Suppose your ecommerce company ships a considerable amount of orders to customers. And at least 10% of them are returned because the address on the label is wrong or incomplete. Such returns imply a high cost for the company if the frequency of errors is constant. In cases like this, where accuracy of information is critical to customer and supplier management, it is imperative to have standardized data.
The standardization of the database is a process that requires, to a large extent, the unification of criteria for the collection and processing of information, as well as a great deal of attention and patience. In the following lines, we will talk about the phases of the procedure necessary to have correct, enriched and duplicate-free data. We will also mention the benefits that this practice generates.
What is the process to obtain normalized data?
In essence, data normalization is a process of organizing databases by applying a set of rules to clean up their structure. The purpose of the procedure is to remove unnecessary duplications and dependencies from the data tables and their related tables.
It is pertinent to remember that duplicate data are those generated by several users who add data to the database at the same time. But they also appear in databases whose design does not include duplicate detection. Unnecessary dependencies are relationships that should not exist between data. An example of this would be to find qualifiers dependent on third tables or temporary qualifiers in an organization's tax information record.
This may require creating new tables and establishing relationships between them following rules designed both for data protection and to obtain a much more flexible database after clearing them of redundancies and dependencies.
Obviously, duplicate data occupy more space in the memory disk and in the cloud storage. Apart from that, they can cause maintenance problems. When making changes to data in multiple locations, they must be made in exactly the same way in each location.
By way of illustration, achieving normalized data from the current customer portfolio would allow temporary indicators, for example, non-essential historical data, to be removed from the registry. It is also feasible to discard data that depend on third party tables.
In particular, accurately assigning the value of the data is very relevant as this will be the only way to ensure the elimination of duplicates. Consequently, changes will be made to the data and the data will be accurately cross-referenced.
Phases or levels to be fulfilled to obtain standardized data
In reality, there are several phases or levels of standardization applicable to databases. However, only three are the most common in organizations and are referred to as "normal forms". Each includes standards and criteria that establish the degree of vulnerability of the information to eventual errors and inconsistencies. Generally, data standardized to the highest level are considered to be those in which the three normal forms required for most applications apply. These levels are briefly described below.
First normal form
The standardization of the database is a process that requires, to a large extent, the unification of criteria for the collection and processing of information, as well as a great deal of attention and patience. In the following lines, we will talk about the phases of the procedure necessary to have correct, enriched and duplicate-free data. We will also mention the benefits that this practice generates.
What is the process to obtain normalized data?
In essence, data normalization is a process of organizing databases by applying a set of rules to clean up their structure. The purpose of the procedure is to remove unnecessary duplications and dependencies from the data tables and their related tables.
It is pertinent to remember that duplicate data are those generated by several users who add data to the database at the same time. But they also appear in databases whose design does not include duplicate detection. Unnecessary dependencies are relationships that should not exist between data. An example of this would be to find qualifiers dependent on third tables or temporary qualifiers in an organization's tax information record.
This may require creating new tables and establishing relationships between them following rules designed both for data protection and to obtain a much more flexible database after clearing them of redundancies and dependencies.
Obviously, duplicate data occupy more space in the memory disk and in the cloud storage. Apart from that, they can cause maintenance problems. When making changes to data in multiple locations, they must be made in exactly the same way in each location.
By way of illustration, achieving normalized data from the current customer portfolio would allow temporary indicators, for example, non-essential historical data, to be removed from the registry. It is also feasible to discard data that depend on third party tables.
In particular, accurately assigning the value of the data is very relevant as this will be the only way to ensure the elimination of duplicates. Consequently, changes will be made to the data and the data will be accurately cross-referenced.
Phases or levels to be fulfilled to obtain standardized data
In reality, there are several phases or levels of standardization applicable to databases. However, only three are the most common in organizations and are referred to as "normal forms". Each includes standards and criteria that establish the degree of vulnerability of the information to eventual errors and inconsistencies. Generally, data standardized to the highest level are considered to be those in which the three normal forms required for most applications apply. These levels are briefly described below.
First normal form
- To accomplish this first phase, you must do the following:
- First, remove from the individual tables the repeated data groups.
- For each group of related data, it is essential to create a separate table.
- Assign a primary key to each group of related data, without null attributes.
- Avoid using several fields in the same table to store analogous data.
It is also important not to incorporate data of identical meaning in the same table. You must also ensure that attributes are minimal and indivisible and that rows and columns are clearly independent. This will prevent a possible change of order from modifying their meaning.
Second normal form
At this point, you must consider the management of several records. In other words, if a set of data applies to several records, it is advisable to create independent tables and relate them to each other with a foreign key.
For example, let's take the address of a customer in an administrative system. This is fundamental in the Customers table, and it is equally essential in the Orders, Shipments, Invoicing and Accounts Receivable tables. Therefore, it is advisable to store the address only in the Customers table or in another table that you can call "Independent Addresses". Do not store it as a separate entry in each table where you need it.
Third normal form
In this last section, the data entered in the same record must be structured in such a way that all fields respond to the primary key. This level is favorable in data tables that need constant updating. In this way, you can dispense with breaking down the data into separate tables. In other words, the values of a record that do not depend on the primary key will not belong to the table.
At this level it is feasible to consider the available information as normalized data.
Benefits of normalized data
Indeed, clean, accurate and consistent normalized data generate benefits of great importance for organizations:
More dynamic data management. In principle, by discarding duplicates, the management and updating of data in the records will be more agile. This helps considerably to improve team productivity.
Better decision making. Indeed, analytical software solutions can only provide useful information based on standardized, complete and accurate data. Such information enables managers to make the best decisions in areas such as production and marketing.
Greater integration. Data standardization equally supports the integration of data with third-party sources and, at this point, strengthens the veracity and security of the available data.
Cost reduction. Today, many companies do not have a unified data collection format, which leads to errors of all kinds. Among them, spelling errors, indiscriminate and wrong use of abbreviations, duplicate data, etc. As we said at the beginning, this can result, for example, in orders being returned because the recipient's address is incorrect or incomplete. Standardized data avoids these costly mistakes in resources and time for companies.
Improved marketing. Data normalization and cleansing make strategies such as email marketing more effective. These strategies require accurate customer names and email addresses.
Increases sales. In turn, your company's sales team will accelerate the sales process by having accurate customer contact data.
Deyde's MyDataQ is your solution to obtain standardized data.
MyDataQ is a very complete software solution for automated data processing, focused on the normalization, deduplication and enrichment of databases. For this purpose, this tool acts on the following data:
MyDataQ is a solution created by Deyde DataCentric, a multinational technology company with more than 20 years of experience in the development of data quality solutions.
Second normal form
At this point, you must consider the management of several records. In other words, if a set of data applies to several records, it is advisable to create independent tables and relate them to each other with a foreign key.
For example, let's take the address of a customer in an administrative system. This is fundamental in the Customers table, and it is equally essential in the Orders, Shipments, Invoicing and Accounts Receivable tables. Therefore, it is advisable to store the address only in the Customers table or in another table that you can call "Independent Addresses". Do not store it as a separate entry in each table where you need it.
Third normal form
In this last section, the data entered in the same record must be structured in such a way that all fields respond to the primary key. This level is favorable in data tables that need constant updating. In this way, you can dispense with breaking down the data into separate tables. In other words, the values of a record that do not depend on the primary key will not belong to the table.
At this level it is feasible to consider the available information as normalized data.
Benefits of normalized data
Indeed, clean, accurate and consistent normalized data generate benefits of great importance for organizations:
More dynamic data management. In principle, by discarding duplicates, the management and updating of data in the records will be more agile. This helps considerably to improve team productivity.
Better decision making. Indeed, analytical software solutions can only provide useful information based on standardized, complete and accurate data. Such information enables managers to make the best decisions in areas such as production and marketing.
Greater integration. Data standardization equally supports the integration of data with third-party sources and, at this point, strengthens the veracity and security of the available data.
Cost reduction. Today, many companies do not have a unified data collection format, which leads to errors of all kinds. Among them, spelling errors, indiscriminate and wrong use of abbreviations, duplicate data, etc. As we said at the beginning, this can result, for example, in orders being returned because the recipient's address is incorrect or incomplete. Standardized data avoids these costly mistakes in resources and time for companies.
Improved marketing. Data normalization and cleansing make strategies such as email marketing more effective. These strategies require accurate customer names and email addresses.
Increases sales. In turn, your company's sales team will accelerate the sales process by having accurate customer contact data.
Deyde's MyDataQ is your solution to obtain standardized data.
MyDataQ is a very complete software solution for automated data processing, focused on the normalization, deduplication and enrichment of databases. For this purpose, this tool acts on the following data:
- Identification: name, surname, ID card, etc.
- Location: postal addresses, enrichment with geographic variables, XY, AGEB, sociodemographic and consumption typologies.
- Contact data: fixed and mobile (cellular) telephones, as well as e-mail addresses.
MyDataQ is a solution created by Deyde DataCentric, a multinational technology company with more than 20 years of experience in the development of data quality solutions.