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Technology in Data Mining

Make informed & smart decisions with data mining!

ByHabileLabs
December 1st . 5 min read

Information mining, also known as data mining, involves gathering and analysing relevant data to transform it according to preset standards. Data mining technology helps companies identify specific information about their existing and potential customers that is most relevant to them.

Companies across industries leverage information mining tools and techniques to use historical data effectively. Data in raw form does not provide much information.

Therefore, businesses must transform massive volumes of raw data into meaningful insights in order to stay competitive. They can use this information to make decisions about marketing, investments, and management practices.

Information mining involves the following steps–

  • Setting goals
  • Data accumulation
  • Loading data into data warehouses
  • Storing the data on the cloud or in-house servers
  • The data mining software applies algorithms to categorise the data based on the user’s results
  • Assessment of results
  • Presenting the data in an easy-to-share format

How does data mining help uncover relevant facts?

Information mining helps analysts uncover relevant facts, trends, exceptions, patterns, relationships, and anomalies hidden in troves of warehoused information using pattern recognition technologies and mathematical/ statistical procedures.

For instance, the patterns help identify the common attributes of customers buying a similar product or seeking the same service from a company.

Moreover, businesses can use the patterns to identify fraudulent transactions, foretell what content website visitors are more interested in viewing or determine which prospects are most likely to respond to a mailing list.

Additionally, businesses can use patterns to identify customers who might leave their company for their competitors.

Information mining can also assist in identifying sales trends, devising more effective marketing campaigns, and precisely predicting customers’ loyalty. Therefore, businesses worldwide leverage data mining to identify patterns and correlations in the data to make informed business decisions.

How can IT assist information mining?

Technology plays an essential role in information mining and provides opportunities for global improvements. Data being the lifeblood of businesses, helps companies make data-driven decisions.

Information technology can assist businesses in identifying vulnerabilities and improving operational efficiency. For instance, with the advent of the IoT and wearable devices, human beings and every internet-enabled device have become the source of data generation.

IoT-enabled devices can continuously generate data, allowing decision-makers to take decisive actions to boost performance and enhance customer/employee satisfaction.

Data Mining Tools & Technologies

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Information mining depends on analytical techniques based on mathematical algorithms. With abundant data and inexpensive storage and processing power, data scientists can apply these techniques to resolve general business problems.

Here are the most popular tools used for information mining-

- Artificial Neural Network

It can efficiently mine raw data and transform it into useful information. It analyses huge volumes of data and look for patterns, thus, enabling businesses to gain a deeper understanding of their customers, which can result in better marketing strategies, higher sales, and lower operating costs.

- Rule Induction

It is the process of determining an if-then rule from a set of data based on its statistical significance. The rules extracted from the data may reflect a full scientific model or local patterns.

- Decision trees

The decision tree algorithm produces a tree-like model by classifying information. The tree-like model depicts sets of decisions that determine how data should be classified.

- Genetic algorithms

These are optimisation techniques that combine genetics, mutation, and natural selection. Based on natural genetics, this advanced data classification sorts the best solution from several possible solutions.

- K-Nearest Neighbour (KNN)

In this technique, each record is categorised based on the documents in a historical database that are most similar to it.

- Artificial Intelligence and Machine Learning

In large-scale data processing, advanced machine learning techniques like deep learning offer exceptionally accurate predictions.

Therefore, they can be used for processing data in Artificial Intelligence deployments such as speech recognition, computer vision, or advanced text analytics using natural language processing (NLP).

- Data Visualisations

They provide users with access to data based on sensory perceptions. With modern data visualisation, data can be streamed in real-time and different colours can reveal different patterns and trends.

Since a dashboard is a powerful way of displaying data mining insights through data visualisations, businesses can create dashboards based on multiple metrics to visually recognise patterns in data rather than relying solely on numerical results.

Latest Trends in Data Mining

Some of the popular trends that have emerged through leveraging data mining are given below-

👉 Ubiquitous information mining

It is used for deriving data about a given individual from mobile devices. Ubiquitous data mining is gathering steam across industries for analysing computer-human interaction. However, it faces a set of challenges, including operational expenses and privacy issues.

👉 Multimedia information mining

It is one of the most effective methods for accurately capturing and extracting data from various new media sources, such as video, audio, hypertext, and so on, which is then transformed into a numerical value.

Multimedia data mining is frequently utilised for clustering, classifying data, and conducting similarity tests.

👉 Time Series & Sequence Information Mining

This is yet another type of information mining that retail companies increasingly use to assess customer purchasing patterns and behaviours.

Time series and sequence data mining are used to examine cyclical and seasonal patterns. It aids in analysing arbitrary events that occur outside the regular sequence of events.

👉 Spatial & Geographic Information Mining

It is one of the most popular types of data mining used for drawing information from multiple sources, such as astronomical, geographic, and environmental data, as well as photographs captured from space.

Advantages of Information Mining

Today’s information age is full of challenges, and information mining provides us with the means to resolve those challenges. The benefits of information mining are as follows:

  • Helps businesses gather accurate information, which enables them to make cost-effective adjustments in production and operations.
  • Helps businesses make informed business decisions using both new and legacy systems.
  • Allows data scientists to analyse dense volumes of data quickly. Identifies hidden patterns and fraud, builds risk models, and improves product safety.

Challenges of Data Mining

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Despite its advantages, information mining has its own set of drawbacks. The process presents several challenges pertaining to performance, data, methods, and techniques. Some of the major issues in data mining are as follows:

  • Difficult to Manage: Data mining requires managing large databases, which makes the process difficult.
  • Required Trained Professionals: Data analytics tools are often complex and challenging. Thus, data scientists must receive the proper training before using data mining tools effectively.
  • Must Use Correct Tools to Derive Useful Data: In terms of tools, they work with different types of data mining based on their algorithms. Therefore, data analysts must select the right tools for their work.
  • Lack of Diversity in Data May Generate Erroneous Results: The methods used for data mining aren’t always 100% accurate, so there is a constant risk of inaccuracies. Especially when the dataset lacks diversity, this obstacle becomes more prominent.
  • Privacy Concerns: Customer data can be sold to other organisations and businesses, causing privacy concerns.

While data mining is tremendously powerful, it encounters several implementation issues/challenges. Thus, identification and organisation of the issues/challenges are essential to the success of data mining.

The Way Forward

Despite its drawbacks, data mining appears to have promising long-term potential. Researchers and companies in many disciplines are turning to data mining as a vital technology since it has the potential to generate profitable results for businesses in the near future.

For instance, a micro-marketing campaign can explore new niches by utilising data mining. In addition, advertisers can target potential customers in a more precise manner.

Businesses can significantly benefit from having a single tool that can perform multiple data mining techniques. Companies can better reinforce the data governance and quality measures necessary for obtaining reliable data by utilising a single platform for various data mining techniques.

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