Have any questions? +1(425)-270-5529 or info@datafactoryhub.com

Product Data Normalization

The e-Commerce/Product Data Normalization hold a number of step to be followed in order to acquire a well-designed database in order to ensure a reliable access and effective storage of data in relational database. To put simply, Product Data Normalization is a process of arranging data successfully in the database.

Product Data Normalization

The e-Commerce/Product Data Normalization hold a number of step to be followed in order to acquire a well-designed database in order to ensure a reliable access and effective storage of data in relational database. To put simply, Product Data Normalization is a process of arranging data successfully in the database. The steps that are usually involved in data normalization include eradication of repetitive data (for example, same data storage in several tables), and ensure the add-up of data dependencies. The Data Normalization can decrease the chances of occurring data inconsistency and data redundancy. The process can also minimize the quantity of space the database occupies and ensure that the data is logically kept in its right, specific place.

What the Service Offers?

There are many guidelines that specify in order to ensure the proper utilization of Product Data Normalization. Those are known and described as the normal forms and are numbered from 1 through 5 (1 as the minimum form of data normalization, and as the maximum form). In many applications, most of the time the normal forms for instance 1NF, 2NF, as well as 3NF are always combine and occasional with 4NF. 5NF is seldom observed.

First Normal Type

  • Remove the repeating sets in the individual tables
  • Create and use an individual table for each set regarding the associated data
  • Identify each group of the associated data that have a primary key

We don’t use several fields in a single table in keeping similar data. For example, in order to monitor one stock item that comes from 2 possible places, a stock record will include the field for the Merchant Code 1 as well as for Merchant Code 2.

Second Normal Type

  • Create different separate tables for the groups regarding that values that affects the multiple records
  • Link thee tables having foreign key.

The records shouldn’t rely on anything than the main key of the table (or a substance key, if really necessary). Such as, considering the customer’s address in accounting system might be a better option.

Third Normal Form

  • Eradicate the field that don’t rely on the key

Values in report that aren’t part of that record’s key will not fit in table. Commonly, the Product Data Normalization of many fields can be implemented to more than one record in table. Think about placing those fields within a separate table.

Advantage of the Service

When your business has several similarly looking products, the products navigation become a real difficult. In such case, a structure, product data normalization can help. This can help in normalization of the key field. For the entire items, taxonomy, defining the navigation attributes, and populating and normalizing values can also help. One of the main reasons of data de-normalization in e-Commerce are the fixed field files with the incorrect field types. Through Product Data Normalization everything will be normalized.

Why Hire Data Factory Hub?

For the huge e-Commerce retailers that store hundreds of similar-looking products, along with the marginal differences in the Product Data Normalization attribute – normalize, organized characteristics become important regarding the faceted navigation of product. In order to normalize your most important business fields, we have developed thorough procedure that can be personalized to your unique requirements. We have thoroughly constructed a significant taxonomy, build the ‘navigation attributes” summary, and effective stabilize and populate the vales regarding the entire items in category.

Discover how we can help you with our Product Data Normalization services in order to reduce the intricacy and cost of a successful data management.

GET QUOTE!