The use of data mining and Artificial intelligence (AI) in business is rapidly evolving and capable of helping organizations create new and innovative business models.
Data mining provides reliable information to make smarter decisions. It is part of a process that gathers data to generate reliable insights. However, to effectively transform this data into valuable information, you must first be diligent with the methods used to obtain and refine this data.
When combined with AI (and machine learning), the data mining process can go deeper and reveal even more insightful information that helps drive business growth. Data mining generally focuses on finding patterns that can be used for predictive purposes. Whereas, artificial intelligence also has additional applications, such as planning and natural language processing.
Yes, you may already be asking yourself more than just a few questions.
Data mining? What is the correlation of data mining with artificial intelligence (AI)…and what are the benefits of combining both of these technologies?
Don’t worry, all of this and more will be explained in this article.
Data mining is the process of extracting and refining data into meaningful, structured data for analysis. It can help uncover hidden patterns and valuable information from large sets of data.
Since raw data on its own does not say much, digging deeper enables you to extract facts and figures to identify patterns that are relevant to you.
Countless organizations use similar methods to understand market trends and consumer behavior. This enables them to make better decisions, improve their products and offerings, mitigate risks, and much more.
Enter the need for data mining.
Through the use of data mining tools, such as software, algorithms, and statistical methods you can determine correlations or new trends in data. From there, you can apply them in a way that benefits your business or organization.
Data mining provides businesses with reliable information to make smarter, more informed decisions on their products, services, and operations. It can also be used to mitigate risks, improve product safety, and predict future outcomes.
The results create data that is reliable for in-depth analysis and help predict outcomes, future trends or even outperform the competition.
Ensuring clean, usable data is not an easy task. As such, the data mining process typically involves a series of steps, divided into two parts.
The first part - data pre-processing - covers data cleaning, data integration, data selection, and data transformation. Pre-processing is important as there are many factors that determine whether a data is useful for your intended purposes. You’ll have to filter out what works and what doesn’t, and whether the data is complete and accurate.
The second part - the actual data mining - performs pattern recognition and visualization to present the mined information as usable knowledge.
A popular methodology for data mining is CRISP-DM, or the Cross Industry Standard Process for Data Mining, which is made up of six basic steps.
The 6 steps of the CRISP-DM Model:
Set clear objectives and what you hope to gain from your data mining project. Doing this simple but crucial step ensures that you get the most out of your project.
Once you’ve acquired the necessary data, it’s time to dig deeper and validate its quality. This stage involves understanding the structure and distribution of data, as well as the relationships among different subsets.
This stage includes data cleaning and transformation, as well as feature selection. Then select the appropriate modeling technique.
This is where data mining models are built, with training data fed into the model, and initial results are obtained. There three main types of data mining modeling techniques are association analysis, regression analysis, and classification and prediction.
Here you decide whether the models you’ve picked have produced results in line with your business and project goals. You’ll also determine if further tweaks are needed, or if you are ready to proceed with deployment.
In the final step, the model is deployed in a real-world setting and results are monitored for performance. Depending on the results, a report may be generated to help improve your organization’s decision-making.
Although it is said these technologies are connected, data mining and AI are not the same. We have already explained plenty about data mining above, now let’s get a brief overview of Artificial Intelligence before diving deeper.
According to John McCarthy, Professor Emeritus of Computer Science at Stanford University, Artificial Intelligence (AI) is defined as “the science and engineering of making intelligent machines, especially intelligent computer programs.”
Building an AI program requires a combination of data analytics, data mining, and machine learning. Once the AI model has been built, new data can be input to generate predictions, without having to repeat the process manually.
Since AI learns from experience, incorrect data leads to inaccurate models if not programmed efficiently. As a result, you will not be able to leverage the right analytics or predictions, which can affect your data mining production cycle.
This is where Machine Learning (ML) ties in and aids the process.
ML is a subset of AI directed at algorithms that learn from data without being explicitly programmed. Machine learning can be used to automatically detect patterns in data and then use this knowledge to make predictions or recommendations.
With that said, data mining is also a complex endeavor.
There is no one-size-fits-all solution or process. What’s essential is finding the right fit for your needs to better align with your organization’s goals.
When it comes to data mining, high-quality and reliable data extraction are key. Finding the right way for both data mining and AI to efficiently improve each other is the key to success.
For example, Zyte's Automatic Extraction API is the fastest way to go, especially if large-scale data extraction is involved.
Through this, you have instant access to open web data leveraging our patented AI-powered automated extraction API.
Below, we will show additional examples to help you better understand how AI can improve data mining.
The way that data mining is used to work together with is key AI to positively impact your data-related efforts, and as a result, your organization as a whole.
Understanding the patterns and relationships hidden in large data sets can help you start to build models that best fit your needs. Once you deploy these models, you can leverage them to make business projections, operational predictions, new procedure recommendations, and much more.
Examples of how Artificial Intelligence helps data mining:
In addition, AI can help improve the accuracy of predictions made from data mining models by providing more accurate results based on larger amounts of training data.
As machine learning algorithms continue to evolve, they will become even better at helping us mine useful information from big data sets.
When used in combination with tools and techniques such as analytics and machine learning, data mining and AI can reveal new insights that benefit your business.
At Zyte, we provide data extraction services that facilitate price intelligence, brand monitoring, and business automation, to name a few.
These are just a few examples of how data mining and AI can be applied in various fields. Their influence will only continue to grow as big data becomes an essential commodity for businesses.
Just as the foundation is core to a building’s stability, the correct data mining process needs to be clearly outlined from an early stage.
This helps mitigate the risk of introducing biases that can negatively affect the entire production cycle.
Data mining and AI best practices:
In addition, there are certain key steps you can follow to ensure you have the right data to build and incorporate reliable AI that produces the results you need.
Variables are often correlated, and it is easy for biases to sneak in through one of the correlated fields. This can have a domino effect within the data mining model. To avoid this, it’s important to examine all correlations to ensure biases are truly eliminated.
Biased data produces biased models. To ensure the creation of a fair model, it requires a deep dive into the model results. You’ll have to select one that provides the best trade-off in terms of performance and interpretability.
Even though both the data and models have been assessed, biases that were previously overlooked can still be present, and become amplified during production. Monitoring how the model performs and flagging issues when performance deteriorates is essential to maintain optimal results.
Artificial intelligence (AI) can be a powerful tool to boost data mining. It can help identify patterns and relationships that would be difficult for humans to find. As well as help filter out irrelevant data, making it easier to focus on the most important information.
Data mining with AI can help organizations save time, resources, and money by reducing the need for manual labor-intensive tasks. Overall these are powerful tools that can greatly improve the efficiency and effectiveness of data mining
Let’s imagine the competition applies similar techniques to gather and analyze data, well naturally, the one with the most valuable data, will get ahead. Applying data mining and artificial intelligence allows you to better understand the complexities of data and then use that knowledge to make intelligent decisions.
It's no surprise that both are likely to become a key ingredient to provide businesses with a competitive edge in the near future.
Zyte is an industry leader in data extraction services, providing organizations, businesses, and data scientists with the data they need.
Our patented AI-powered automated extraction API allows you to access high-quality web data in a structured format.
Get in touch with us today to better understand how we can help power your data extraction operations.