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6 minutes read

The Importance of Knowledge Discovery

By Jonathan Tarud
The importance of knowledge discovery
By Jonathan Tarud
6 minutes read

Knowledge discovery and data mining are terms that are often confused with one another. This makes a fair amount of sense since data mining is an aspect or part of the knowledge discovery in databases (KDD) process. Knowledge discovery has been put to good use in a variety of disciplines, including marketing, risk management, business analysis, manufacturing, and telecommunications, to name a few. 

Knowledge discovery can be utilized in app development to help create more sophisticated Machine Learning tools. It can also help businesses quickly parse through large amounts of information and retrieve actionable insights that can benefit their operations in various ways. 

Knowledge discovery is an important topic to know about. This post will explain what knowledge discovery is and why it is important. 

What is Knowledge Discovery?

Knowledge discovery, which is also sometimes referred to as knowledge discovery in databases, is the procedure of extracting useful information from a larger database or data set. This is a popular method of collecting information from data sources and refining it for more targeted applications. 

In the past, knowledge discovery and data mining were done manually. This soon became an untenable arrangement due to the massive amounts of data businesses were collecting with the aid of computers. However, pure data is of little use if it can not be sorted, charted, graphed, etc., to find underlying models and hidden patterns. 

This necessitated the invention and adoption of intelligent software tools, such as Machine Learning and data visualization, into the knowledge discovery process to help businesses find hidden patterns and help make predictions based on prior knowledge and data. 

In today’s modern, analytics-driven world, you can never have too much data. This makes knowledge discovery an important part of our present and future, and computer science has made great strides in improving the data mining methods used in the knowledge discovery in databases process.

What are the Steps in the Knowledge Discovery in Databases Process 

Depending on who you talk to or what you read on the topic of knowledge discovery in databases, there are between five and seven steps involved in this process. To be clear, when there are fewer steps, it is not because steps are being left out. In a situation where you see fewer than seven steps to this process, steps have been combined, but nothing is missing. In this post, we will outline seven steps involved in the knowledge discovery in databases process for the sake of clarity. 

The seven steps of this process are as follows:

  1. Cleaning 
  2. Integration
  3. Selection
  4. Transformation 
  5. Mining 
  6. Evaluation 
  7. Presentation 


The first step in the process is ensuring that useful data is kept while irrelevant and unreliable data are discarded. This is called data cleaning. Often raw data from data sources has a fair amount of unreliable or irrelevant data. Therefore, before the process of extracting useful knowledge can begin, this data must be removed. 

Once you go through the cleaning process enough times, you can use predictive analytics to help you predict and automatically remove faulty or unreliable data from your data sources in the future. 


Once your data has been cleaned, the next step is combining your data sources. Computer scientists refer to this as data integration. You must clean all of your data before you combine data sources. This will make the next step in the process much simpler for your project management team. 


Now that you have your data from various sources in one database, the next step is choosing what data is relevant to your analysis task. For example, if you are performing knowledge discovery on sales data, you will want to select any information from your database related to sales. In this phase, you will also determine the variables that will be used for evaluation. 


Data transformation is an essential part of the knowledge discovery process that focuses on converting your pre-processed data into formats that can be easily mined. If your data is not uniform and organized into a single type, it will be very difficult to perform data mining and extract any useful knowledge or hidden patterns. Data transformation helps you sort, organize, and unify your data sources before data mining occurs.  


Data mining is the most well-known step in the knowledge discovery in databases process. This step focuses on sifting through the organized data to find hidden patterns and extract useful knowledge that can be used to further business interests and goals. 

Data mining uses Machine Learning tools to recognize patterns and draw conclusions, and data visualization tools are utilized to chart, graph, or otherwise visually display the insights gleaned from the data. 


Once data patterns have been found and reported, they can be evaluated. This step is the interpretation of the information provided by the data mining step of the process. In addition, analysts are tasked with finding any useful knowledge that may have come from the previous step in the process. 


After data analysts have had the opportunity to review and evaluate the patterns gathered from the data mining step, the data and insights are presented to the decision-makers of the business. This is the final step in the process. Finally, business decisions are made based on the conclusions drawn by analysts and business leaders from the final data evaluations, and then the process starts over. 

The Importance of Knowledge Discovery 

Staying current with customer behaviors, actions, and needs is extremely important in today’s hyper-connected digital world. Knowledge discovery helps businesses accurately forecast future market trends and conditions. This gives them a competitive edge over their competitors, and it helps them stay relevant in an ever-changing world. 

The knowledge discovery process gets better over time because it is an iterative process. This means that the knowledge acquired during the process is fed back through the process repeatedly, which enhances its quality each time through. This leads to better, more accurate predictions for businesses. 

Final Thoughts 

Knowledge discovery in databases is an important process that you should be putting to use in your industry. If you need help getting started or if you need guidance, reach out to an app development partner. A partner will help you through the entire process of implementing knowledge discovery in your intelligence processes from project kickoff to launch.

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