Since the public release of the internet, data has quickly become an invaluable resource for businesses around the world, helping companies gain key insights into consumer habits and opinions. Using Artificial Intelligence, these businesses have created effective data mining strategies that can discover patterns and knowledge from large, diverse data sets, leading to the emergence of big data. 

By using Machine Learning and its ability to automate tedious analytical tasks, businesses are opening themselves up to new strategies using predictive analytics that can help business leaders capitalize on seasonal trends and activity. Moreover, advanced AI models can even restructure fragmented data and adapt themselves over time with continuous learning to create more effective analytical material. 

Historical Context

Before the development of AI technology, data mining was originally done through traditional statistics that included regression analysis, clustering, and chi-square tests. Data visualization was a key way of turning this data into actionable insights, but required humans to plot the information on graphs manually which took tremendous time. However, data analysts were still able to make meaningful observations with this rudimentary data, finding correlations between certain products and customer trends. 

By the turn of the 21st century, new digital technology like the internet and computers were able to generate larger amounts of data through social media, e-commerce, and digital communication like e-mail lists. This led to new storage distribution systems like Hadoop that can store and process petabytes of data. 

Over time, even more powerful systems were created as the demand for real-time data became stronger. Cloud platforms like AWS and Azure helped businesses manage large amounts of commercial data and became a powerful tool for both big and small businesses, eventually leading to the incorporation of AI technology. 

Deep Dive: How AI Enhances Data Mining

With the rise of AI and more advanced Deep Learning algorithms, businesses are finding new ways to interact with raw data, leading to a revolution in data mining. 

• Speed and efficiency: Data mining processes originally required large amounts of dedicated time from teams and individuals. Not only was this time-consuming, but it was also prone to human errors. Fortunately, AI is able to mitigate these errors and automate tedious input tasks with machine learning.  

• Pattern recognition: Deep learning models can identify subtle patterns gathered from data mining, extracting valuable information that was originally overlooked by traditional statistical methods used by humans. 

• Predictive analytics: AI can use processed data that creates powerful forecasts into consumer trends, helping businesses prepare for higher or lower customer demand. Over time, these forecasts can become more intricate with advanced deep learning models like Long Short-Term Memory networks. 

Real-world Application

There are many ways that businesses can utilize AI-powered data mining to improve their performance. For example, if an e-commerce business sees that their sales are plateauing, or even dropping, they can use Machine Learning applications to identify which areas of their website and storefront need improving. For instance, insights suggesting stronger product recommendation feeds or more nuanced AI chatbots can improve the customer experience and drive up sales. 

Beyond improving the performance of a single e-commerce website, this application of improved data mining also benefits industry competition and creates a better experience for customers overall as they search for the most beneficial platforms to interact with. 

Big Data Challenges and How AI Addresses Them

Beyond the massive scale of big data, there are many other challenges that exist when using AI for data mining. Due to its sheer volume, a lot of captured data can be low-quality and fairly useless. This requires additional AI models that can detect low-quality information and either correct it or remove it from the database to ensure more accurate results. 

However, a lot of valuable data can also be damaged or fragmented, making it difficult for aI models to utilize in predictive analytics. In this instance, neural networks are needed to repair the broken data so that it can be usable, fortunately, these repairs can also be applied to images and video data using convolutional neural networks that are primarily used for grid-like data structures. 

Ethical Implications and Considerations

Common with most situations involving big data, the use of private information by companies and businesses has become a large concern for consumers who are uncomfortable with how their personal data is being used. Since the spawn of big data and the internet, private information has become a valuable commodity and creates many legal debates about what can and cannot be used for business purposes. 

Over the last decade, websites have slowly become required to inform online visitors about how their data is being collected and used, with the option to opt-out for those who oppose the use of their data. Another solution was to anonymize the data itself so that consumers couldn’t be identified through their data, but AI has become powerful enough that it can reverse-engineer this data and identify the specific users. 

What's Next for AI in Data Mining and Big Data?

As AI becomes more powerful, so will data mining, which holds a lot of exciting potential for the world of business going forward. With more powerful Deep Learning models, predictive accuracy is only set to grow, giving businesses more beneficial insights into consumer trends and habits. 

It doesn’t end there though, emerging technology like quantum computing holds exponential benefits to data mining with its ability to extract actionable insights instantly by using superpositioning. While quantum computing still requires decades of research, the benefits it can create are incentive enough to ensure that its development never ceases.