Top Three Reasons to Normalize Your Data

Data Normalization

Most businesses focus on data cleanliness. Having accurate data helps to segment customers and analyze the data in terms of marketing in order to engage the brand further. There are a number of reasons to normalize your Data. This facilitates the entire data cleaning process and keeps the customer data clean and organized. Without data normalization one may face several types of data errors.

Data normalization is the process of restructuring the data to ‘normal’ in terms of data integrity. It is a key part of data management that can improve data cleansing, lead routine, segmentation, and other data quality processes.

Data normalization makes the data look clean, organized, easy to read and navigate through, and uniform across the entire customer database. Normalization includes standardization of specific fields in the customer database which brings uniformity.

In addition, here are the top three reasons to normalize your data.

1. Identifying Duplication of Data

Data duplication is a crucial problem that companies face and getting rid of duplicates is an important part of data management. Data duplication can hinder the overall customer experience. Customers may receive the same data more than once which is not very appealing. It not only impacts the sales and marketing aspects of the business, but also increases data storage cost. Normalization makes it easier to locate and eliminate the duplicated data.

2. Improving Lead Scoring

Lead scoring is defined as the process of assigning a value to specific leads in the CRM so that you can identify and grasp potential opportunities. Effective lead scoring is dependent on high-quality data and effective segmentation. For example, a B2B company will assign value to its specific leads based on the job titles as a variable. Moreover, proper segmentation is not possible without normalization. This will impact the values and business might lose out on the best opportunities. Data normalization enhances data quality and improves the process of lead scoring.

3. Reduce Response Times through Normalization

In B2C companies, customers expect faster response time for their queries. Having to feed in thousands of names along with their responses can often be time-consuming. In order to achieve an organized data, companies must have a perfect internal administration team and must use the data normalization tools. Data normalization ensures reduced response times and well-structured data.

There are specific tools that can identify standardization issues and assist in the data normalization process. And also these tools analyze the existing customer data to generate an assessment report. So, based on the report, multiple categories are assigned to help companies normalize and standardize their customer data. This is an ongoing process, which means that the business can track and fix the standardization issues as they arise. In addition, the number of data normalization errors can be limited, resulting in a high-quality customer database.

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.