What is data normalisation? (Its benefits and types)

By Indeed Editorial Team

Published 24 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you perform data analysis in your professional life, you may benefit from learning how to structure a large data set. You can then easily source the information that you're looking for more quickly, before applying it to practical tasks. By following this guide, you can learn how to apply such techniques in a professional context. In this article, we define 'data normalisation', explain this process's benefits and detail the varying types of normalisation that you can use to categorise data sets.

What does data normalisation mean?

Data normalisation involves streamlining a database so that you can easily source and interpret set pieces of data. In this context, you can reorganise data contained within specific sections, removing duplicate data and structuring the remaining information in a more logical fashion. This process also creates common rules for ordering data, to ensure that you could clearly differentiate each data sub-set. For example, if you're logging clients' mobile phone numbers, you can use a standard format to list each contact, such as (+44) xxxx xxx xxx. In this situation, each 'x' represents a number.

Data analysts usually apply normalisation techniques to relational databases. These databases are the most common form of database, storing statistical data in rows and columns. They may resemble the spreadsheet format that financial professionals often use to store client or commercial data. A normalised database uses a column header to log a data set's titles while using rows to log related data. For example, if you're logging customers' monthly payments, you include a title in the column header, such as 'due payments'. You can use rows to list surnames and costs, using standard formats to list each data type.

Related: What is quantitative analysis? (With definitions and examples)

Benefits of the normalisation process

The below section explains the benefits of using the process:

Complete tasks more productively

One reason why you can benefit from normalising databases is that you can access relevant information in a shorter time period. If your manager asks for specific data about a client, product or service, you may search for the table, column or row title using a search bar feature. You may then extract and interpret the required statistics without reading through the entire database, allowing you to make quick and informed decisions as a result. This can also help colleagues to perform their own tasks in short order, boosting overall productivity.

For example, if you're a chief financial officer, you can use the normalisation process to promptly access data about your firm's current finances. In this context, you can use a database search feature to research specific topics, such as quarterly sales, projected profits or capital reserves. You could then use this data to influence immediate business plans, allowing you to make informed investment decisions more quickly.

Related: How to use data consolidation in Excel: a complete guide

Increase data storage space

Another reason why it's important to follow the normalisation process is that you can condense vast swathes of data onto single tables. This increases the overall storage space available on your organisation's computer system, allowing you to keep a larger number of tables and documents on one device. You could then switch between subjects without using memory drives to access relevant information. Increased storage space may also deliver productivity benefits by improving a device's performance. If the computer operates at a faster speed, you could analyse large data sets without experiencing lag problems.

For example, if you work as a freelance copywriter, you can use digital data sets to collate your earnings from different employers. In this context, you may benefit from classifying income data based on certain variables, such as the firm, payment date or amount received. By taking this approach, you may more easily search for financial data when filing tax returns, allowing you to submit the relevant information in an accurate manner. You could work on copywriting projects without experiencing slow computer speeds, ensuring that you could meet deadlines without unexpected delays.

Improve data security

You could also use the normalisation process to improve data security, as streamlining data systems reduces opportunities for hackers to access sensitive data. As you may more easily source-specific data via simplified data sets, you could quickly judge if you've unexpectedly lost sensitive information, before using contingency measures to limit further loss. You may also more easily monitor data on a routine basis, preventing hackers from covertly attaching malware to specific values to infect the entire system.

For example, if you're a data loss prevention expert, you may use the normalisation process to develop more robust intrusion prevention systems. By doing so, you could make it difficult for criminals to deploy advanced evasion techniques to exploit system weaknesses by limiting the layers of data for them to infiltrate. You may then find it easier to monitor data flows on a routine basis, identifying and resolving potential threats as they emerge. By making cyber security systems as simple as possible, you can protect clients' data against illegal access by criminals.

Related: What are cyber security roles? With salary expectations

Types of normalisation

Depending on the scale and scope of your data sets, you may use various techniques to normalise statistical information. Each technique includes its own rules on how to effectively categorise data, while more complex techniques also combine their own rules with those of simpler techniques. The following section details four ways that you could use the normalisation process to categorise and simplify data sets:

First Normal Form

First Normal Form involves categorising data to ensure that the columns are and row is free of repeat values. To meet these stipulations, you may only include one written or numerical value in each of a database's cells, with no value appearing more than once. For example, if you're a salesperson working on a large project, you can use First Normal Form to log key data about each client onto distinct tables, such as their name, business or units sold. You can then use this data to track your progress, presenting it to your manager during your final appraisal.

Second Normal Form

Second Normal Form follows the rules of First Normal Form whilst also specifying that each piece of data has a single primary key. A primary key is an identifying marker that links distinct yet related pieces of information, which share certain defining traits, such as their name or creator. These keys show that the related data have the same functional value, making it easier to find alternatives if specific data is unavailable.

For example, If you work as a university librarian, you may use Second Normal Form to arrange separate yet related scholarly texts. If students can read a textbook in hardback or digital form, you could use the primary key to show that both texts are essentially identical, despite their varying formats and accessibility. A student could then access the text even if the physical copy was unavailable.

Third Normal Form

Third Normal Form follows the rules of the previous normalisation techniques whilst also specifying that data categorisation relies on the primary key. If the primary key changes, you can move the associated data onto a new table.

For example, if you work in sales for a vehicle components contractor, you may record the details of different business contacts, such as their age, gender or employer. You can also use a primary key to draw distinctions between certain contacts, such as separating individuals based on employer. If a contact leaves one business to start a similar job for another client, you can alter their primary key to reflect this change. You can then log their details onto another table, making them easier to find in the future.

Boyce and Codd Normal Form

Boyce and Codd Normal Form develops the rules laid down by Third Normal Form, created to solve problems ignored by that technique. If projected onto a relational database, this method creates a superkey, which is a primary key that identifies a completely unique record within a table. If a change causes the record to lose its fully unique status, you can downgrade its primary key from superkey status. You may then move this data to a new table.

For example, if you work at a public library, you could log each non-fiction book onto a database according to certain traits, such as its genre or year of release. If one book contains unique values for every measure, you can consider its primary key to be a superkey. If your organisation later acquired a book released in the same year, you could revoke this status and move the data to a new table, shared with its relative.

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