and outcomes, so ensuring fairness in AI models is critical.
- Interpretability. AI algorithms, particularly deep learning models, can be complex and challenging to interpret, making it difficult to understand how decisions are reached.
The future of data management and artificial intelligence
The future of data management is intertwined with artificial intelligence, and as AI technology continues to advance, its ability for handling big data will become even more sophisticated.
Areas of potential future developments may include:
- Hyper-personalisation. AI-powered data management can enable businesses to deliver hyper-personalised experiences and initiatives for their customers, tailoring products and services to individual preferences.
- AI in healthcare. The integration of big data and AI in the healthcare sector holds immense potential for predictive diagnostics, personalised treatments, and health insights from large patient data sets.
- Autonomous decision-making. As AI algorithms in big data management become more trustworthy and interpretable, they may play a more significant role in autonomous decision-making.
Build expertise in big data and artificial intelligence
Develop the knowledge and skills to recommend solutions and apply existing AI tools to real-world challenges with the 100%-online MSc Computer Science with Artificial Intelligence at the University of Wolverhampton. This flexible Master’s degree has been developed for forward-thinking individuals who may not have a background in computer science.
A key module on this course is in data science, allowing you to explore more about the applications of AI in big data management. You’ll learn about data capture, the acquisition of data, data maintenance, data processing, data analysis, data communication, and intelligent decision-making. You’ll also learn how to use software in the application of specialist algorithms and approaches to data processing and analysis.
Other topics of study include:
- machine learning
- applications of AI
- intelligent agents
- data mining