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How AI-Augmented Data Mining Tools Automate
Data Flow Process?


Author : Jyothish

AIMLEAP Automation Works Startups | Digital | Innovation | Transformation

How AI-Augmented Data Mining Tools Automate Data Flow Process?

Author : Jyothish

AIMLEAP Automation Works Startups | Digital | Innovation | Transformation

Data mining is a process of analyzing data patterns and correlations to draw meaningful information. In other words, data mining is the science, art, and technology of collection, filtration, and analysis of meaningful patterns in big and complicated sets of data. Businesses these days rely on data for accelerating their growth.  

Today, 90% of corporate analytics and business experts believe that data and analytics are critical to their company’s digital transformation efforts. 

With data becoming more important than ever, the demand for mining tools is growing. Theoreticians, practitioners, and business analysts are always looking for new ways to increase the efficiency, cost-effectiveness, and accuracy of data mining. Apps and tools are mostly used in data mining. The applications of data mining are not just limited to business use cases but are also considerably used in other domains such as science, military operations, and education. But more often companies use it to get information about customer behavior to build sales and marketing strategies. 

It’s clear that data mining if used correctly can significantly benefit your business, but manually mining data is a tedious task. In order to accelerate the process of data mining, businesses are using smart, AI-powered data mining tools. With the advent of Artificial Intelligence, almost every process, system, or tool is capable of eliminating human intervention. AI is a branch of computer science concerned with the development of machines that can mimic human behavior. Every day, AI is changing the way we think, work and connect with one another. AI has had a lot of success in practically every field, whether it’s healthcare, education, or manufacturing.This holds true for data mining as well, automating data mining tools with AI augmentation can help the user achieve great results with minimal efforts. 

Accenture investigated the impact of artificial intelligence in 12 developed nations. According to the report, by 2035, the growth rates of the economy might quadruple. People will be able to make better use of their time, thanks to AI, which will enhance their productivity by 40%. 

The data mining process powered by Artificial Intelligence and Machine learning programs is more efficient and accurate. To optimally utilize the automated data mining tools, it is critical to know how AI can impact the data flow processes. Let’s look at how AI-augmented data mining tools automate data flow processes.

Flow Of Data Processes Through AI Automation

According to a BARC study, organizations that used big data experienced an 8% gain in profit and a 10% reduction in costs. Other advantages that have resulted from the shift to data monitoring were cited by the firms as well: 69% said big data helped them make better strategic decisions, 54% said it helped them govern operational operations, and 52% said it helped them understand their consumers better. 

This shows the importance of data in businesses today. For the collection of data with high efficiency and accuracy, AI-powered data mining tools must be used. AI in data mining is all about improving the flow of data processes. Here is how you improve data mining processes with the integration of AI.

Flow Of Data Processes Through Ai Automation

1. Computing The Statistics

The link between statistics and computer science is computational statistics, sometimes known as statistical computing. It refers to statistical procedures that are made possible by the use of computer methods. AI automation puts emphasis on computer-intensive statistical approaches, such as for scenarios with high sample sizes and non-homogeneous data sets. Using AI-powered data mining tools, computing statistics becomes efficient. Statistical analysis is automatically improved through AI automation in data mining.

2. Sampling

Data sampling is a statistical analysis technique that includes choosing, altering, and evaluating a sample of data points in order to find patterns and trends in a larger data set. Manual sampling causes various discrepancies in the retrieved samples. When using AI-augmented data mining tools, data sampling becomes easier and more accurate. Artificial Intelligence is known for its accuracy and efficiency. When both come together, the data sampling process automates and enables users to increase output, reduce risk, and improve sample accuracy.

3. Identify Data Types

As the amount of data in a normal company has risen dramatically, it’s become even more important to optimize data storage. The size of your data has an influence on query performance as well as storage space and expenses. It’s critical to carefully pick the data types in your database in order for it to perform effectively and be as efficient as possible. The data type you choose is an important aspect in deciding the size of your data. With the use AI-powered data mining tools, identification of the right data types becomes easy.

4. Eliminate Irrelevant Attributes

Data in the real world is frequently multi-dimensional and contains redundant or useless properties. Big data is challenging to collect and due to this, the accuracy suffers. Redundant and unnecessary data increase the size of the data beyond what is required, obscuring the key attributes. Duplicate and inaccurate data can ruin your analysis and strategy. With the integration of AI-augmented data mining tools in your business, irrelevant data is eliminated and only accurate data is sent to the database pipeline. Artificial intelligence helps in the collection of high-quality data from relevant sources improving the accuracy of your decisions.

5. Select The Right Algorithm

Data analysis is the process of turning raw data into clear, relevant, and actionable insights, whereas artificial intelligence (AI) is a data science area that employs complex algorithms to allow computers to learn on their own. You can automatically clean, analyze, explain, and display your data when you use AI-guided data mining tools in your data analysis. Traditional software relies on human input all of the time. AI software with Machine learning, on the other hand, requires no human input at all. AI-powered tools have the ability to learn from data by “feeding” machine learning algorithms labeled samples of text, often known as training data. They effectively employ human-tagged data to discover trends and learn to evaluate data on their own.

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6. Transforming The Data

AI-supported data mining tools can automatically analyze high-quality data from any relevant source and deliver valuable insights. They have the power to transform your database by delivering authentic and accurate data in a structured format. Data transformation is the process of changing data from one format to another, usually from a format of source system to a required format of destination system. Massive amounts of data are constantly produced by an ever-increasing number of programmes, apps, and devices. Data compatibility is constantly in jeopardy with so much heterogeneous data flowing in from a number of sources. That’s where the data transformation process comes in: it enables businesses and organizations to convert data from any source into a format that can be easily integrated, stored, analyzed, and mined for meaningful business insight. AI-powered data mining enables data delivery in the ready-to-analyze format in your data pipeline which reduces your efforts and time that you would have otherwise wasted on the classification of data.

7. Select Model And Assess Quality

Algorithms and machine learning produce models that we can use after harvesting digital data. These models are critical for anticipating customer behaviour. Models are constructed in order to feed data mining software and kick off the machine learning process. These models may then be used to make more precise forecasts. They improve decision-making confidence and can save expenses by deleting things that aren’t being used. Data mining initiatives can improve their algorithms by identifying known values of consumer behaviour. AI-assisted data mining tools can also assist you in detecting abnormalities within your models and patterns, preventing third parties from exploiting your system.

8. Generate Output

By using AI-powered data mining tools and data scraping technology, high quality data can be generated. Apiscrapy is one such company that offers data scraping capabilities. Data output refers to the process and manner through which data can be analyzed under various conditions and altered as needed by the researcher. Data output also includes data representation. The acquired data must be transformed into a presentable format so that additional conclusions and inferences can be taken from it. As a result, the researcher must investigate several data output strategies for this objective.

Some Of The Popular Use Cases Of Data Mining

  • Walmart used it for sales forecasting, to increase the sales of Kellogg’s strawberry pop tarts
  • T-mobile used data mining to bring down the unsubscribe rate, which they did, by 50%
  • It reduced the costs of Amazon’s supply and distribution and also improved delivery accuracy
  • In Netflix, an algorithm uses data (from data mining) to predict the taste and preference of the user and then provide desired results, which users see as recommendations.
  • Spotify uses it to show new music to its listeners, by tracking their activity and the type of music they often listen to.


Data-driven businesses are not only 23 times more likely to attract consumers, but they are also six times more likely to retain customers and 19 times more likely to be profitable, according to Mckinsey Global Institute.  

For a better understanding of the market and customers, it is a necessity for the companies to be data-driven. To adopt a business growth strategy, a company needs to be backed by high-quality data. Using AI-assisted data mining tools, you can collect accurate, high-quality data that supports precise market predictions. If you want to automate the process of data mining in your business, you can take the support of Outsource Bigdata. The company offers world-class AI-Augmentation solutions within your budget. Along with AI-augmentation, they offer feature-rich apps and software that make data mining a completely effortless, efficient and accurate process for the users. Automate Data Mining with Outsource Bigdata’s AI-Augmentation and take your business a step further toward digital transformation.

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Jyothish Chief Data Officer

Jyothish - Chief Data Officer

A visionary operations leader with over 14+ years of diverse industry experience in managing projects and teams across IT, automobile, aviation, and semiconductor product companies. Passionate about driving innovation and fostering collaborative teamwork and helping others achieve their goals.

Certified scuba diver, avid biker, and globe-trotter, he finds inspiration in exploring new horizons both in work and life. Through his impactful writing, he continues to inspire.

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