Data Normalization and Its Importance

All about Data Normalization and Its Importance

Advancement in technology and the changing work pattern within organizations has led to an increased importance for data management. Companies are building databases that are helping them collect, store, and analyze information. When it comes to bid data, another term that is widely used is data normalization. In this blog, we will understand more about data normalization and its importance.

Data normalization can be defined as the process in which data is organized in a way through which data users can easily analyze the data further. Data normalization has several applications. For instance, data normalization helps to get rid of any duplicate data. This reduces any possible redundancies which can adversely affect the data and enhances the capability of efficient data analysis.

Data normalization also helps to group the data together. The data that relates to each other is clubbed together into a single group making it easy to view the entire data at once. Sometimes the datasets have conflicting information. Data normalization helps to resolve all the data conflicts before any further analysis. By using the data normalization process, one can convert the entire data into a specific format which is simpler to read and analyze.

Now that we know about the applications of data normalization, it is time to understand the importance of data normalization.

A well-functioning database must go through the data normalization process. By why you ask? As discussed earlier, data normalization helps to get rid of all types of data defects and makes it easier for the users to analyze the data. Since the defects can occur at all times while the data is modified or updated, data normalization must be carried out regularly.

If a company does not use the data normalization process, then although the company would gather data, most of the data would be unorganized and unused. The data would take up most of the space and will not be of any benefit to the company. And since there is a lot of money invested in data collection and database designing, unused or misused data can lead to serious financial losses.

In addition to rectifying any data anomalies and faster analysis, data normalization offers several benefits to the organization:

  • Databases take up less space – Although technology advancement gives bigger data storage options, data normalization offers ways in which lesser disk space can be used for storage.

  • Enhance performance – Databases that are not unnecessarily loaded can lead to faster data analysis and increased performance.

  • Faster data upgradation and modification – Since the data anomalies are rectified, data can be easily updated and modified.

  • Data can be used to improve an organization’s performance – Company can look at the data to understand the company’s performances in different departments.

  • Can be used as a business intelligence tool – Data normalization can easily cross-examine the data coming from various sources.

Data normalization process works wonders for data scientists, business analysts, and people involved in database maintenance. It is considered to be one of the most necessary processes to be carried out by every company that deals with large data collection, storage and analysis.

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Advance Your Image Processing Knowledge

Read this to Advance Your Image Processing Knowledge

Today, automated image analysis has become an integral practice in multiple industrial and academic sectors. In order to analyze an image in detail, acquiring advanced image processing knowledge and skills can be highly useful.

Image processing as the name suggests is the process of editing images in a way to make them look more appealing and to identify the hidden details. Advanced image processing enhances the image in its best possible way. It is one of the most rapidly growing technologies used widely in medicine, forensic science, electrical engineering, and computer science domains.

Image processing can be carried out using two methods.

Analogue Image ProcessingDigital Image Processing
This method is used for hard copies such as printouts and photographs.This method is used for processing digital images with the help of computers.
Analysts use fundamental techniques of interpretation – Analog signalsAnalysts use three steps for interpretation – Pre-processing, Enhancement and Display.

Due to the increase in the usage of digital mediums like digital cameras, computers, mobiles, etc., digital image processing method is used more often as compared to analogue image processing method. In order to understand image processing in detail let us look at the following steps.

1. Acquisition – The first step is to acquire the image from the source. It also includes aspects like scaling and color conversion. Color alteration enhances the image.

2. Image enhancement – This is a subjective phase, which may or may not be applicable for every image. However, this step exposes the hidden features of the image.

3. Image restoration – This step makes the image more appealing and may or may not be applicable for every image.

4. Color image processing – It deals with full color and pseudocolor image processing

5. Wavelets and Multiresolution processing – In this step, image is presented in various degrees for better image clarity.

6. Image compression – This step deals with image size and resolution modification.

7. Morphological processing – This step deals with extracting image components that can describe the shape of the objects in the image.

8. Segmentation Procedure – Segmentation is one of the most difficult steps of image processing. This step partitions the image into its constituent parts.

9. Representation & Description – This step transforms raw data into processed data

10. Object detection and recognition – In this step, one can assign labels to the objects detected after the entire image processing is completed.

Artificial Intelligence (AI) has proved to have several applications in image processing. For example, helping doctors in interpreting X-rays and MRI images by developing computer aided diagnosis systems. This is a breakthrough in medical sciences, making diagnosis simpler and easier to manage.

Image is simply a two-dimensional signal; however, image processing focuses on the details and hidden aspects of the image, enhancing its usability.