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How Data Mining and AI create business value

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. 

Understanding Data Mining

Data mining is the process of extracting and refining data into meaningful, structured data for analysisIt 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

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. 

How does it work? 

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.

Data Mining Stages

  • Data Cleaning removing invalid or missing data. 
  • Data Integration combining multiple data sources. 
  • Data Selection where relevant data is retrieved. 
  • Data Transformation where data is made into forms suitable for mining.
  • Pattern recognition and statistical techniques such as the use of mining algorithms, are applied to extract patterns and identify those that represent knowledge. 
  • Data Visualization where mined information is presented to users. 

Making sense of CRISP-DM

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: 

  1. Business Understanding 

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. 

  1. Data Understanding 

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. 

  1. Data Preparation

This stage includes data cleaning and transformation, as well as feature selection. Then select the appropriate modeling technique. 

  1. Modeling 

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. 

  1. Evaluation 

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. 

  1. 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. 

Bringing Data Mining and AI together 

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.” 

data mining

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. 

High-quality data mining with AI powered automated extraction

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.

Identifying patterns in large data sets

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: 

  1. Identify relevant data sets that may contain valuable information - this is often a time-consuming task for humans, but AI can quickly scan through large amounts of data to find relevant datasets.
  1. Extract useful information from those datasets - a task that would be very time-consuming for humans but which AI can do quickly and efficiently.
  1. Analyze the extracted information and identify any patterns or trends - this analysis would be extremely difficult for humans to do manually but could be easily accomplished by AI algorithms.

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.

Use cases for data mining and AI

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. 

  • In marketing: Data mining can be used in market research to improve market segmentation. By analyzing areas such as a customer’s gender, age, and preferences, as well as how they correlate, marketing organizations can predict behavior to tailor personalized campaigns. 
  • In education: By accessing student performance data in certain subjects. Data mining helps educators predict achievement levels and determine which students require additional help or intervention. 
  • In travel: Airlines can use data mining and AI to improve their services and offerings. They can access flight data at scale from trip searches and bookings with web, social media and call center interactions, to optimize their customer experience and create personalized travel experiences. 

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.  

What about data mining and AI best practices? 

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: 

  1. Define your goals upfront - before you start mining data or using AI algorithms, it's important to know what you're trying to achieve and what problems you want to solve.  
  1. Clean up your data first - data Mining is only as good as the quality of the data being fed into it. If your dataset is full of inaccuracies, missing values, or incorrect formatting, then all of those problems will carry over into your results. 
  1. Use multiple methods - consider it an exploratory process, which means that there isn't always a clear path forward. Trying different methods and approaches can help you find new insights that you might have otherwise missed.
  1. Be prepared for false positives - just because a pattern exists in your data doesn't mean that it's meaningful or accurate. 
  1. Understand how your results will be used - it's not enough just to find patterns in your data, you also need to understand how those patterns will be used by decision makers. This means defining which metrics will evaluate success.  

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.

Follow these steps when incorporating AI 

  • Data risk assessment 

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. 

  • Model risk management

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. 

  • Production monitoring

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. 

Conclusion 

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. 

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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.